Publications
1.
Song, R.; Zhou, L.; Lyu, L.; Festag, A.; Knoll, A.
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals Journal Article
In: IEEE Internet of Things Journal, 2023.
Abstract | BibTeX | Tags: federated learning, Intelligent Transport Systems
@article{Song:IEEE-IOT:2023_ResFed,
title = {ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals},
author = {R. Song and L. Zhou and L. Lyu and A. Festag and A. Knoll},
year = {2023},
date = {2023-10-02},
urldate = {2023-10-02},
journal = {IEEE Internet of Things Journal},
abstract = {Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight- or gradient-based federated learning) by over 1.4x on federated datasets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.},
keywords = {federated learning, Intelligent Transport Systems},
pubstate = {accepted},
tppubtype = {article}
}
Federated learning allows for cooperative training among distributed clients by sharing their locally learned model parameters, such as weights or gradients. However, as model size increases, the communication bandwidth required for deployment in wireless networks becomes a bottleneck. To address this, we propose a residual-based federated learning framework (ResFed) that transmits residuals instead of gradients or weights in networks. By predicting model updates at both clients and the server, residuals are calculated as the difference between updated and predicted models and contain more dense information than weights or gradients. We find that the residuals are less sensitive to an increasing compression ratio than other parameters, and hence use lossy compression techniques on residuals to improve communication efficiency for training in federated settings. With the same compression ratio, ResFed outperforms current methods (weight- or gradient-based federated learning) by over 1.4x on federated datasets, including MNIST, FashionMNIST, SVHN, CIFAR-10, CIFAR-100, FEMNIST, in client-to-server communication, and can also be applied to reduce communication costs for server-to-client communication.
2.
Hegde, A.; Song, R.; Festag, A.
Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach Journal Article
In: IEEE Access, vol. 11, pp. 87225-87244, 2023.
Abstract | Links | BibTeX | Tags: Cellular V2X, Cooperative ITS, Intelligent Transport Systems, machine learning, V2X communication
@article{Hegde:Access:2023_ResAlloc_ActorCritic,
title = {Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach},
author = {A. Hegde and R. Song and A. Festag},
url = {https://ieeexplore.ieee.org/document/10216959},
doi = {10.1109/ACCESS.2023.3305267},
year = {2023},
date = {2023-08-04},
urldate = {2023-08-15},
journal = {IEEE Access},
volume = {11},
pages = {87225-87244},
abstract = {The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15-20% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.},
keywords = {Cellular V2X, Cooperative ITS, Intelligent Transport Systems, machine learning, V2X communication},
pubstate = {published},
tppubtype = {article}
}
The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15-20% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.
3.
Song, R.; Lyu, L.; Jiang, W.; Festag, A.; Knoll, A.
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection Journal Article
In: arXiv, 2023, (arXiv preprint arXiv:2305.11654).
Abstract | Links | BibTeX | Tags: client selection, federated learning, Intelligent Transport Systems
@article{Song:ARXIV:2023_V2XBoosted,
title = {V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection},
author = {R. Song and L. Lyu and W. Jiang and A. Festag and A. Knoll},
url = {https://arxiv.org/pdf/2305.11654},
year = {2023},
date = {2023-06-21},
urldate = {2023-06-21},
journal = {arXiv},
abstract = {Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes: (i) fusing V2X messages, (ii) predicting future traffic topology, (iii) pre-clustering clients based on local data distribution similarity, and (iv) selecting clients with minimal latency for future model aggregation. Experiments show that our pipelineoutperforms baselines on various datasets, particularly in non-iid settings.},
note = {arXiv preprint arXiv:2305.11654},
keywords = {client selection, federated learning, Intelligent Transport Systems},
pubstate = {published},
tppubtype = {article}
}
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by training ML models in distributed systems, exchanging model parameters instead of raw data. However, the dynamic states of connected vehicles affect the network connection quality and influence the FL performance. To tackle this challenge, we propose a contextual client selection pipeline that uses Vehicle-to-Everything (V2X) messages to select clients based on the predicted communication latency. The pipeline includes: (i) fusing V2X messages, (ii) predicting future traffic topology, (iii) pre-clustering clients based on local data distribution similarity, and (iv) selecting clients with minimal latency for future model aggregation. Experiments show that our pipelineoutperforms baselines on various datasets, particularly in non-iid settings.
4.
Song, R.; Liu, D.; Chen, D. Z.; Festag, A.; Trinitis, C.; Schulz, M.; Knoll, A.
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments Proceedings Article
In: International Joint Conference on Neural Networks (IJCNN 2023), Queensland, Australia, 2023.
Abstract | Links | BibTeX | Tags: federated learning, Intelligent Transport Systems
@inproceedings{Song:IJCNN:2023,
title = {Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments},
author = {R. Song and D. Liu and D. Z. Chen and A. Festag and C. Trinitis and M. Schulz and A. Knoll},
url = {https://2023.ijcnn.org},
year = {2023},
date = {2023-06-20},
urldate = {2023-06-20},
booktitle = {International Joint Conference on Neural Networks (IJCNN 2023)},
address = {Queensland, Australia},
abstract = {In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches.},
keywords = {federated learning, Intelligent Transport Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches.
5.
Bauder, M.; Festag, A.; Kubjatko, T.; Schweiger, H. -G.
Data Accuracy in Vehicle-to-X Cooperative Awareness Messages: An Experimental Study for the First Commercial Deployment of C-ITS in Europe Journal Article
In: Social Science Research Network (SSRN), pp. 31, 2023.
