Abstract
In order to improve the accuracy of structured road boundary detection and solve the problem of the poor robustness of single feature boundary extraction, this paper proposes a multi-feature road boundary detection algorithm based on HDL-32E LIDAR. According to the road environment and sensor information, the former scenic cloud data is extracted, and the primary and secondary search windows are set according to the road geometric features and the point cloud spatial distribution features. In the search process, we propose the concept of the largest and smallest cluster points set and a two-way search method. Finally, the quadratic curve model is used to fit the road boundary. In the actual road test in the campus road, the accuracy of the linear boundary detection is 97.54%, the accuracy of the curve boundary detection is 92.56%, and the average detection period is 41.8 ms. In addition, the algorithm is still robust in a typical complex road environment.
Highlights
At present, the automatic driving is mainly realized by Global Position System (GPS) positioning tracing and on-board sensor sensing
In order to further improve the accuracy of the road boundary fitting, this paper uses the difference point cloud spatial distribution to filter based on the candidate window obtained of point cloud spatial distribution to noise filter more noise accurately more accurately based on the candidate window by the first search
In order to evaluate the effect of our algorithm on road boundary detection, we compare it with
Summary
The automatic driving is mainly realized by Global Position System (GPS) positioning tracing and on-board sensor sensing. GPS positioning is usually affected by factors such as weather and building occlusion, causing signal drift or loss At this time, it is necessary to divide the road area and the non-road area by road boundary detection to determine the safe driving area of the intelligent vehicle [6], and at the same time reduce the sensor search range to improve the sensing accuracy and real-time performance [7,8]. The above research mainly focuses on the extraction of boundary points of fixed regions based on single features in road geometry or spatial distribution They have a certain improvement in detection accuracy and real-time performance. The overall detection accuracy is above 95%, and it fully meets the requirements of the intelligent vehicle algorithm in real-time
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