自动驾驶车辆基于风险场的盲区避障方法

doi:10.13229/j.cnki.jdxbgxb.20241264

Abstract

Abstract:

To address the safety hazards in driving blind spot scenarios， a collaborative obstacle avoidance planning algorithm for vehicles based on a collision risk field model was proposed. Considering the impact of vehicle-to-vehicle communication continuity， a communication continuity risk field model is constructed. This risk field was incorporated into the dynamic path planning algorithm， and an adaptive obstacle avoidance safety cost function based on the communication continuity impact factor was designed. Simulation results demonstrate that the proposed risk model-based obstacle avoidance planning algorithm can accurately reflect dynamic risks in driving blind spots， enhancing the safety of autonomous vehicles in extreme situations.

Key words: obstacle avoidance planning, autonomous driving, vehicle-to-vehicle communication, risk assessment, dynamic programming

CLC Number:

U491.6

Ning TONG,Ya-xing QIAO,Jun-tong JIANG,Li-zhi WANG. Obstacle avoidance method for autonomous vehicles based on risk field in blind spot scenarios[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(7): 1894-1903.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Risk field parameter setting"

参数	数值	描述
$l m a x$ /m	30.451 1	风险场影响最大范围
$l m i n$ /m	5.330 6	以车辆为中心的风险场边界
$δ$ /m	32.970 7	跟车距离
$M 1$ /kg	1 500	产生风险场的重力
$S m a x$ /（m·s^-1）	55.56	车道最大限速
$a$ /m	33.451 1	椭圆的长半轴
$b$ /m	3.5	椭圆的短半轴

Table 2

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

References 21

[1]	Li H M, Dong X, Rasheed I, et al. A software-defined networking roadside unit cloud resource management framework for vehicle ad hoc networks[J]. Journal of Advanced Transportation, 2022, 2022: 1-13.
[2]	Lu J Y, Peng Z X, Shi R W, et al. A quantitative blind area risks assessment method for safe driving assistance[J]. Journal of Systems Architecture, 2024, 150: 1-15.
[3]	Wang L H, Zhang F Q, Cui Y H, et al. Stochastic velocity prediction for connected vehicles considering V2V communication interruption[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11): 11654-11667.
[4]	Jo J H, Shim J N, Kim B, et al. AoA-based position and orientation estimation using lens MIMO in cooperative vehicle-to-vehicle systems[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(12): 3719-3735.
[5]	陈吉清, 翁楚滨, 兰凤崇. 智能车辆换道潜在冲突分析与风险量化方法[J]. 汽车工程, 2021, 43(11): 1565-1576, 1586.
	Chen Ji-qing, Weng Chu-bin, Lan Feng-chong. Potential conflict analysis and risk quantification method of intelligent vehicle lane change[J]. Automotive Engineering, 2021, 43(11): 1565-1576, 1586.
[6]	赵睿, 李云, 胡宏宇, 等. 基于V2I通信的交叉口车辆碰撞预警方法[J]. 吉林大学学报: 工学版, 2023, 53(4): 1019-1029.
	Zhao Rui, Li Yun, Hu Hong-yu, et al. Vehicle collision warning method at intersection based on V2I communication[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1019-1029.
[7]	郑讯佳, 蒋骏皓, 黄荷叶, 等. 行车风险量化新方法及其防控策略仿真研究[J]. 机械工程学报, 2024, 60(10): 207-221.
	Zheng Xun-jia, Jiang Jun-hao, Huang He-ye, et al. Novel quantitative approach for assessing driving risks and simulation study of its prevention and control strategies[J]. Journal of Mechanical Engineering, 2024, 60(10): 207-221.
[8]	Lee M, Jo K, Sunwoo M. Collision risk assessment for possible collision vehicle in occluded area based on precise map[C]∥IEEE International Conference on Intelligent Transportation Systems, Yokohama, Japan, 2017: 1-6.
[9]	封硕, 吉现友, 程博, 等. 融合动态障碍物运动信息的路径规划算法[J]. 计算机工程与应用, 2022, 58(21): 279-285.
	Feng Shuo, Ji Xian-you, Cheng Bo, et al. Path planning algorithm based on dynamic obstacle movement information[J]. Computer Engineering and Applications, 2022, 58(21): 279-285.
[10]	任玥, 郑玲, 张巍, 等. 基于模型预测控制的智能车辆主动避撞控制研究[J]. 汽车工程, 2019, 41(4): 404-410.
	Ren Yue, Zheng Ling, Zhang Wei, et al. A study on active collision avoidance control of autonomous vehicles based on model predictive control[J]. Automotive Engineering, 2019, 41(4): 404-410.
[11]	Szczepanski R. Safe artificial potential field-novel local path planning algorithm maintaining safe distance from obstacles[J]. IEEE Robotics and Automation Letters, 2023, 8(8): 4823-4830.
[12]	韩小健, 赵伟强, 陈立军, 等. 基于区域采样随机树的客车局部路径规划算法[J]. 吉林大学学报: 工学版, 2019, 49(5): 1428-1440.
	Han Xiao-jian, Zhao Wei-qiang, Chen Li-jun, et al. Local path planning of bus based on RS-RRT algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1428-1440.
[13]	Zheng Xun-jia, Zhang Di, Gao Hong-bo, et al. A novel framework for road traffic risk assessment with HMM-based prediction model[J]. Sensors, 2018, 18(12): 4313-4327.
[14]	Liu Fang-ming, Chen Jian, Zhang Qi-xia, et al. Online MEC offloading for V2V networks[J]. IEEE Transactions on Mobile Computing, 2023, 22(10): 6097-6109.
[15]	Meng Zong-lin, Xia Xin, Xu Run-sheng, et al. HYDRO-3D: hybrid object detection and tracking for cooperative perception using 3D LiDAR[J]. IEEE Transactions on Intelligent Vehicles, 2023, 8(8): 4069-4080.
[16]	Ke X C, Ho K C. Localization by doppler derivatives and doppler-shifted frequencies[J].EEE Transactions on Signal Processing, 2024, 72: 2890-2904.
[17]	Fomeni F D. A lifted-space dynamic programming algorithm for the quadratic knapsack problem[J]. Discrete Applied Mathematics, 2023, 335: 52-68.
[18]	Zhang Lin, Wu Da-de, Zhao Ming-jing, et al. Uncertainty relation and the constrained quadratic programming[J]. Physica Scripta, 2024, 99(6): 1-35.
[19]	Reiter R, Nurkanovic A, Frey J, et al. Frenet-Cartesian model representations for automotive obstacle avoidance within nonlinear MPC[J]. European Journal of Control, 2023, 74: 1-8.
[20]	Yoon S, Kwon Y, Ryu J, et al. Reinforcement-learning-based trajectory learning in frenet frame for autonomous driving[J]. Applied Sciences-Basel, 2024, 14(16):1-14.
[21]	Zhang Yi-xiao, Hao Rui, Zhang Ting-ting, et al. A trajectory optimization-based intersection coordination framework for cooperative autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 14674-14688.

