Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System through Distributed Database and Multimodal Perception: Demonstrated in Crossroads
Abstract: The autonomous driving industry is rapidly advancing, with Vehicle-to-Vehicle (V2V) communication systems highlighting as a key component of enhanced road safety and traffic efficiency. This paper introduces a novel Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System (VVCCS), designed to revolutionize macro-scope traffic planning and collision avoidance in autonomous driving. Implemented on Quanser Car (Qcar) hardware platform, our system integrates the distributed databases into individual autonomous vehicles and an optional central server. We also developed a comprehensive multi-modal perception system with multi-objective tracking and radar sensing. Through a demonstration within a physical crossroad environment, our system showcases its potential to be applied in congested and complex urban environments.
- Qcar. https://www.quanser.com/products/qcar/.
- Apollo, B. Robosense lidar systems for autonomous driving. https://apollo.baidu.com/community/apollo_d_kit. Accessed: 01.09.2024.
- Risk ranked recall: Collision safety metric for object detection systems in autonomous vehicles. In 2021 10th Mediterranean Conference on Embedded Computing (MECO) (2021), pp. 1–4.
- Hardware-in-the-loop autonomous driving simulation without real-time constraints. IEEE Transactions on Intelligent Vehicles 4, 3 (2019), 375–384.
- Lidar cluster first and camera inference later: A new perspective towards autonomous driving. ArXiv abs/2111.09799 (2021).
- Exploring the limitations of behavior cloning for autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision (2019), pp. 9329–9338.
- etcd company. etcd. https://etcd.io/, 2023.
- Wireless distributed consensus in vehicle to vehicle networks for autonomous driving. IEEE Transactions on Vehicular Technology 72, 6 (2023), 8061–8073.
- Target fusion detection of lidar and camera based on the improved yolo algorithm. Mathematics 6, 10 (2018).
- Pedestrian detection for autonomous driving within cooperative communication system. In 2019 IEEE Wireless Communications and Networking Conference (WCNC) (2019), pp. 1–6.
- Jie Wang, OpenAI DALL-E. High-tech v2v communication scene. https://openai.com/dall-e, 2024.
- Cooperative driving at blind crossings using intervehicle communication. IEEE Transactions on Vehicular Technology 55, 6 (2006), 1712–1724.
- Advances in APPFL: A comprehensive and extensible federated learning framework. CoRR abs/2409.11585 (2024).
- A strategy to reduce older driver injuries at intersections using more accommodating roundabout design practices. Accident Analysis & Prevention 39, 3 (2007), 427–432.
- Exploiting vehicle-to-vehicle communications for enhanced situational awareness. In 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) (2019), pp. 88–92.
- Miller, V. The impact of stopping on fuel consumption. http://large.stanford.edu/courses/2011/ph240/miller1/, 2011.
- A resilient and distributed near real-time traffic forecasting application for fog computing environments. Future Generation Computer Systems 87 (2018), 198–212.
- Dynamic cell association for non-orthogonal multiple-access v2s networks. IEEE Journal on Selected Areas in Communications 35, 10 (2017), 2342–2356.
- Robosense. Robosense lidar systems for autonomous driving. https://www.robosense.ai/en/scheme. Accessed: 01.09.2024.
- Factors contributing to the severity of intersection crashes. Journal of Advanced Transportation 41 (06 2007), 245 – 265.
- Ultralytics. YOLO algorithm documentation, Accessed 2024. Online Documentation.
- V2v-based method for the detection of road traffic congestion. IET Intelligent Transport Systems 13, 5 (2019), 880–885.
- Lidar and camera detection fusion in a real-time industrial multi-sensor collision avoidance system. Electronics 7, 6 (2018).
- A tutorial survey on vehicle-to-vehicle communications. Telecommunication Systems 73 (March 2020), 469–489.
- Collision avoidance predictive motion planning based on integrated perception and v2v communication. IEEE Transactions on Intelligent Transportation Systems 23, 7 (2022), 9640–9653.
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