Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels
Source abstract
Abstract
The satellite navigation signal in the tunnel is weak, and it is difficult to achieve accurate lateral positioning in complex conditions such as low-speed congestion by relying solely on inertial navigation or line image recognition, which is one of the problems of automatic driving at present. In this paper, a lane-level location method based on multi-sensor fusion is proposed. Using the machine vision method, detecting lane lines with the monocular camera, and fitting the lane lines to determine the driving status of the vehicle based on the lane line information. The top view of the lane line is taken by the binocular camera, and the distance of the vehicle from the lane line and the width of the lane are calculated from the pictures taken by the binocular camera. Obtaining the heading angle information of the vehicle using the gyroscope in inertial navigation and the distance information of the vehicle using the odometer. When a car changes lanes or overtakes, the new lane the vehicle is in is calculated by calculating the difference in heading angle and combining it with the lane width and odometer information so as to complete the lateral positioning of the vehicle. The simulation results show that the algorithm has high lateral positioning accuracy. The positioning accuracy is less affected by the drift of inertial elements, and the error will not accumulate.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotics and Sensor-Based Localization · Video Surveillance and Tracking Methods · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Funding
the National Natural Science Foundation of China
References (26)
Show source-provided references
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