Summary
Driving race cars without a human requires the car to know exactly where other cars are and where they're going, even at very fast speeds and in tricky conditions like bad weather or shaky movement. The authors designed a system that uses different types of sensors — cameras, LiDAR, and RADAR — combining their data in a smart way to track other vehicles more accurately and quickly. This combined approach helps the car predict movements while accounting for delays and knows the rules of the track to stay safe. Tests in real-world racing situations showed the system works well, even in tough scenarios that are similar to those in city driving. This technology can help race cars make better decisions while driving on their own.
autonomous racingobject detectionobject trackingmulti-modal sensorslate fusionLiDARRADARvehicle dynamicsperception systemsstate estimation
Authors
Davide Malvezzi, Michele Pestarino, Vittoria Cavicchioli, Valentina La Gamba, Silvia Severi, Fabio Bagni, Luca Bartoli, Massimiliano Bosi, Francesco Gatti, Micaela Verucchi, Ayoub Raji, Marko Bertogna
Abstract
Object detection and tracking are fundamental components of perception systems for autonomous driving. Achieving robust performance under adverse conditions such as limited visibility, sensor noise, and failures remains an open challenge, particularly in autonomous racing, where vehicles operate at very high speeds, experience strong vibrations, and interact under small safety margins. This paper presents a multi-modal late-fusion perception pipeline for object detection and tracking in the autonomous racing domain. The proposed system extends previous work by exploiting all onboard sensors through a late-fusion approach and a dedicated multi-object tracking framework. Independent detections from cameras, LiDARs, and RADARs are combined to provide timely and robust state estimates of surrounding vehicles. The tracking method explicitly compensates for detection delays and embeds in its model prior knowledge of vehicle dynamics and track layout. Experimental evaluation on real-world data across diverse critical scenarios, representative of challenging edge cases also in urban driving, confirms the effectiveness of the proposed pipeline and its suitability to support safe and adaptive planning decisions.