Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System

• URL: http://arxiv.org/abs/2404.09496v1
• Date: Mon, 15 Apr 2024 06:33:32 GMT
• Title: Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
• Authors: Genjia Liu, Yue Hu, Chenxin Xu, Weibo Mao, Junhao Ge, Zhengxiang Huang, Yifan Lu, Yinda Xu, Junkai Xia, Yafei Wang, Siheng Chen,
• Abstract summary: Vehicle-to-everything-aided autonomous driving (V2X-AD) has a huge potential to provide a safer driving solution. We present V2Xverse, a comprehensive simulation platform for collaborative autonomous driving. We introduce CoDriving, a novel end-to-end collaborative driving system.
• Score: 35.447617290190294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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