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.
Abstract (translated)
V2X合作，通过整合来自车辆和基础设施的传感器数据，被认为是推动自动驾驶技术发展的重要方法。当前的研究主要集中在提高感知精度和事故预测精度，往往忽视了端到端学习对事故预测精度系统性改进，导致对自动驾驶技术的安全问题关注不足。为了解决这个挑战，本文引入了UniE2EV2X框架，一种V2X集成式的端到端自动驾驶系统，将关键驾驶模块整合在一个统一的网络中。该框架采用了一种可变的注意力基于数据融合策略，有效促进了车辆和基础设施之间的合作。主要优点包括：1）显著增强了代理的感知和运动预测能力，从而提高了事故预测的准确性；2）确保了数据融合过程的高可靠性；3）与模块化方法相比具有卓越的端到端感知能力。此外，我们在具有挑战性的DeepAccident模拟数据集上实现了UniE2EV2X框架。
URL
https://arxiv.org/abs/2405.03971
