Unified End-to-End V2X Cooperative Autonomous Driving

• URL: http://arxiv.org/abs/2405.03971v1
• Date: Tue, 7 May 2024 03:01:40 GMT
• Title: Unified End-to-End V2X Cooperative Autonomous Driving
• Authors: Zhiwei Li, Bozhen Zhang, Lei Yang, Tianyu Shen, Nuo Xu, Ruosen Hao, Weiting Li, Tao Yan, Huaping Liu,
• Abstract summary: UniE2EV2X is a V2X-integrated end-to-end autonomous driving system that consolidates key driving modules within a unified network. The framework employs a deformable attention-based data fusion strategy, effectively facilitating cooperation between vehicles and infrastructure. We implement the UniE2EV2X framework on the challenging DeepAccident, a simulation dataset designed for V2X cooperative driving.
• Score: 21.631099800753795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: V2X cooperation, through the integration of sensor data from both vehicles and infrastructure, is considered a pivotal approach to advancing autonomous driving technology. Current research primarily focuses on enhancing perception accuracy, often overlooking the systematic improvement of accident prediction accuracy through end-to-end learning, leading to insufficient attention to the safety issues of autonomous driving. To address this challenge, this paper introduces the UniE2EV2X framework, a V2X-integrated end-to-end autonomous driving system that consolidates key driving modules within a unified network. The framework employs a deformable attention-based data fusion strategy, effectively facilitating cooperation between vehicles and infrastructure. The main advantages include: 1) significantly enhancing agents' perception and motion prediction capabilities, thereby improving the accuracy of accident predictions; 2) ensuring high reliability in the data fusion process; 3) superior end-to-end perception compared to modular approaches. Furthermore, We implement the UniE2EV2X framework on the challenging DeepAccident, a simulation dataset designed for V2X cooperative driving.

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