EI-Drive: A Platform for Cooperative Perception with Realistic Communication Models

TL;DR

EI-Drive is a realistic autonomous driving simulation platform that models cooperative perception with transmission latency and errors, enhancing evaluation of safety and performance in complex traffic scenarios.

Contribution

The paper introduces EI-Drive, a novel simulation platform integrating advanced cooperative perception with realistic communication models built on CARLA.

Findings

Improved vehicle safety in complex traffic scenarios

Enhanced perception accuracy with realistic communication modeling

Demonstrated performance gains over existing platforms

Abstract

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/}.