A Multi-Modal Perception Pipeline for Object Detection and Tracking in Autonomous Racing

2026-09-08

AI Summary

本文针对自动驾驶赛车中物体检测与跟踪问题，提出了一种多模态后融合感知管道，通过结合摄像头、激光雷达和雷达数据来提高在恶劣条件下的鲁棒性。

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.

Problem

• Object Detection
• Tracking
• Autonomous Racing
• Robust Performance
• Adverse Conditions

Innovation

• late-fusion
• multi-modal perception
• autonomous racing
• detection delays compensation
• vehicle dynamics