Detection-Verification-Cascade-Based Low-Cost Precise Vehicle Positioning Exploiting Extended Digital Map

Abstract

This paper proposes a map-matching based precise positioning system for highways. The proposed system fuses multiple sensors such as a camera, conventional GPS, and vehicle motion sensors widely adapted in mass-produced vehicles. While conventional map-matching based systems consume a large amount of computation because of the landmark detection, the proposed system achieves both precise positioning and small computations by introducing a detection-verification-cascade strategy: the proposed system generates vehicle position candidates per lane by utilizing lane endpoints detectable with small computation, and then it selects the candidate on an ego-lane by verifying the existence of road signs within the region of interest generated by a map and each candidate. To reduce the computation further, the proposed system adapts an extended Kalman filter (EKF) as a positioning filter instead of a particle filter. As the noise distribution of the EKF is limited to a unimodal, the proposed system maintains multiple EKFs to track the candidates for each lane until the ego lane is identified. After ego-lane identification, the system leaves only one EKF to track the candidate on an ego-lane. The proposed system achieves 0.17m average positioning error in highway situations and it takes about 53ms to process the whole procedure from landmark detection to position estimation in low-end hardware equipped with a cortex-A9 CPU whose clock is 1.0 GHz.

Keywords

• Vehicle positioning
• map-matching
• sensor fusion
• camera
• extended Kalman filter
• embedded system