发表机构
University of Modena and Reggio Emilia; HiPeRT Srl（摩德纳与雷焦艾米利亚大学; HiPeRT有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶赛车中的目标检测与跟踪难题，提出一种多模态后期融合感知流水线，融合摄像头、激光雷达和雷达检测结果，并补偿延迟、嵌入动力学先验，实验验证其有效性与安全性。
AI 中文摘要
目标检测与跟踪是自动驾驶感知系统的基本组成部分。在能见度受限、传感器噪声和故障等不利条件下实现稳健性能仍然是一个开放挑战，尤其是在自动驾驶赛车中，车辆以极高速度行驶、经历强烈振动并在较小的安全裕度下交互。本文提出了一种用于自动驾驶赛车领域目标检测与跟踪的多模态后期融合感知流水线。所提出的系统通过后期融合方法和专门的多目标跟踪框架，利用所有车载传感器扩展了先前的工作。来自摄像头、激光雷达和雷达的独立检测结果被组合起来，以提供周围车辆及时且稳健的状态估计。跟踪方法显式补偿检测延迟，并在其模型中嵌入车辆动力学和赛道布局的先验知识。在涵盖多种关键场景（也代表城市驾驶中具有挑战性的边缘情况）的真实世界数据上进行的实验评估，证实了所提出流水线的有效性及其支持安全自适应规划决策的适用性。
英文摘要
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.
Comments8 pages, 6 figures, ITSC 2026, Invited Session