Abstract | Links | BibTeX | Tags: Cooperative ITS, Intelligent Transport Systems, measurements, V2X communication
@article{Bauder:SSRN:2023_DataAccuracy_V2X,
title = {Data Accuracy in Vehicle-to-X Cooperative Awareness Messages: An Experimental Study for the First Commercial Deployment of C-ITS in Europe},
author = {M. Bauder and A. Festag and T. Kubjatko and H. -G. Schweiger},
url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4442746},
doi = {10.2139/ssrn.4442746},
year = {2023},
date = {2023-05-09},
urldate = {2023-05-09},
journal = {Social Science Research Network (SSRN)},
pages = {31},
abstract = {Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position has still an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.},
keywords = {Cooperative ITS, Intelligent Transport Systems, measurements, V2X communication},
pubstate = {published},
tppubtype = {article}
}
Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position has still an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.
6.
Song, R.; Xu, R.; Festag, A.; Ma, J.; Knoll, A
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems Journal Article
In: IEEE Transactions on Intelligent Vehicles, vol. 71, no. 4, pp. 1-12, 2023.
Abstract | Links | BibTeX | Tags: federated learning, Intelligent Transport Systems
@article{Song:TIV:2023-FedVEVT,
title = {FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems},
author = {R. Song and R. Xu and A. Festag and J. Ma and A Knoll},
url = {https://ieeexplore.ieee.org/document/10236488},
doi = {10.1109/TIV.2023.3310674},
year = {2023},
date = {2023-04-08},
urldate = {2023-04-04},
journal = {IEEE Transactions on Intelligent Vehicles},
volume = {71},
number = {4},
pages = {1-12},
abstract = {Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road traffic are often private, they are typically not shared. Federated learning offers a solution that enables clients to collaborate and train models without exchanging data but model parameters. In this paper, we introduce FedBEVT, a federated transformer learning approach for BEV perception. In order to address two common data heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying sensor numbers in perception systems, we propose two approaches - Federated Learning with Camera-Attentive Personalization (FedCaP) and Adaptive Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world settings, we create a dataset consisting of four typical federated use cases. Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving.},
keywords = {federated learning, Intelligent Transport Systems},
pubstate = {published},
tppubtype = {article}
}
Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road traffic are often private, they are typically not shared. Federated learning offers a solution that enables clients to collaborate and train models without exchanging data but model parameters. In this paper, we introduce FedBEVT, a federated transformer learning approach for BEV perception. In order to address two common data heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying sensor numbers in perception systems, we propose two approaches - Federated Learning with Camera-Attentive Personalization (FedCaP) and Adaptive Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world settings, we create a dataset consisting of four typical federated use cases. Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving.
7.
Song, R.; Xu, R.; Festag, A.; Ma, J.; Knoll, A
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems Journal Article
In: arXiv, 2023, (arXiv preprint arXiv:2304.01534).
Abstract | Links | BibTeX | Tags: federated learning, Intelligent Transport Systems
@article{Song:ARXIV:2023-FedVEVT,
title = {FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems},
author = {R. Song and R. Xu and A. Festag and J. Ma and A Knoll},
url = {https://arxiv.org/pdf/2304.01534},
year = {2023},
date = {2023-04-08},
urldate = {2023-04-04},
journal = {arXiv},
abstract = {Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road traffic are often private, they are typically not shared. Federated learning offers a solution that enables clients to collaborate and train models without exchanging data but model parameters. In this paper, we introduce FedBEVT, a federated transformer learning approach for BEV perception. In order to address two common data heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying sensor numbers in perception systems, we propose two approaches - Federated Learning with Camera-Attentive Personalization (FedCaP) and Adaptive Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world settings, we create a dataset consisting of four typical federated use cases. Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving.},
note = {arXiv preprint arXiv:2304.01534},
keywords = {federated learning, Intelligent Transport Systems},
pubstate = {published},
tppubtype = {article}
}
Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road traffic are often private, they are typically not shared. Federated learning offers a solution that enables clients to collaborate and train models without exchanging data but model parameters. In this paper, we introduce FedBEVT, a federated transformer learning approach for BEV perception. In order to address two common data heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying sensor numbers in perception systems, we propose two approaches - Federated Learning with Camera-Attentive Personalization (FedCaP) and Adaptive Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world settings, we create a dataset consisting of four typical federated use cases. Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving.
8.
Delooz, Q.; Festag, A.; Vinel, A.; Lobo, S.
Simulation-Based Performance Optimization of V2X Collective Perception by Adaptive Object Filtering Proceedings Article
In: IEEE Intelligent Vehicles Symposium (IV), Anchorage, Alaska, US, 2023.
Abstract | Links | BibTeX | Tags: Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication
@inproceedings{Delooz:IV:2023,
title = {Simulation-Based Performance Optimization of V2X Collective Perception by Adaptive Object Filtering},
author = {Q. Delooz and A. Festag and A. Vinel and S. Lobo},
url = {https://ieeexplore.ieee.org/document/10186788},
doi = {10.1109/IV55152.2023.10186788},
year = {2023},
date = {2023-04-04},
urldate = {2023-04-04},
booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
address = {Anchorage, Alaska, US},
abstract = {V2X Collective Perception is the principle of exchanging sensor data among V2X-capable stations, such as vehicles or roadside units, by exchanging lists of perceived objects in the 5.9 GHz frequency band for road safety and traffic efficiency. An object can be anything relevant to traffic safety, e.g., vehicles or pedestrians. The current standardization of Collective Perception in Europe considers filtering objects for transmission based on their locally perceived dynamics and freshness to preserve channel resources. However, two remaining problems of object filtering are: information redundancy and adapting object filtering to the available channel resources. In this paper, we combine redundancy mitigation and congestion control-aware filtering. We evaluate the performance of the resulting object filtering techniques by realizing realistic, large-scale simulations of a mid-size city in Germany. We assess the performance using a scoring metric. The results show better information redundancy control and adjustable channel usage for object filtering.},
keywords = {Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication},
pubstate = {published},
tppubtype = {inproceedings}
}
V2X Collective Perception is the principle of exchanging sensor data among V2X-capable stations, such as vehicles or roadside units, by exchanging lists of perceived objects in the 5.9 GHz frequency band for road safety and traffic efficiency. An object can be anything relevant to traffic safety, e.g., vehicles or pedestrians. The current standardization of Collective Perception in Europe considers filtering objects for transmission based on their locally perceived dynamics and freshness to preserve channel resources. However, two remaining problems of object filtering are: information redundancy and adapting object filtering to the available channel resources. In this paper, we combine redundancy mitigation and congestion control-aware filtering. We evaluate the performance of the resulting object filtering techniques by realizing realistic, large-scale simulations of a mid-size city in Germany. We assess the performance using a scoring metric. The results show better information redundancy control and adjustable channel usage for object filtering.
