The KITTI Vision Benchmark Suite

This paper presents an enhanced 3D multiobject
tracking (MOT) framework designed for autonomous
vehicles operating in complex traffic
environments. The proposed framework includes
adaptive Kalman filtering (AKF) technology and
incorporating diagnostic and correction mechanisms
to improve tracking accuracy and system
robustness. The framework is designed to
dynamically adjust the filter parameters and adapt
to environmental changes, enhancing the stability
of state estimation. Furthermore, a 3D
hierarchical data association model is developed
that not only comprehensively considers the 3D
intersection-over-union (IoU), appearance, and
distance features of targets to improve data
association accuracy but also leverages a cascaded
strategy to increase real-time performance. By
utilizing an Internet of Things (IoT) framework to
integrate data from LiDAR and camera sensors, the
system compensates for the limitations of
individual sensors and enhances overall tracking
performance. [la][on]

\

\