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.

翻译：V2X协同通过整合车辆与基础设施的传感器数据，被视为推动自动驾驶技术发展的关键途径。当前研究主要聚焦于提升感知精度，往往忽视了通过端到端学习系统性地提升事故预测准确性，导致对自动驾驶安全问题的关注不足。针对这一挑战，本文提出了UniE2EV2X框架，这是一种基于V2X集成的端到端自动驾驶系统，将关键驾驶模块整合至统一网络中。该框架采用基于可变形注意力机制的数据融合策略，有效促进车路协同。其主要优势包括：1）显著增强智能体的感知与运动预测能力，从而提升事故预测精度；2）确保数据融合过程的高可靠性；3）相较于模块化方法，具备更优越的端到端感知性能。此外，我们还在面向V2X协同驾驶的复杂模拟数据集DeepAccident上实现了UniE2EV2X框架。