Lane Marking Quality Assessment for Autonomous Driving

Measuring the quality of roads and ensuring they are ready for autonomous driving is important for future transportation systems. Here we focus on developing metrics and algorithms to assess lane marking (LM)qualities from an egocentric view of an inspection vehicle equipped with a global positioning system (GPS)receiver, a frontal-view camera, and a light detection and ranging (LIDAR)system. We propose three quality metrics for LMs: correctness, shape, and visibility. The correctness metric measures the divergence between the expected LMs based on prior map inputs and the actual sensor inputs. The shape metric evaluates smoothness in road curvature and width range. The visibility metric evaluates the contrast between LMs and background road surfaces. We propose a dual-modal algorithm to compute these metrics. We have implemented the algorithms and tested them under KITTI dataset. The results show that our metrics can successfully detect LM anomalies in all testing scenarios.