ArticleSensors (Basel, Switzerland)2019
A Multi-Feature Search Window Method for Road Boundary Detection Based on LIDAR Data.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Research on Lane Line Detection Algorithm Based on Instance Segmentation.Sensors (Basel, Switzerland) · 2023Article
- ScatterHough: Automatic Lane Detection from Noisy LiDAR Data.Sensors (Basel, Switzerland) · 2022Article
- Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles.Sensors (Basel, Switzerland) · 2019Article
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3 authors.
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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.
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