基于动态占用网格改进概率模型的换道辅助策略

杨正才; 高振海; 高菲; 武馨宇; 何磊

doi:10.1631/FITEE.2000439

Your Location：

Home >

Browse articles >

基于动态占用网格改进概率模型的换道辅助策略

常规文章 | Updated：2022-06-06

- 基于动态占用网格改进概率模型的换道辅助策略
- Lane changing assistance strategy based on an improved probabilistic model of dynamic occupancy grids
- 信息与电子工程前沿（英文） 2021年22卷第11期页码：1492-1504
- Affiliations：
  
  State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
  School of Automotive Engineering, Hubei University of Automotive Technology, Shiyan 442002, China
- Author bio：
  
  Zhenhai GAO, E-mail: gaozh@jlu.edu.cn
- Funds：
  
  Project supported by the National Key Research and Development Program of China (No. 2017YFB0102601) and the Hubei Provincial Key Research and Development Project, China (No. 2020BAB099)
- DOI：10.1631/FITEE.2000439
  中图分类号： TP391
- 收稿：2020-08-30，
  
  修回：2021-;9-;2，
  
  纸质出版：2021-11
- Accepted：
Scan QR Code
杨正才, 高振海, 高菲, 等. 基于动态占用网格改进概率模型的换道辅助策略[J]. 信息与电子工程前沿（英文）, 2021,22(11):1492-1504.

Zhengcai YANG, Zhenhai GAO, Fei GAO, et al. Lane changing assistance strategy based on an improved probabilistic model of dynamic occupancy grids[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(11): 1492-1504.
杨正才, 高振海, 高菲, 等. 基于动态占用网格改进概率模型的换道辅助策略[J]. 信息与电子工程前沿（英文）, 2021,22(11):1492-1504. DOI： 10.1631/FITEE.2000439.

Zhengcai YANG, Zhenhai GAO, Fei GAO, et al. Lane changing assistance strategy based on an improved probabilistic model of dynamic occupancy grids[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(11): 1492-1504. DOI： 10.1631/FITEE.2000439.

摘要

自动驾驶汽车换道辅助是一个热门研究课题。驾驶区域场景建模是解决换道决策问题的前提。提出一种基于动态占用网格的道路环境表示方法。该模型将车辆速度、障碍物、车道线和交通规则等信息封装成一种空间驾驶概率的形式。这些信息被编译成哈希表，通过哈希函数将网格图映射到一个哈希图中。利用该模型建立一个车辆行为决策成本方程，该方程基于最小成本原则，同时考虑车辆驾驶性能、安全性和动力等影响因素，辅助驾驶员做出准确换道决策。通过车辆测试验证了该换道辅助策略的可行性。结果表明，基于动态占用网格概率模型的换道辅助系统可为驾驶者提供兼顾动力和安全性的换道辅助。

Abstract

Lane changing assistance in autonomous vehicles is a popular research topic. Scene modeling of the driving area is a prerequisite for lane changing decision problems. A road environment representation method based on a dynamic occupancy grid is proposed in this study. The model encapsulates the data such as vehicle speed

obstacles

lane lines

and traffic rules into a form of spatial drivability probability. This information is compiled into a hash table

and the grid map is mapped into a hash map by means of hash function. A vehicle behavior decision cost equation is established with the model to help drivers make accurate vehicle lane changing decisions based on the principle of least cost

while considering influencing factors such as vehicle drivability

safety

and power. The feasibility of the lane changing assistance strategy is verified through vehicle tests

and the results show that the lane changing assistance system based on a probabilistic model of dynamic occupancy grids can provide lane changing assistance to drivers taking into consideration the dynamics and safety.

关键词

Keywords

references

C Coue , , , T Fraichard , , , P Bessiere , , , 等 . . Using Bayesian programming for multi-sensor multi-target tracking in automotive applications . . IEEE Int Conf on Robotics and Automation , , 2003 . . p.2104 - - 2109 . . DOI: 10.1109/ROBOT.2003.1241904 http://doi.org/10.1109/ROBOT.2003.1241904 . .

R Danescu , , , F Oniga , , , S Nedevschi . . Modeling and tracking the driving environment with a particle-based occupancy grid . . IEEE Trans Intell Transp Syst , , 2011 . . 12 ( ( 4 ): ): 1331 - - 1342 . . DOI: 10.1109/tits.2011.2158097 http://doi.org/10.1109/tits.2011.2158097 . .

A Elfes . . Using occupancy grids for mobile robot perception and navigation . . Computer , , 1989 . . 22 ( ( 6 ): ): 46 - - 57 . . DOI: 10.1109/2.30720 http://doi.org/10.1109/2.30720 . .

J Gao , , , YL Murphey , , , HH Zhu . . Personalized detection of lane changing behavior using multisensor data fusion . . Computing , , 2019 . . 101 ( ( 4 ): ): 1837 - - 1860 . . DOI: 10.1007/s00607-019-00712-9 http://doi.org/10.1007/s00607-019-00712-9 . .

F Hui , , , KN Mu , , , XM Zhao . . Assistant driving decision method of vehicle lane change based on dynamic probability grid and Bayesian decision network . . J Traff Transp Eng , , 2018 . . 18 ( ( 2 ): ): 148 - - 158 . . DOI: 10.19818/j.cnki.1671-1637.2018.02.016 http://doi.org/10.19818/j.cnki.1671-1637.2018.02.016 . .

