Lane Marking Detection Using Low-Channel Roadside LiDAR

Lane marking is the criterion line for the roadside unit to extract lane-level and high-resolution microlevel traffic data (HRMTD), which is essential information in the vehicle-to-infrastructure (V2I) cooperative. For lane marking detection in complex environments, inconsistent lane width and indistinctive laser intensity are two significant challenges for roadside LiDAR. To address these issues, we proposed an accuracy and robustness lane marking detection method. With the low-channel roadside LiDAR, we divided LiDAR scanned area into grids to extract vehicle points and filter out noise points. Then, the tilt angle of the lane markings was estimated using the grids. Finally, the lane number and lane marking were extracted by vehicle passing area identification according to the vehicle points and grids distribution. Experimental results show that the average distance error (ADE) of lane marking detection is less than 0.08 m, and the average detection time is 111 s in different environments.

Automated summary

Lane marking is crucial for extracting lane-level and high-resolution microlevel traffic data in the vehicle-to-infrastructure (V2I) cooperative. However, detecting lane markings in complex environments poses challenges due to inconsistent lane width and indistinctive laser intensity. To address these issues, an accurate and robust lane marking detection method is proposed, which utilizes low-channel roadside LiDAR to divide the scanned area into grids, estimate the tilt angle of the lane markings, and extract the lane number and lane marking. Experimental results show satisfactory performance in terms of average distance error (ADE) and detection time.