Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents

Yuxi Wei, Zi Wang, Yifan Lu, Chenxin Xu, Changxing Liu, Hao Zhao, Siheng Chen, Yanfeng Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15077-15087

Scene simulation in autonomous driving has gained significant attention because of its huge potential for generating customized data. However existing editable scene simulation approaches face limitations in terms of user interaction efficiency multi-camera photo-realistic rendering and external digital assets integration. To address these challenges this paper introduces ChatSim the first system that enables editable photo-realistic 3D driving scene simulations via natural language commands with external digital assets. To enable editing with high command flexibility ChatSim leverages a large language model (LLM) agent collaboration framework. To generate photo-realistic outcomes ChatSim employs a novel multi-camera neural radiance field method. Furthermore to unleash the potential of extensive high-quality digital assets ChatSim employs a novel multi-camera lighting estimation method to achieve scene-consistent assets' rendering. Our experiments on Waymo Open Dataset demonstrate that ChatSim can handle complex language commands and generate corresponding photo-realistic scene videos. Code can be accessed at: https://github.com/yifanlu0227/ChatSim.