English(EN) A Multi-Modal Perception Pipeline for Object Detection and Tracking in Autonomous Racing

新的多模态感知管线提升自动驾驶赛车安全性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一种新的多模态感知管线，用于自动驾驶赛车中的目标检测和跟踪。该系统采用晚期融合方法，整合了来自摄像头、LiDAR 和 RADAR 的数据，以增强在低能见度和传感器噪声等挑战性条件下的鲁棒性。该管线还包含一个专用的多目标跟踪框架，该框架考虑了检测延迟，并利用了车辆动力学和赛道布局的先验知识。在真实世界数据上的评估表明，该系统在关键场景中的有效性，使其能够支持自动驾驶车辆的安全规划决策。 AI

影响增强了自动驾驶汽车的感知系统，有望提高高速赛车和城市驾驶的安全性与性能。

排序理由该集群包含一篇详细介绍新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · 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 · 2026-09-10 04:00

面向自动驾驶赛车中目标检测与跟踪的多模态感知管线

arXiv:2609.08338v1 Announce Type: cross 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 ch…

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报道来源 [1]

面向自动驾驶赛车中目标检测与跟踪的多模态感知管线

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