IMM-based Multiple Object Tracking using a State Prediction Neural Network

2026-09-10

AI Summary

本文提出一种基于状态预测神经网络的交互多模型跟踪方法(PR-IMM)，利用雷达多普勒测量提高物体运动表示准确性，减少定位误差和ID切换。

Abstract

Object tracking is essential for autonomous vehicles to avoid obstacles and plan routes. Radar maintains detection performance even in adverse weather and can measure relative velocity through the Doppler effect, making it well suited for object tracking. In this paper, we propose a data-driven state PRedictor-based Interacting Multiple Model tracking method (PR-IMM) that improves nonlinear object-motion representation while preserving the stability and interpretability of physics-based motion models. The proposed method employs a transformer-based PRediction model (PR) that incorporates radar Doppler measurements to predict object displacement. The PR model is integrated into the IMM as a mode alongside the CV, CA, and CT motion models, and their prior positions are dynamically combined according to the mode probabilities. Experimental results show that PR-IMM reduces position-estimation error by 57.3% over the IMM and by 16.5% over the PR, while reducing ID switches by 25.3% and improving IDF1 by 9.6% over the IMM.

Problem

• Multiple Object Tracking
• Interacting Multiple Model
• State Prediction
• Radar Doppler Measurements
• Nonlinear Object-Motion

Innovation

• state prediction neural network
• transformer-based prediction model
• radar Doppler measurements
• interacting multiple model (IMM)
• nonlinear object-motion representation