9.
Hegde, A.; Lobo, S.; Festag, A.
Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS Proceedings Article
In: 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Thessaloniki, Greece, 2023.
Abstract | Links | BibTeX | Tags: Intelligent Transport Systems, safety, V2X communication, vulnerable road users
@inproceedings{Hegde:WiMob:2022,
title = {Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS},
author = {A. Hegde and S. Lobo and A. Festag},
url = {http://www.wimob.org/wimob2022},
doi = {10.1109/WiMob55322.2022.9941707},
year = {2023},
date = {2023-02-21},
urldate = {2022-10-12},
booktitle = {2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)},
address = {Thessaloniki, Greece},
abstract = {Cooperative Intelligent Transport Systems (C-ITS) play a significant role in improving road traffic safety and efficiency. Primary use cases rely on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. In C-ITS, the safety of vulnerable road users (VRUs) such as pedestrians, motorists and other users with reduced mobility is being increasingly considered. A warning system, where VRUs actively send and receive messages instead of being passively monitored as road traffic objects, can play an important role in detecting risky situations and allowing the warning of vehicle drivers. However, in a dense urban traffic scenario with closely moving vehicles and pedestrians, the communication network can face severe resource constraints. Considering Cellular-V2X communication systems, the user equipments (UEs) may have to use the unmanaged mode of the sidelink interface. This paper analyzes the limitations of the existing radio resource allocation mechanisms and proposes adaptive strategies to ensure fairness in the distribution of radio resources between vehicles and VRUs. In addition, this work addresses the question about the situations in which VRU safety, being subject to a resource-constrained Cellular-V2X network, can be ensured.},
keywords = {Intelligent Transport Systems, safety, V2X communication, vulnerable road users},
pubstate = {published},
tppubtype = {inproceedings}
}
Cooperative Intelligent Transport Systems (C-ITS) play a significant role in improving road traffic safety and efficiency. Primary use cases rely on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. In C-ITS, the safety of vulnerable road users (VRUs) such as pedestrians, motorists and other users with reduced mobility is being increasingly considered. A warning system, where VRUs actively send and receive messages instead of being passively monitored as road traffic objects, can play an important role in detecting risky situations and allowing the warning of vehicle drivers. However, in a dense urban traffic scenario with closely moving vehicles and pedestrians, the communication network can face severe resource constraints. Considering Cellular-V2X communication systems, the user equipments (UEs) may have to use the unmanaged mode of the sidelink interface. This paper analyzes the limitations of the existing radio resource allocation mechanisms and proposes adaptive strategies to ensure fairness in the distribution of radio resources between vehicles and VRUs. In addition, this work addresses the question about the situations in which VRU safety, being subject to a resource-constrained Cellular-V2X network, can be ensured.
10.
Delooz, Q.; Vinel, A.; Festag, A.
Optimizing the Channel Resource Usage for Sensor Data Sharing with V2X Communications Journal Article
In: at – Automatisierungstechnik, vol. 71, no. 4, pp. 311-317, 2023.
Abstract | Links | BibTeX | Tags: Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication
@article{Delooz:ATJournal:2023,
title = {Optimizing the Channel Resource Usage for Sensor Data Sharing with V2X Communications},
author = {Q. Delooz and A. Vinel and A. Festag},
url = {2023-02-21},
doi = {10.1515/auto-2022-0162},
year = {2023},
date = {2023-01-01},
urldate = {2023-02-21},
journal = {at – Automatisierungstechnik},
volume = {71},
number = {4},
pages = {311-317},
abstract = {Sensor data sharing in V2X communication enables vehicles to exchange locally perceived sensor data with each other to increase their environmental awareness. It relies on the periodic exchange of selected, safety-relevant objects. Object selection is used to reduce channel resource usage. Additionally, vehicles use congestion control mechanisms to avoid overloading the channel. Currently, both object selection and congestion control mechanisms operate independently. We study a congestion-aware object filtering approach combining both and improving the performance of sensor data sharing. Die Übertragung von Sensordaten mit V2X-Kommunikation ermöglicht Fahrzeugen, lokale Umgebungsinformationen auszutauschen, um die Wahrnehmungsreichweite zu erhöhen. Der Sensordatenaustausch basiert auf der periodischen Übertragung sicherheitsrelevanter Objekte. Dabei wird die Anzahl der Objekte reduziert, um die Datenlast zu verringern. Zusätzlich steuern Mechanismen die Datenlast um eine Überlast zu vermeiden. Bisher arbeiten die Objektauswahl und die Datenüberlaststeuerung unabhängig voneinander. Wir untersuchen einen kombinierten Ansatz zur Objektfilterung, der die Performanz des Sensordatenaustauschs verbessert.},
keywords = {Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication},
pubstate = {published},
tppubtype = {article}
}
Sensor data sharing in V2X communication enables vehicles to exchange locally perceived sensor data with each other to increase their environmental awareness. It relies on the periodic exchange of selected, safety-relevant objects. Object selection is used to reduce channel resource usage. Additionally, vehicles use congestion control mechanisms to avoid overloading the channel. Currently, both object selection and congestion control mechanisms operate independently. We study a congestion-aware object filtering approach combining both and improving the performance of sensor data sharing. Die Übertragung von Sensordaten mit V2X-Kommunikation ermöglicht Fahrzeugen, lokale Umgebungsinformationen auszutauschen, um die Wahrnehmungsreichweite zu erhöhen. Der Sensordatenaustausch basiert auf der periodischen Übertragung sicherheitsrelevanter Objekte. Dabei wird die Anzahl der Objekte reduziert, um die Datenlast zu verringern. Zusätzlich steuern Mechanismen die Datenlast um eine Überlast zu vermeiden. Bisher arbeiten die Objektauswahl und die Datenüberlaststeuerung unabhängig voneinander. Wir untersuchen einen kombinierten Ansatz zur Objektfilterung, der die Performanz des Sensordatenaustauschs verbessert.
