Autonomous Formula Racecar: Overall System Design and Experimental Validation

TL;DR

This paper presents the design, implementation, and experimental validation of an autonomous system for a Formula Student racecar, integrating perception, localization, and control components for high-performance autonomous racing.

Contribution

It introduces a comprehensive system framework for autonomous racecars, including a novel cone detection method and high-precision localization combining GPS-INS and LIDAR data.

Findings

Successful autonomous racecar operation on a closed track

Effective cone detection and localization system demonstrated

System framework can be easily replicated for similar applications

Abstract

This paper develops and summarizes the work of building the autonomous integrated system including perception system and vehicle dynamic controller for a formula student autonomous racecar. We propose a system framework combining X-by-wired modification, perception & motion planning and vehicle dynamic control as a template of FSAC racecar which can be easily replicated. A LIDAR-vision cooperating method of detecting traffic cone which is used as track mark is proposed. Detection algorithm of the racecar also implements a precise and high rate localization method which combines the GPS-INS data and LIDAR odometry. Besides, a track map including the location and color information of the cones is built simultaneously. Finally, the system and vehicle performance on a closed loop track is tested. This paper also briefly introduces the Formula Student Autonomous Competition (FSAC).