Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving

Volk, Georg; Delooz, Quentin; Schiegg, Florian A.; Bernuth, Alexander von; Festag, Andreas; Bringmann, Oliver

doi:10.1109/itsc48978.2021.9564783

Cited by 15 publications

(3 citation statements)

References 13 publications

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“…In fact, the simulator used in our study provides detailed modeling of the communication but does not have the capabilities for realistic sensor modeling and we assume ideal perception (no delay and no errors) in the object measurement. To better understand the impact of realistic sensor models on the RMR performance, enhanced simulation models and alternative simulation tools such as [5] would need to be used.…”

Section: ) Discussionmentioning

confidence: 99%

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Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception

et al. 2022

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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.

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Section: ) Discussionmentioning

confidence: 99%

“…Information redundancy in CPS occurs when different vehicles send CPM carrying similar information about the same objects. While this is beneficial for misbehavior detection or to improve the perception of receiving stations, it comes at the cost of both channel and processing resources, indirectly increasing the end-to-end delay [5].…”

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confidence: 99%

Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception

et al. 2022

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“…Of course, as data is constantly being sent to the service, the model of the environment has to keep up with the real-world motion of the vehicles. Therefore, some form of object tracking, e.g., using Kalman filters, has to be implemented [35].…”

Section: B Advanced Edge Service Design For Collective Perceptionmentioning

confidence: 99%

Towards realistic simulation of MEC-based Collective Perception: an initial edge service design for the Artery/Simu5G framework

Kovács¹,

Bokor²

2023

1st Workshop on Intelligent Infocommunication Networks, Systems and Services

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Day 2 V2X applications are the next step in vehicular communications, as they aim to extend the scope of shared information. In addition to status data, traffic participants are also aware of each other's sensory capabilities and the data deriving from those sensors. Collective Perception (CP), one of the flagship Day 2 services, enables vehicles and infrastructural elements, like roadside units or smart intersection controllers, to provide information about detected objects, significantly increasing the level of cooperative awareness V2X can grant for the communicating entities. However, with the escalation in the amount of data to be sent over the vehicular or mobile networks and the need for more processing power to enable real-time safety applications based on CP, some form of infrastructural aid is needed. The traditional central cloud-based approach might fall short of meeting the latency requirements. To keep latency within an acceptable range and solve the computational tasks locally, leveraging the capabilities of 5G Multi-access Edge Computing (MEC) could be a potential solution, thus ensuring a minimum impact on the core network. The architectural design and the modular approach of service implementation in the MEC specification by ETSI enable quick service deployment. This paper focuses on advanced Collective Perception-based V2X applications and the potential positive impact on their performance when operating with the support of edge computing. We also present the descriptionincluding the design considerationsof an initial, Artery/Simu5G-based edge service model working as a network-side intelligence extending the functionality of applications hosted on the vehicles.

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Robust Local and Cooperative Perception Under Varying Environmental Conditions

Gamerdinger,

Volk,

Teufel

et al. 2024

Cooperatively Interacting Vehicles

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Robust perception of the environment under a variety of ambient conditions is crucial for autonomous driving. Convolutional Neural Networks (CNNs) achieve high accuracy for vision-based object detection, but are strongly affected by adverse weather conditions such as rain, snow, and fog, as well as soiled sensors. We propose physically correct simulations of these conditions for vision-based systems, since publicly available data sets lack scenarios with different environmental conditions. In addition, we provide a data set of real images containing adverse weather for evaluation. By training CNNs with augmented data, we achieve a significant improvement in robustness for object detection. Furthermore, we present the advantages of cooperative perception to compensate for limited sensor ranges of local perception. A key aspect of autonomous driving is safety; therefore, a robustness evaluation of the perception system is necessary, which requires an appropriate safety metric. In contrast to existing approaches, our safety metric focuses on scene semantics and the relevance of surrounding objects. The performance of our approaches is evaluated using real-world data as well as augmented and virtual reality scenarios.

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Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving

Cited by 15 publications

References 13 publications

Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception

Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception

Towards realistic simulation of MEC-based Collective Perception: an initial edge service design for the Artery/Simu5G framework

Robust Local and Cooperative Perception Under Varying Environmental Conditions

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