11.
Song, R.; Zhou, L.; Lyu, L.; Festag, A.; Knoll, A.
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals Journal Article
In: arXiv, 2022, (arXiv:2212.05602 [cs.LG]).
Abstract | Links | BibTeX | Tags: federated learning, Intelligent Transport Systems
@article{Song:ARXIV:2022,
title = {ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals},
author = {R. Song and L. Zhou and L. Lyu and A. Festag and A. Knoll},
url = {https://arxiv.org/abs/2212.05602},
year = {2022},
date = {2022-12-22},
urldate = {2022-12-11},
journal = {arXiv},
abstract = {Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. However, with increasing model size, deploying federated learning requires a large communication bandwidth, which limits its deployment in wireless networks. To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training. In particular, we integrate two pairs of shared predictors for the model prediction in both server-to-client and client-to-server communication. By employing a common prediction rule, both locally and globally updated models are always fully recoverable in clients and the server. We highlight that the residuals only indicate the quasi-update of a model in a single inter-round, and hence contain more dense information and have a lower entropy than the model, comparing to model weights and gradients. Based on this property, we further conduct lossy compression of the residuals by sparsification and quantization and encode them for efficient communication. The experimental evaluation shows that our ResFed needs remarkably less communication costs and achieves better accuracy by leveraging less sensitive residuals, compared to standard federated learning. For instance, to train a 4.08 MB CNN model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, our approach achieves a compression ratio over 700X in each communication round with minimum impact on the accuracy. To reach an accuracy of 70%, it saves around 99% of the total communication volume from 587.61 Mb to 6.79 Mb in up-streaming and to 4.61 Mb in down-streaming on average for all clients.},
note = {arXiv:2212.05602 [cs.LG]},
keywords = {federated learning, Intelligent Transport Systems},
pubstate = {published},
tppubtype = {article}
}
Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. However, with increasing model size, deploying federated learning requires a large communication bandwidth, which limits its deployment in wireless networks. To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training. In particular, we integrate two pairs of shared predictors for the model prediction in both server-to-client and client-to-server communication. By employing a common prediction rule, both locally and globally updated models are always fully recoverable in clients and the server. We highlight that the residuals only indicate the quasi-update of a model in a single inter-round, and hence contain more dense information and have a lower entropy than the model, comparing to model weights and gradients. Based on this property, we further conduct lossy compression of the residuals by sparsification and quantization and encode them for efficient communication. The experimental evaluation shows that our ResFed needs remarkably less communication costs and achieves better accuracy by leveraging less sensitive residuals, compared to standard federated learning. For instance, to train a 4.08 MB CNN model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, our approach achieves a compression ratio over 700X in each communication round with minimum impact on the accuracy. To reach an accuracy of 70%, it saves around 99% of the total communication volume from 587.61 Mb to 6.79 Mb in up-streaming and to 4.61 Mb in down-streaming on average for all clients.
12.
Hegde, A.; Delooz, Q.; Mariyaklla, C. L.; Festag, A.; Klingler, F.
Radio Resource Allocation for Collective Perception in 5G-NR Vehicle-to-X Communication Systems Proceedings Article
In: IEEE Wireless Communications and Networking Conference (WCNC 2023), Glasgow, UK, 2022.
Abstract | Links | BibTeX | Tags: Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication
@inproceedings{Hegde:WCNC:2023,
title = {Radio Resource Allocation for Collective Perception in 5G-NR Vehicle-to-X Communication Systems},
author = {A. Hegde and Q. Delooz and C. L. Mariyaklla and A. Festag and F. Klingler},
url = {https://wcnc2023.ieee-wcnc.org},
doi = {10.1109/WCNC55385.2023.10118606},
year = {2022},
date = {2022-12-11},
urldate = {2022-12-11},
booktitle = {IEEE Wireless Communications and Networking Conference (WCNC 2023)},
address = {Glasgow, UK},
abstract = {Sensor data sharing has an immense potential to enhance the perception capabilities of vehicles and to provide better situational awareness. It is being standardized as collective perception by the European Telecommunication Standards Institute (ETSI) as part of Cooperative Intelligent Transport Systems (C-ITS). For the transmission of collective perception messages via sidelink in Cellular-V2X, sensing-based semi-persistent scheduling (SB-SPS) in the unmanaged mode of 5G-NR V2X provides low-latency communication among road traffic participants that are located outside the cellular network coverage. The unpredictability of the collective perception messages in periodicity and size poses certain challenges on the SB-SPS, thereby creating poor utilization of radio resources and high risk of resource collisions. Existing system level simulations study the performance of collective perception from the application perspective without addressing the radio resource allocation at the access layer. This work investigates the challenges of the sidelink resource allocation mechanisms in 5G-NR V2X and assesses the impact of the mechanism on the performance of collective perception by simulation in a realistic urban traffic environment. A practical approach is adopted to formulate mathematical models that can characterize the radio resource utilization and resource collisions arising in such environments and yield the appropriate 5G-NR V2X parameters.},
keywords = {Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication},
pubstate = {published},
tppubtype = {inproceedings}
}
Sensor data sharing has an immense potential to enhance the perception capabilities of vehicles and to provide better situational awareness. It is being standardized as collective perception by the European Telecommunication Standards Institute (ETSI) as part of Cooperative Intelligent Transport Systems (C-ITS). For the transmission of collective perception messages via sidelink in Cellular-V2X, sensing-based semi-persistent scheduling (SB-SPS) in the unmanaged mode of 5G-NR V2X provides low-latency communication among road traffic participants that are located outside the cellular network coverage. The unpredictability of the collective perception messages in periodicity and size poses certain challenges on the SB-SPS, thereby creating poor utilization of radio resources and high risk of resource collisions. Existing system level simulations study the performance of collective perception from the application perspective without addressing the radio resource allocation at the access layer. This work investigates the challenges of the sidelink resource allocation mechanisms in 5G-NR V2X and assesses the impact of the mechanism on the performance of collective perception by simulation in a realistic urban traffic environment. A practical approach is adopted to formulate mathematical models that can characterize the radio resource utilization and resource collisions arising in such environments and yield the appropriate 5G-NR V2X parameters.
