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.

翻译：车联网辅助自动驾驶（V2X-AD）具有提供更安全驾驶解决方案的巨大潜力。尽管在交通和通信领域已有大量研究支持V2X-AD，但如何实际利用这些基础设施和通信资源来提升驾驶性能仍基本未被探索。这凸显了协同自动驾驶的必要性：一种通过优化信息共享策略来提升每辆车驾驶性能的机器学习方法。这一努力需要两个关键基础：一个能够生成数据以促进V2X-AD训练和测试的平台，以及一个将完整驾驶相关功能与信息共享机制集成的综合系统。从平台角度，我们提出了V2Xverse，一个面向协同自动驾驶的综合仿真平台。该平台提供了完整的协同驾驶流水线。从系统角度，我们引入了CoDriving，一种新颖的端到端协同驾驶系统，它将V2X通信恰当地集成到整个自动驾驶流水线中，通过共享感知信息促进驾驶。其核心思想是一种创新的面向驾驶的通信策略。利用该策略，CoDriving在优化通信效率的同时提升了驾驶性能。我们使用V2Xverse进行了全面基准测试，分析了模块化性能与闭环驾驶性能。实验结果表明，CoDriving：i）与最先进的端到端驾驶方法相比，驾驶评分显著提升62.49%，行人碰撞率大幅降低53.50%；ii）在动态约束的通信条件下仍能保持持续的驾驶性能优势。