Low-Cost Hybrid Multisensor Fusion Method and Implementation for Production Intelligent Vehicles

Abstract

Faced with cost limitation and 360-degree fusion and tracking requirement of production intelligent vehicles, this paper proposes a low-cost hybrid multi-sensor fusion method, which provides flexibility and scalability for multi-level information input and different sensor configurations. By combining radar, lane detection and ego vehicle information, we design a centralized fusion algorithm for 360-degree object tracking, which provides a low-computation multi-sensor association algorithm by a scheme of local sensor to global track association, and proves an optimal centralized estimator in the condition of multi-coordinate system Doppler information. To make the sensor fusion system compatible with multi-level information input and enhance the redundancy to improve security, a distributed fusion algorithm based on covariance intersection method is also designed as a part of fusion framework for the secondary fusion of high-level information. We validate the feasibility of our fusion method in an industrial-level controller with only 10% computing power consumption and build a ground truth system to quantitatively evaluate the method, which shows that our fusion method performs well on 360-degree object tracking and guarantees stable object IDs, and greatly improves the accuracy especially for sensor boundary regions. Furthermore, we also test and evaluate the performance of our fusion method in a variety of complex open road scenarios including expressway, curved road and lane fading scenarios.