DJ Kim , , , SH Lee , , , CC Chung . . Object vehicle motion prediction based on dynamic occupancy grid map utilizing cascaded support vector machine . . Proc 19 th Int Conf on Control, Automation and Systems , , 2019 . . p. 496 - - 500 . . DOI: 10.23919/ICCAS47443.2019.8971617 http://doi.org/10.23919/ICCAS47443.2019.8971617 . .

P Kumar , , , M Perrollaz , , , S Lefèvre , , , 等 . . Learning-based approach for online lane change intention prediction . . IEEE Intelligent Vehicles Symp , , 2013 . . p. 797 - - 802 . . DOI: 10.1109/IVS.2013.6629564 http://doi.org/10.1109/IVS.2013.6629564 . .

HP Moravec . . Sensor fusion in certainty grids for mobile robots . . AI Mag , , 1988 . . 9 ( ( 2 ): ): 61 - - 74 . . . .

P Nadarajan , , , M Botsch . . Probability estimation for predicted-occupancy grids in vehicle safety applications based on machine learning . . IEEE Intelligent Vehicles Symp , , 2016 . . p.1285 - - 1292 . . DOI: 10.1109/IVS.2016.7535556 http://doi.org/10.1109/IVS.2016.7535556 . .

National Highway Traffic Safety Administration . . Traffic Safety Facts 2019: A Compilation of Motor Vehicle Crash Data (Report No. DOT HS 813 141) , , 2021 . . .

A Nègre , , , L Rummelhard , , , C Laugier . . Hybrid sampling Bayesian occupancy filter . . IEEE Intelligent Vehicles Symp Proceeding , , 2014 . . p.1307 - - 1312 . . DOI: 10.1109/ivs.2014.6856554 http://doi.org/10.1109/ivs.2014.6856554 . .

SI Oh , , , HB Kang . . Fast occupancy grid filtering using grid cell clusters from LIDAR and stereo vision sensor data . . IEEE Sens J , , 2016 . . 16 ( ( 19 ): ): 7258 - - 7266 . . DOI: 10.1109/jsen.2016.2598600 http://doi.org/10.1109/jsen.2016.2598600 . .

E Richter , , , P Lindner , , , G Wanielik , , , 等 . . Advanced occupancy grid techniques for lidar based object detection and tracking . . Proc 12 th Int IEEE Conf on Intelligent Transportation Systems , , 2009 . . p.1 - - 5 . . DOI: 10.1109/ITSC.2009.5309730 http://doi.org/10.1109/ITSC.2009.5309730 . .

C Robbiano , , , EKP Chong , , , MR Azimi-Sadjadi , , , 等 . . Bayesian learning of occupancy grids . . IEEE Trans Intell Transp Syst, early access , , 2020 . . DOI: 10.1109/TITS.2020.3019813 http://doi.org/10.1109/TITS.2020.3019813 . .

S Sivaraman , , , MM Trivedi . . Dynamic probabilistic drivability maps for lane change and merge driver assistance . . IEEE Trans Intell Transp Syst , , 2014 . . 2014 ( ( 5 ): ): 2063 - - 2073 . . DOI: 10.1109/TITS.2014.2309055 http://doi.org/10.1109/TITS.2014.2309055 . .

M Schreier , , , V Willert , , , J Adamy . . Compact representation of dynamic driving environments for ADAS by parametric free space and dynamic object maps . . IEEE Trans Intell Transp Syst , , 2016 . . 17 ( ( 2 ): ): 367 - - 384 . . DOI: 10.1109/TITS.2015.2472965 http://doi.org/10.1109/TITS.2015.2472965 . .

R Schubert , , , K Schulze , , , G Wanielik . . Situation assessment for automatic lane-change maneuvers . . IEEE Trans Intell Transp Syst , , 2010 . . 11 ( ( 3 ): ): 607 - - 616 . . DOI: 10.1109/TITS.2010.2049353 http://doi.org/10.1109/TITS.2010.2049353 . .

O Sencan , , , H Temeltas . . A quantized approach for occupancy grids for autonomous vehicles: Q-Trees . . Adv Rob , , 2018 . . 32 ( ( 11 ): ): 575 - - 589 . . DOI: 10.1080/01691864.2018.1468818 http://doi.org/10.1080/01691864.2018.1468818 . .

N Smirnov , , , YZ Liu , , , A Validi , , , 等 . . A game theory-based approach for modeling autonomous vehicle behavior in congested, urban lane-changing scenarios . . Sensors , , 2021 . . 21 ( ( 4 ): ): 1523 DOI: 10.3390/s21041523 http://doi.org/10.3390/s21041523 . .

JJ Wang , , , QC Zhang , , , DB Zhao , , , 等 . . Lane change decision-making through deep reinforcement learning with rule-based constraints . . Int Joint Conf on Neural Networks , , 2019 . . p.1 - - 6 . . DOI: 10.1109/IJCNN.2019.8852110 http://doi.org/10.1109/IJCNN.2019.8852110 . .

NA Yatim , , , N Buniyamin , , , ZM Noh , , , 等 . . Occupancy grid map algorithm with neural network using array of infrared sensors . . J Phys , , 2020 . . 1502 012053 DOI: 10.1088/1742-6596/1502/1/012053 http://doi.org/10.1088/1742-6596/1502/1/012053 . .

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据