Curvature Map-Based Magnetic Guidance for Automated Vehicles in an Urban Environment

Magnetic guidance is commonly used in real applications of automated vehicles for its reliability. However, due to the downward view of magnet detectors, the control of a vehicle based on magnetic guidance is difficult, particularly in an urban environment. This paper proposes the strategy of using a curvature map of magnets to carry out look-ahead control for magnetic guidance-based vehicles. First, a Gauss curve fitting-based lateral offset estimation method is proposed in the magnet localization to improve the robustness to sensors' noise. Then, the curvature of the road is generated based on a magnet-tracking algorithm, which records passed road segments by tracking every magnet detected by the magnetic sensor. Magnet-tracking results describe the relative position of the vehicle with respect to the reference road, whereas the curvature map contains forward road information. The fusion of these two sources of information gives a full description of the local road segment. Furthermore, to suppress noises, a simple local road model with a general form is used to fuse tracking results and curvature map information. Compared with magnetic guidance methods based on accurately positioned magnets, the proposed curvature map-based method boasts a simple map-generation process, in which the vehicle only needs to be driven manually along the reference trajectory once. Experiments on real application scenarios have verified the effectiveness of the proposed methods.