LimSim Series: An Autonomous Driving Simulation Platform for Validation and Enhancement
Daocheng Fu, Naiting Zhong, Xu Han, Pinlong Cai, Licheng Wen, Song, Mao, Botian Shi, Yu Qiao

TL;DR
The LimSim Series is a versatile simulation platform for autonomous driving systems that balances accuracy, efficiency, and comprehensive evaluation, aiding rapid development and validation of ADS.
Contribution
It introduces a new simulation platform with multi-type data integration, human-like decision algorithms, and the AoI concept for optimized resource use, supporting flexible ADS validation.
Findings
Compatible with modular, end-to-end, and VLM-based systems
Facilitates rapid iteration and performance evaluation across scenarios
Provides multi-dimensional metrics for thorough system insights
Abstract
Closed-loop simulation environments play a crucial role in the validation and enhancement of autonomous driving systems (ADS). However, certain challenges warrant significant attention, including balancing simulation accuracy with duration, reconciling functionality with practicality, and establishing comprehensive evaluation mechanisms. This paper addresses these challenges by introducing the LimSim Series, a comprehensive simulation platform designed to support the rapid deployment and efficient iteration of ADS. The LimSim Series integrates multi-type information from road networks, employs human-like decision-making and planning algorithms for background vehicles, and introduces the concept of the Area of Interest (AoI) to optimize computational resources. The platform offers a variety of baseline algorithms and user-friendly interfaces, facilitating flexible validation of multiple…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Vehicle Dynamics and Control Systems · Vehicle emissions and performance
