Towards Collaborative Autonomous Driving: Simulation Platform and
  End-to-End System
Genjia Liu Yue Hu Chenxin Xu Weibo Mao Junhao Ge Zhengxiang Huang Yifan Lu Yinda Xu Junkai Xia Yafei Wang Siheng Chen
Vehicle-to-everything-aided autonomous driving (V2X-AD) has a huge potential
to provide a safer driving solution. Despite extensive researches in
transportation and communication to support V2X-AD, the actual utilization of
these infrastructures and communication resources in enhancing driving
performances remains largely unexplored. This highlights the necessity of
collaborative autonomous driving: a machine learning approach that optimizes
the information sharing strategy to improve the driving performance of each
vehicle. This effort necessitates two key foundations: a platform capable of
generating data to facilitate the training and testing of V2X-AD, and a
comprehensive system that integrates full driving-related functionalities with
mechanisms for information sharing. From the platform perspective, we present
V2Xverse, a comprehensive simulation platform for collaborative autonomous
driving. This platform provides a complete pipeline for collaborative driving.
From the system perspective, we introduce CoDriving, a novel end-to-end
collaborative driving system that properly integrates V2X communication over
the entire autonomous pipeline, promoting driving with shared perceptual
information. The core idea is a novel driving-oriented communication strategy.
Leveraging this strategy, CoDriving improves driving performance while
optimizing communication efficiency. We make comprehensive benchmarks with
V2Xverse, analyzing both modular performance and closed-loop driving
performance. Experimental results show that CoDriving: i) significantly
improves the driving score by 62.49% and drastically reduces the pedestrian
collision rate by 53.50% compared to the SOTA end-to-end driving method, and
ii) achieves sustaining driving performance superiority over dynamic constraint
communication conditions.