Robust 3D Multi-Object Tracking for Autonomous Driving with Adaptive LiDAR-Visual Fusion and Multilevel Data Association

Abstract

To increase the safety and reliability of autonomous driving systems in complex traffic environments, this paper proposes a novel 3D multiobject tracking (MOT) method that integrates center-plane adaptive multisensor fusion, motion compensation, and multilevel data association. Unlike traditional methods, our approach employs a center-plane adaptive fusion strategy to align LiDAR and visual data precisely, mitigating errors in the target width caused by pose variations, and improving tracking accuracy. To address vehicle motion-induced association errors in dynamic scenarios, we incorporate IMU and GPS data for high-frequency vehicle pose estimation and compensation, ensuring stable and robust target association. Additionally, a rotational geometric distance intersection-over-union (RGDIoU) cost function is introduced, combined with multilevel spatial indexing, to optimize the data association efficiency and accuracy. The experimental results on benchmark datasets, including KITTI and nuScenes, demonstrate that our method achieves state-of-the-art (SOTA) performance across multiple tracking metrics, including HOTA and sAMOTA, while maintaining real-time performance at 90 FPS. Specifically, our method improves sAMOTA tracking accuracy by 13% over the best existing methods and achieves a HOTA score of 50.24%, surpassing all compared methods.