Road Boundaries Detection based on Modified Occupancy Grid Map Using Millimeter-wave Radar

被引：0
作者：
Fenglei Xu
Huan Wang
Bingwen Hu
Mingwu Ren
机构：
[1] Nanjing University of Science and Technology,
来源：
Mobile Networks and Applications | 2020年 / 25卷
关键词：
Road detection; Millimeter-wave radar; Modified occupancy grid map; Modified RANSAC; Unmanned ground vehicle;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：1496 / 1503
页数：7
相关论文
共 40 条
  • [1] Lu H(2019)Cognitive internet of vehicles for automatic driving IEEE Netw 33 65-73
  • [2] Liu G(2018)An illumination robust road detection method based on color names and geometric information Cogn Syst Res 52 240-250
  • [3] Li Y(2019)A real-time road detection method based on reorganized lidar data PLoS ONE 14 4-50
  • [4] Kim H(2014)Underwater image dehazing using joint trilateral filter Comput Electr Eng 40 41-96
  • [5] Serikawa S(2019)CONet: a cognitive ocean network IEEE Wirel Commun 26 90-148
  • [6] Xu F(2018)Low illumination underwater light field images reconstruction using deep convolutional neural networks Futur Gener Comput Syst 82 142-2322
  • [7] Hu B(2018)Motor anomaly detection for unmanned aerial vehicles using reinforcement learning IEEE Internet Things J 5 2315-822
  • [8] Chen L(1974)Automotive radar: a brief review Proc IEEE 62 804-53
  • [9] Xu F(1975)Automotive station keeping and braking radars. A review Microw J 18 49-131
  • [10] Chen L(1989)Cradar- an open-loop extended-mono pulse automotive radar IEEE Trans Veh Technol 38 123-105
1234