Lane Marking Quality Assessment for Autonomous Driving Conference Paper uri icon


  • © 2018 IEEE. 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.

name of conference

  • 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

published proceedings

  • 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

author list (cited authors)

  • Li, B., Song, D., Li, H., Pike, A., & Carlson, P.

citation count

  • 5

complete list of authors

  • Li, Binbin||Song, Dezhen||Li, Haifeng||Pike, Adam||Carlson, Paul

publication date

  • October 2018