13.
Maksimovski, D.; Facchi, C.; Festag, A.
Cooperative Driving: Research on Generic Decentralized Maneuver Coordination for Connected and Automated Vehicles Book Section
In: Klein, Cornel; Jarke, Matthias; Helfert, Markus; Berns, Karsten; Gusikhin, Oleg (Ed.): Smart Cities, Green Technologies, and Intelligent Transport Systems, pp. 348–370, Springer International Publishing, Cham, 2022.
Abstract | Links | BibTeX | Tags: Cooperative ITS, Intelligent Transport Systems, maneuver coordination, V2X communication, vehicle automation
@incollection{Maksimovski:Springer:2022,
title = {Cooperative Driving: Research on Generic Decentralized Maneuver Coordination for Connected and Automated Vehicles},
author = {D. Maksimovski and C. Facchi and A. Festag},
editor = {Cornel Klein and Matthias Jarke and Markus Helfert and Karsten Berns and Oleg Gusikhin},
url = {https://link.springer.com/chapter/10.1007/978-3-031-17098-0_18},
doi = {10.1007/978-3-031-17098-0_18},
year = {2022},
date = {2022-10-11},
urldate = {2022-10-11},
booktitle = {Smart Cities, Green Technologies, and Intelligent Transport Systems},
pages = {348–370},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Maneuver coordination for Connected and Automated Vehicles (CAVs) can be enhanced by vehicle-to-everything (V2X) communication. In order to disseminate planned maneuver intentions or requests, Maneuver Coordination Messages (MCMs) are exchanged between the CAVs that enable them to negotiate and perform cooperative maneuvers. In this way, V2X communication can extend the perception range of the sensors, enhance the decision making and maneuver planning of the CAVs, as well as allow complex interactions between the vehicles. Various maneuver coordination schemes exist for specific traffic use cases. Recently, several maneuver coordination approaches have been proposed that target at generic decentralized solutions which can be applied for a wide range of use cases relying on direct vehicle-to-vehicle (V2V) communication. This paper presents such use cases and existing generic approaches for decentralized maneuver coordination. The approaches are systematically described, compared and classified considering explicit and implicit trajectory broadcast, space-time reservation, cost values, priority maneuvers and complex interactions among vehicles. Furthermore, this paper outlines open research gaps in the field and discusses future research directions.},
keywords = {Cooperative ITS, Intelligent Transport Systems, maneuver coordination, V2X communication, vehicle automation},
pubstate = {published},
tppubtype = {incollection}
}
Maneuver coordination for Connected and Automated Vehicles (CAVs) can be enhanced by vehicle-to-everything (V2X) communication. In order to disseminate planned maneuver intentions or requests, Maneuver Coordination Messages (MCMs) are exchanged between the CAVs that enable them to negotiate and perform cooperative maneuvers. In this way, V2X communication can extend the perception range of the sensors, enhance the decision making and maneuver planning of the CAVs, as well as allow complex interactions between the vehicles. Various maneuver coordination schemes exist for specific traffic use cases. Recently, several maneuver coordination approaches have been proposed that target at generic decentralized solutions which can be applied for a wide range of use cases relying on direct vehicle-to-vehicle (V2V) communication. This paper presents such use cases and existing generic approaches for decentralized maneuver coordination. The approaches are systematically described, compared and classified considering explicit and implicit trajectory broadcast, space-time reservation, cost values, priority maneuvers and complex interactions among vehicles. Furthermore, this paper outlines open research gaps in the field and discusses future research directions.
14.
Hegde, A.; Lobos, S.; Festag, A.
Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS Proceedings Article
In: WiMob, Thessaloniki, Greece, 2022.
Abstract | Links | BibTeX | Tags: Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication
@inproceedings{Hegde::2021,
title = {Cellular-V2X for Vulnerable Road User Protection in Cooperative ITS},
author = {A. Hegde and S. Lobos and A. Festag},
url = {http://www.wimob.org/wimob2022/programme.html},
year = {2022},
date = {2022-10-11},
urldate = {2022-10-11},
booktitle = {WiMob},
address = {Thessaloniki, Greece},
abstract = {Cooperative Intelligent Transport Systems (C-ITS) play a significant role in improving road traffic safety and efficiency. Primary use cases rely on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. In C-ITS, the safety of vulnerable road users (VRUs) such as pedestrians, motorists and other users with reduced mobility is being increasingly considered. A warning system, where VRUs actively send and receive messages instead of being passively monitored as road traffic objects, can play an important role in detecting risky situations and allowing the warning of vehicle drivers. However, in a dense urban traffic scenario with closely moving vehicles and pedestrians, the communication network can face severe resource constraints. Considering Cellular-V2X communication systems, the user equipments (UEs) may have to use the unmanaged mode of the sidelink interface. This paper analyzes the limitations of the existing radio resource allocation mechanisms and proposes adaptive strategies to ensure fairness in the distribution of radio resources between vehicles and VRUs. In addition, this work addresses the question about the situations in which VRU safety, being subject to a resource-constrained Cellular-V2X network, can be ensured.},
keywords = {Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication},
pubstate = {published},
tppubtype = {inproceedings}
}
Cooperative Intelligent Transport Systems (C-ITS) play a significant role in improving road traffic safety and efficiency. Primary use cases rely on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. In C-ITS, the safety of vulnerable road users (VRUs) such as pedestrians, motorists and other users with reduced mobility is being increasingly considered. A warning system, where VRUs actively send and receive messages instead of being passively monitored as road traffic objects, can play an important role in detecting risky situations and allowing the warning of vehicle drivers. However, in a dense urban traffic scenario with closely moving vehicles and pedestrians, the communication network can face severe resource constraints. Considering Cellular-V2X communication systems, the user equipments (UEs) may have to use the unmanaged mode of the sidelink interface. This paper analyzes the limitations of the existing radio resource allocation mechanisms and proposes adaptive strategies to ensure fairness in the distribution of radio resources between vehicles and VRUs. In addition, this work addresses the question about the situations in which VRU safety, being subject to a resource-constrained Cellular-V2X network, can be ensured.
