Unified End-to-End V2X Cooperative Autonomous Driving

TL;DR

This paper presents UniE2EV2X, an end-to-end V2X cooperative autonomous driving framework that improves perception, accident prediction accuracy, and data fusion reliability through a unified network and deformable attention-based data fusion.

Contribution

The paper introduces a novel unified end-to-end V2X framework with deformable attention data fusion, enhancing perception and accident prediction over modular approaches.

Findings

Improved perception and motion prediction accuracy.

Enhanced accident prediction capabilities.

Reliable data fusion in cooperative driving.

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.