Low cost vision-based real-time lane recognition and lateral pose estimation

Shi, Tan; Agnes,; Mae, Johannes

doi:10.1109/cyberneticscom.2013.6865800

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…At present, the sensor applied to sense the road environment is mainly the visual sensor, which is small‐sized and can collect large amounts of information and achieve high‐precision close‐range detection at a low cost 6 . However, the lane marking detection algorithm based on the visual sensor is susceptible to the interference of the road environment, which will result in a failure in detection 7 . Therefore, in environment sensing module of the unmanned driving system, other sensors such as the LIght Detection And Ranging (LIDAR), the millimeter wave radar, the infrared sensing device, and so on, can be used collaboratively to achieve the objective of sensing the environment surrounding the road where the vehicle is driving.…”

Section: Introductionmentioning

confidence: 99%

Lane marking detection algorithm based on high‐precision map and multisensor fusion

Yao

Chen

Liu

et al. 2020

Concurrency and Computation

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Summary In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high‐precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter‐level high‐precision positioning combined with high‐precision map data to complete the detection of lane markings. In the process of generating high‐precision maps or in the uncovered areas of high‐precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust.

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Section: Introductionmentioning

confidence: 99%