15.
Lobo, S.; Festag, A.; Facchi, C.
Enhancing the Safety of Vulnerable Road Users: Messaging Protocols for V2X Communication Proceedings Article
In: IEEE 96th Vehicular Technology Conference (VTC2022-Fall), London UK and Beijing, China, 2022.
Abstract | Links | BibTeX | Tags: Intelligent Transport Systems, safety, V2X communication, vulnerable road users
@inproceedings{Lobo:VTCFall:2022,
title = {Enhancing the Safety of Vulnerable Road Users: Messaging Protocols for V2X Communication},
author = {S. Lobo and A. Festag and C. Facchi},
url = {https://events.vtsociety.org/vtc2022-fall/},
year = {2022},
date = {2022-09-05},
urldate = {2022-09-05},
booktitle = {IEEE 96th Vehicular Technology Conference (VTC2022-Fall)},
address = {London UK and Beijing, China},
abstract = {The protection of vulnerable road users (VRUs) by means of V2X communication is increasingly considered for the next generation of cooperative safety systems. Two messaging protocols are being standardized: (i) With collective perception for sensor data sharing: VRUs can be passively perceived by the local sensors in vehicles and roadside units, which disseminate this information in dedicated messages carrying lists with VRU and other objects. (ii) VRUs can actively transmit messages in order to make other road users in their vicinity aware of their presence. This paper carries out a performance evaluation of a scenario with the two main messaging protocols and also their combined deployment, and compares them with two baseline approaches. Simulation results for a representative roundabout scenario with manifold interaction among vehicles and pedestrians indicate that the combination of sensor data sharing and active VRU transmissions performs best: It provides the highest VRU detection rate and the shortest VRU detection latency. This approach keeps the channel busy ratio below a critical threshold, which prevents the data congestion control of the V2X communication system from its activation.},
keywords = {Intelligent Transport Systems, safety, V2X communication, vulnerable road users},
pubstate = {published},
tppubtype = {inproceedings}
}
The protection of vulnerable road users (VRUs) by means of V2X communication is increasingly considered for the next generation of cooperative safety systems. Two messaging protocols are being standardized: (i) With collective perception for sensor data sharing: VRUs can be passively perceived by the local sensors in vehicles and roadside units, which disseminate this information in dedicated messages carrying lists with VRU and other objects. (ii) VRUs can actively transmit messages in order to make other road users in their vicinity aware of their presence. This paper carries out a performance evaluation of a scenario with the two main messaging protocols and also their combined deployment, and compares them with two baseline approaches. Simulation results for a representative roundabout scenario with manifold interaction among vehicles and pedestrians indicate that the combination of sensor data sharing and active VRU transmissions performs best: It provides the highest VRU detection rate and the shortest VRU detection latency. This approach keeps the channel busy ratio below a critical threshold, which prevents the data congestion control of the V2X communication system from its activation.
16.
Song, R.; Zhou, L.; Lakshminarasimhan, V.; Festag, A.; Knoll, A.
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS Proceedings Article
In: IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 3502-3508, Macau, China, 2022.
Abstract | Links | BibTeX | Tags: federated learning, Intelligent Transport Systems
@inproceedings{Song:ITSC:2022,
title = {Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS},
author = {R. Song and L. Zhou and V. Lakshminarasimhan and A. Festag and A. Knoll},
doi = {10.1109/ITSC55140.2022.9922064},
year = {2022},
date = {2022-06-22},
urldate = {2022-06-22},
booktitle = {IEEE International Intelligent Transportation Systems Conference (ITSC)},
pages = {3502-3508},
address = {Macau, China},
abstract = {In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional pre-trained deep learning model. The framework exploits data from connected public traffic agents in vehicular networks without affecting user data privacy. By coordinating existing traffic infrastructure, including roadside units and road traffic clouds, the model parameters are efficiently disseminated by vehicular communications and hierarchically aggregated. Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i.e., aggregation in layers of roadside units and cloud. The experiment results indicate that our method can well balance the learning accuracy and stability according to the knowledge of heterogeneity in current communication networks. Compared to other baseline approaches, the evaluation on a Non-IID MNIST dataset shows that our framework is more general and capable especially in application scenarios with low communication quality. Even when 90% of the agents are timely disconnected, the pre-trained deep learning model can still be forced to converge stably, and its accuracy can be enhanced from 68% to over 90% after convergence.},
keywords = {federated learning, Intelligent Transport Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional pre-trained deep learning model. The framework exploits data from connected public traffic agents in vehicular networks without affecting user data privacy. By coordinating existing traffic infrastructure, including roadside units and road traffic clouds, the model parameters are efficiently disseminated by vehicular communications and hierarchically aggregated. Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i.e., aggregation in layers of roadside units and cloud. The experiment results indicate that our method can well balance the learning accuracy and stability according to the knowledge of heterogeneity in current communication networks. Compared to other baseline approaches, the evaluation on a Non-IID MNIST dataset shows that our framework is more general and capable especially in application scenarios with low communication quality. Even when 90% of the agents are timely disconnected, the pre-trained deep learning model can still be forced to converge stably, and its accuracy can be enhanced from 68% to over 90% after convergence.
