Towards Robust Velocity and Position Estimation of Opponents for Autonomous Racing Using Low-Power Radar

Abstract

This paper presents the design and development of an intelligent subsystem that includes a novel low-power radar sensor integrated into an autonomous racing perception pipeline to robustly estimate the position and velocity of dynamic obstacles. The proposed system, based on the Infineon BGT60TR13D radar, is evaluated in a real-world scenario with scaled race cars. The paper explores the benefits and limitations of using such a sensor subsystem and draws conclusions based on fieldcollected data. The results demonstrate a tracking error up to 0.21 ± 0.29 m in distance estimation and 0.39 ± 0.19 m/s in velocity estimation, despite the power consumption in the range of 10s of milliwatts. The presented system provides complementary information to other sensors such as LiDAR and camera, and can be used in a wide range of applications beyond autonomous racing.

Assessing the Robustness of LiDAR, Radar and Depth Cameras Against Ill-Reflecting Surfaces in Autonomous Vehicles: An Experimental Study

“…Our study utilizes a 1:10 scaled autonomous racing platform known as F1TENTH [16,17]. This platform facilitates safe, cost-effective, and rapid prototyping and has been instrumental in illustrating the principles and fundamentals of autonomous driving in various robotic tasks such as perception [5,18], planning [19] and control [20,21].…”