Unified End-to-End V2X Cooperative Autonomous Driving
It addresses safety issues in autonomous driving for vehicles and infrastructure, but appears incremental as it builds on existing V2X and end-to-end approaches.
This paper tackles the problem of insufficient safety in autonomous driving by introducing the UniE2EV2X framework, an end-to-end system that integrates V2X cooperation to enhance perception and motion prediction, resulting in improved accident prediction accuracy.
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.