17.
Delooz, Q.; Willecke, A.; Garlichs, K.; Hagau, A. -C.; Wolf, L.; Vinel, A.; Festag, A.
Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception Journal Article
In: IEEE Access, vol. 10, pp. 47076-47093, 2022.
Abstract | Links | BibTeX | Tags: Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication
@article{Delooz:IEEEAccess:2022,
title = {Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception},
author = {Q. Delooz and A. Willecke and K. Garlichs and A. -C. Hagau and L. Wolf and A. Vinel and A. Festag},
url = {2022-04-27},
doi = {10.1109/ACCESS.2022.3170029},
year = {2022},
date = {2022-04-09},
urldate = {2022-04-09},
journal = {IEEE Access},
volume = {10},
pages = {47076-47093},
abstract = {Sensor data sharing enables vehicles to exchange locally perceived sensor data among each other and with the roadside infrastructure to increase their environmental awareness. It is commonly regarded as a next-generation vehicular communication service beyond the exchange of highly aggregated messages in the first generation. The approach is being considered in the European standardization process, where it relies on the exchange of locally detected objects representing anything safety-relevant, such as other vehicles or pedestrians, in periodically broadcasted messages to vehicles in direct communication range. Objects filtering methods for inclusion in a message are necessary to avoid overloading a channel and provoking unnecessary data processing. Initial studies provided in a pre-standardization report about sensor data sharing elaborated a first set of rules to filter objects based on their characteristics, such as their dynamics or type. However, these rules still lack the consideration of information received by other stations to operate. Specifically, to address the problem of information redundancy, several rules have been proposed, but their performance has not been evaluated yet comprehensively. In the present work, the rules are further analyzed, assessed, and compared. Functional and operational requirements are investigated. A performance evaluation is realized by discrete-event simulations in a scenario for a representative city with realistic vehicle densities and mobility patterns. A score and other redundancy-level metrics are elaborated to ease the evaluation and comparison of the filtering rules. Finally, improvements and future works to the filtering methods are proposed.},
keywords = {Cooperative ITS, Intelligent Transport Systems, sensor data sharing, V2X communication},
pubstate = {published},
tppubtype = {article}
}
Sensor data sharing enables vehicles to exchange locally perceived sensor data among each other and with the roadside infrastructure to increase their environmental awareness. It is commonly regarded as a next-generation vehicular communication service beyond the exchange of highly aggregated messages in the first generation. The approach is being considered in the European standardization process, where it relies on the exchange of locally detected objects representing anything safety-relevant, such as other vehicles or pedestrians, in periodically broadcasted messages to vehicles in direct communication range. Objects filtering methods for inclusion in a message are necessary to avoid overloading a channel and provoking unnecessary data processing. Initial studies provided in a pre-standardization report about sensor data sharing elaborated a first set of rules to filter objects based on their characteristics, such as their dynamics or type. However, these rules still lack the consideration of information received by other stations to operate. Specifically, to address the problem of information redundancy, several rules have been proposed, but their performance has not been evaluated yet comprehensively. In the present work, the rules are further analyzed, assessed, and compared. Functional and operational requirements are investigated. A performance evaluation is realized by discrete-event simulations in a scenario for a representative city with realistic vehicle densities and mobility patterns. A score and other redundancy-level metrics are elaborated to ease the evaluation and comparison of the filtering rules. Finally, improvements and future works to the filtering methods are proposed.
18.
Song, R.; Hegde, A.; Senel, N.; Festag, A.
Edge-Aided Sensor Data Sharing in Vehicular Communication Networks Proceedings Article
In: IEEE Vehicular Technology Conference (VTC-Spring), pp. 8, Helsinki, Finland, 2022.
Abstract | Links | BibTeX | Tags: Cooperative ITS, cooperative perception, Intelligent Transport Systems, sensor data sharing, vehicular communication
@inproceedings{Song:VTC-Spring:2022,
title = {Edge-Aided Sensor Data Sharing in Vehicular Communication Networks},
author = {R. Song and A. Hegde and N. Senel and A. Festag},
url = {https://ieeexplore.ieee.org/document/9860849},
doi = {10.1109/VTC2022-Spring54318.2022.9860849},
year = {2022},
date = {2022-03-22},
urldate = {2022-03-22},
booktitle = {IEEE Vehicular Technology Conference (VTC-Spring)},
pages = {8},
address = {Helsinki, Finland},
abstract = {Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality and can result in road traffic accidents. Specifically, when the measurement error from the sensors – also referred as measurement noise – is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors. In this paper, we consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. The edge estimates the noise and the targets alternately in double dynamic sliding time windows and enhances the distributed cooperative environment sensing at each vehicle with low communication costs. We evaluate the proposed algorithm and data dissemination strategy in an application scenario by simulation and show that the perception accuracy is on average improved by around 80 percent with only 12 kbps uplink and 28 kbps downlink bandwidth.},
keywords = {Cooperative ITS, cooperative perception, Intelligent Transport Systems, sensor data sharing, vehicular communication},
pubstate = {published},
tppubtype = {inproceedings}
}
Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality and can result in road traffic accidents. Specifically, when the measurement error from the sensors – also referred as measurement noise – is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors. In this paper, we consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. The edge estimates the noise and the targets alternately in double dynamic sliding time windows and enhances the distributed cooperative environment sensing at each vehicle with low communication costs. We evaluate the proposed algorithm and data dissemination strategy in an application scenario by simulation and show that the perception accuracy is on average improved by around 80 percent with only 12 kbps uplink and 28 kbps downlink bandwidth.
19.
Festag, A.; Udupa, S.; Garcia, L.; Wellens, R.; Hecht, M.; Ulfig, P.
