Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automated Vehicles

This paper presents a novel curvilinear-coordinate-based approach to improve object and situation assessment performance for highly automated vehicles under various curved road conditions. The approach integrates object information from radars and lane information from a camera with three steps: track-to-track fusion, curvilinear coordinate conversion, and lane assessment. The track-to-track fusion is achieved through a nearest neighbor filter that updates the target state estimation and covariance with the nearest neighbor measurement, and a cross-covariance method that merges the duplicate tracks using error covariance. In order to determine in which lane the fused tracks are located accurately and reliably, the curvilinear coordinate conversion process is performed. The curvilinear coordinates are generated in the form of a cubic Hermite spline lane model from the lane information of the camera. Based on the converted track information and the lane model in the curvilinear coordinates, the probability distribution of the threat levels in each lane is determined though a probabilistic lane association and threat assessment. The developed algorithm is verified and evaluated through experiments using a real-time embedded system. The results show that the proposed curvilinear-coordinate-based approach provides excellent performance of object and situation assessment, in respect of accuracy and computational efficiency, in real-time operation.