The KITTI Vision Benchmark Suite

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.

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@inproceedings{zhang2026motion,
title = {An Enhanced 3D Multi-Object Tracking
Framework for Autonomous Vehicles: Fusion,
Compensation, and Optimization},
author = {*},
booktitle = {Proceedings of the AAAI Conference
on Artificial Intelligence (AAAI)},
year = {2026},
note = {Submitted for review; under
consideration},
}