Road Boundaries Detection based on Modified Occupancy Grid Map Using Millimeter-wave Radar
- 30 Downloads
Road region detection is a hot spot research topic in autonomous driving field. It requires to give consideration to accuracy, efficiency as well as prime cost. In that, we choose millimeter-wave (MMW) Radar to fulfill road detection task, and put forward a novel method based on MMW which meets real-time requirement. In this paper, a dynamic and static obstacle distinction step is firstly conducted to estimate the dynamic obstacle interference on boundary detection. Then, we generate an occupancy grid map using modified Bayesian prediction to construct a 2D driving environment model based on static obstacles, while a clustering procedure is carried out to describe dynamic obstacles. Next, a Modified Random Sample Consensus (Modified RANSAC) algorithm is presented to estimate candidate road boundaries from static obstacle maps. Results of our experiments are presented and discussed at the end. Note that, all our experiments in this paper are run in real-time on an experimental UGV (unmanned ground vehicle) platform equipped with Continental ARS 408-21 radar.
KeywordsRoad detection Millimeter-wave radar Modified occupancy grid map Modified RANSAC Unmanned ground vehicle
This work was supported by the National Key Scientific Instrument and Equipment Development Projects of China (Grant Number: 61727802) and National Natural Science Foundation of China (Grant Number: 61703209, 61773215).
- 3.Xu F, Chen L, Lou J, et al. (2019) A real-time road detection method based on reorganized lidar data. PLoS ONE 14:4Google Scholar
- 9.Jones T O, Grimes D M (1975) Automotive station keeping and braking radars. A review. Microw J 18 (10):49–53Google Scholar
- 11.Park S, Kim E, Lee H, et al. (2008) Multiple data association and tracking using millimeter wave radar. In: Proceedings of international conference on control automation and systemsGoogle Scholar
- 14.Bertozzi M, Bombini L, Cerri P, et al. (2008) Obstacle detection and classification fusing radar and vision. In: Proceedings of IEEE intelligent vehicles symposiumGoogle Scholar
- 15.Wang X, Xu L, Sun H, et al. (2014) Bionic vision inspired on-road obstacle detection and tracking using radar and visual information. In: Proceedings of IEEE international conference on intelligent transportation systemsGoogle Scholar
- 17.Feng Z, Li M, Stolz M, et al. (2018) Lane detection with a high-resolution automotive radar by introducing a new type of road marking. IEEE Trans Intell Transp Syst PP:1–18Google Scholar
- 18.Bento L, Conde P, et al. (2018) Bonnifait set-membership position estimation with GNSS pseudorange error mitigation using lane-boundary measurements. IEEE Trans Intell Transp Syst, 1–10Google Scholar
- 19.Yang W, Lai-Liang C, Shu-Dan GU (2018) Extraction city road boundary method based on point cloud normal vector clustering. Acta Photonica SinicaGoogle Scholar