The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive, an edge-AI based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: \url{https://ucd-dare.github.io/eidrive.github.io/}.

翻译：随着自动驾驶领域关注度的日益增长，对能够精确模拟真实交通场景中协同感知过程的仿真平台的需求也愈发迫切。现有的协同感知研究往往未充分考虑实际环境中的传输延迟与误差。为弥补这一不足，我们提出了EI-Drive——一个基于边缘人工智能的自动驾驶仿真平台，该平台将先进的协同感知技术与更真实的通信模型相结合。基于CARLA框架构建的EI-Drive，在考虑传输延迟与误差的同时，引入了新的协同感知模块，为评估协同感知算法提供了一个更为真实的平台。特别地，该平台支持车辆融合来自多源的数据，从而提升复杂环境下的态势感知能力与安全性。凭借其模块化设计，EI-Drive允许对各种协同驾驶场景中的传感、感知、规划与控制进行深入探究。基于EI-Drive的实验表明，该平台在车辆安全性与性能方面带来了显著提升，尤其在交通流复杂和网络条件多变的场景中效果更为突出。所有代码与文档均可在我们的GitHub页面访问：\url{https://ucd-dare.github.io/eidrive.github.io/}。