End-to-End Performance Measurements of Drone Communications in 5G Cellular Networks Proceedings Article
In: IEEE Vehicular Technology Conference (VTC-Fall), pp. 6, Virtual, 2021.
Abstract | Links | BibTeX | Tags: drones, measurements, mobile communication, wireless communication
@inproceedings{Festag:VTC-Fall:2021,
title = {End-to-End Performance Measurements of Drone Communications in 5G Cellular Networks},
author = {A. Festag and S. Udupa and L. Garcia and R. Wellens and M. Hecht and P. Ulfig},
url = {https://ieeexplore.ieee.org/document/9625429},
doi = {10.1109/VTC2021-Fall52928.2021.9625429},
year = {2021},
date = {2021-09-27},
urldate = {2021-12-10},
booktitle = {IEEE Vehicular Technology Conference (VTC-Fall)},
pages = {6},
address = {Virtual},
abstract = {Cellular communications with its ubiquitous cellular coverage enable various use cases for long-distance aerial inspection, surveillance and monitoring with drones. For these use cases, the exchange of command & control and sensor data, such as video, require a reliable and low-latency communication link. In this paper, we present results of a measurement campaign of drone communications in an experimental 5G network. The measurement scenarios cover (i)~the capacity and quality of the link between the drone and the ground station, (ii) the robustness of drone communications with the isolation provided by network slicing, and (iii)~the stability of the link between drone and ground station for long-distance flights. The results indicate that cellular communications in principle meet the reliability and latency of video and command & control data for long-distance flights even when the performance of cellular network is optimized for terrestrial devices. The application of network slicing in future 5G networks has a great potential to protect the drone critical command & control data from undesired wireless interferers.},
keywords = {drones, measurements, mobile communication, wireless communication},
pubstate = {published},
tppubtype = {inproceedings}
}
Cellular communications with its ubiquitous cellular coverage enable various use cases for long-distance aerial inspection, surveillance and monitoring with drones. For these use cases, the exchange of command & control and sensor data, such as video, require a reliable and low-latency communication link. In this paper, we present results of a measurement campaign of drone communications in an experimental 5G network. The measurement scenarios cover (i)~the capacity and quality of the link between the drone and the ground station, (ii) the robustness of drone communications with the isolation provided by network slicing, and (iii)~the stability of the link between drone and ground station for long-distance flights. The results indicate that cellular communications in principle meet the reliability and latency of video and command & control data for long-distance flights even when the performance of cellular network is optimized for terrestrial devices. The application of network slicing in future 5G networks has a great potential to protect the drone critical command & control data from undesired wireless interferers.
20.
Maksimovski, D.; Facchi, C.; Festag, A.
Priority Maneuver (PriMa) Coordination for Connected and Automated Vehicles Proceedings Article
In: IEEE International Intelligent Transportation Systems Conference (ITSC), Virtual, 2021.
Abstract | Links | BibTeX | Tags: Cooperative ITS, Intelligent Transport Systems, maneuver coordination, V2X communication, vehicle automation
@inproceedings{Maksimovski:ITSC:2021,
title = {Priority Maneuver (PriMa) Coordination for Connected and Automated Vehicles},
author = {D. Maksimovski and C. Facchi and A. Festag},
url = {https://ieeexplore.ieee.org/document/9564923},
doi = {10.1109/ITSC48978.2021.9564923},
year = {2021},
date = {2021-09-19},
urldate = {2021-10-25},
booktitle = {IEEE International Intelligent Transportation Systems Conference (ITSC)},
address = {Virtual},
abstract = {Connected and automated vehicles (CAVs) are expected to achieve safe and efficient driving by the broadcast of maneuver intentions using vehicle-to-vehicle (V2V) communications. The cooperative maneuver coordination has a high potential to help CAVs solving challenging traffic situations. We present a novel approach for decentralized and generic maneuver coordination. Relying on an explicit exchange of coordination messages, the proposed Priority Maneuver (PriMa) approach introduces three levels of maneuver requests. The priorities help vehicles that request and accept maneuvers to take decisions by differentiating among desired, necessary and critical maneuvers. Advantages of the prioritization lie in scenarios with many vehicles and with several subsequent maneuvers. Furthermore, the Maneuver Coordination Message (MCM) is extended defining several MCM subtypes. The paper presents the design of PriMa and analyzes three main scenarios by simulations: (i) maneuver coordination between two vehicles with and without prioritization, (ii) coordination with more than two vehicles, and (iii) cascading maneuver. PriMa utilizes different communication patterns for the presented scenarios to enable safe, fast and efficient coordination. The simulation results demonstrate the advantages of PriMa compared to existing coordination approaches in the decision-making process.},
keywords = {Cooperative ITS, Intelligent Transport Systems, maneuver coordination, V2X communication, vehicle automation},
pubstate = {published},
tppubtype = {inproceedings}
}
Connected and automated vehicles (CAVs) are expected to achieve safe and efficient driving by the broadcast of maneuver intentions using vehicle-to-vehicle (V2V) communications. The cooperative maneuver coordination has a high potential to help CAVs solving challenging traffic situations. We present a novel approach for decentralized and generic maneuver coordination. Relying on an explicit exchange of coordination messages, the proposed Priority Maneuver (PriMa) approach introduces three levels of maneuver requests. The priorities help vehicles that request and accept maneuvers to take decisions by differentiating among desired, necessary and critical maneuvers. Advantages of the prioritization lie in scenarios with many vehicles and with several subsequent maneuvers. Furthermore, the Maneuver Coordination Message (MCM) is extended defining several MCM subtypes. The paper presents the design of PriMa and analyzes three main scenarios by simulations: (i) maneuver coordination between two vehicles with and without prioritization, (ii) coordination with more than two vehicles, and (iii) cascading maneuver. PriMa utilizes different communication patterns for the presented scenarios to enable safe, fast and efficient coordination. The simulation results demonstrate the advantages of PriMa compared to existing coordination approaches in the decision-making process.