Gaussian Mixture Model-Based Data Association Incorporating a Deep Learning Network for Multivehicle Tracking and Detection in Autonomous Driving Systems

초록

In autonomous driving systems, 2D and 3D object detection and tracking demand accurate detection, robust affinity computation, and efficient data association in real-time environments. This article presents a deep learning-based multivehicle tracking and detection framework that fuses light detection and ranging (LiDAR) and camera data for simultaneous detection and tracking. The proposed system integrates a Gaussian mixture model-based data association and performs object detection and correlation using 2D images and 3D point cloud inputs. A key contribution of this work is a robust affinity computation module that effectively handles multiple occlusions and models object appearance and motion in 3D space. Additionally, the framework introduces a joint data association strategy that optimizes affinity scores, detection confidence, and start-end probabilities. Extensive experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute car tracking benchmark demonstrate that the proposed method achieves real-time performance and superior tracking accuracy, outperforming multiple state-of-the-art LiDAR-camera fusion methods, including the joint multiobject detection and tracking baseline by up to 1.69% in multiobject tracking precision and 0.10% in multiobject tracking accuracy, while also achieving more stable trajectories and fewer identity switches than boost correlation multiobject detection and tracking.

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