Robust multi‐lane detection method based on semantic discrimination

Zhong, Yuzhong; Zhang, Jianwei; Li, Yingjiang; Geng, Tianyu; Wang, Maoning

doi:10.1049/iet-its.2019.0391

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…With the apparent colors different from the road, lane markings are often detected using vision-based methods. The traditional vision-based method of lane detection usually involves image processing, such as region of interest (ROI), edge detection, and Hough transform [20][21][22][23]. These methods use manual features, yielding low robustness and generalization.…”

Section: Introductionmentioning

confidence: 99%

Road geometry perception without accurate positioning and lane information

Yang

Luo³

et al. 2022

IET Intelligent Trans Sys

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Road geometry perception indicates road direction and drivable area for Lane Keeping System and Lane Departure Warning System in Advanced Driver Assistance Systems or routing and decision-making in Autonomous Driving. High-definition map with accurate positioning or lane information is generally required. However, these two types of information are not always available because of positioning failure or lane mark degradation. In this paper, a road geometry estimation method is proposed without requirement of accurate positioning and lane information. Due to the fact the lane guides the front vehicle, the potential correlation between the relative azimuth of front vehicle and the direction of road is explored. The stable and observable information of front vehicles are exploited, such as the license plate location. A radar-camera fusion system using image processing, and a nonlinear system is then built with Unscented Kalman Filter to estimate the road curvature. Simulation results with Carla demonstrate that the method enhances the road geometry perception by 77.1%, compared with the baseline method. The proposed method can provide reliable road geometry information for autonomous driving when accurate positioning and lane information are unavailable, so that it renders more robust and safer perception for future autonomous driving.

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Section: Introductionmentioning

confidence: 99%