Related Articles 15

[1]	Tian-jun FENG,Yan-ming HAO,Fei-yan LI,He-yao GAO,Yi-xian LIU,Nan LIU,Jin-feng LI. Lane⁃changing model for connected and automated vehicle aggregation considering driving styles [J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(7): 1834-1844.
[2]	Rui ZHAO,Qi-rui YUAN,Jia-jun LIAN,Fei GAO,Hong-yu HU,Zhen-hai GAO. Intelligent vehicle collision risk assessment considering dual uncertainties [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2597-2610.
[3]	Jian WANG,Chen-wei JIA. Trajectory prediction model for intelligent connected vehicle [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 1963-1972.
[4]	Zhao-xia LIU,Fui FU,Shi-feng NIU. Risk assessment in overtaking scenarios using extreme value theory and intelligent and connected information [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 925-937.
[5]	Xiang WANG,Guo-zhen TAN,Yan-fei PENG,Hao REN,Jian-ping LI. Autonomous driving decision⁃making model based on language reasoning and cognitive memory [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(12): 3918-3927.
[6]	Yu-tao LUO,Zhi-cheng XUE. Autonomous driving policy based on reinforcement learning with environment representation [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(10): 3169-3179.
[7]	Gui-zhen CHEN,Hui-ting CHENG,Cai-hua ZHU,Yu-ran LI,Yan LI. A risk evaluation method for urban intersections considering drivers' physiological information [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1277-1284.
[8]	Si-feng ZHU,Jiang-hao CAI,Zheng-yi CHAI,En-lin SUN. Computing offloading optimization scheme based on immune algorithm in edge computing scenes of internet of vehicles [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(1): 221-231.
[9]	Ke HE,Hai-tao DING,Xuan-qi LAI,Nan XU,Kong-hui GUO. Wheel odometry error prediction model based on transformer [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 653-662.
[10]	Bo TAO,Fu-wu YAN,Zhi-shuai YIN,Dong-mei WU. 3D object detection based on high⁃precision map enhancement [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 802-809.
[11]	Chun-hui LIU,Si-chang WANG,Ce ZHENG,Xiu-lian CHEN,Chun-lei HAO. Obstacle avoidance planning algorithm for indoor navigation robot based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3558-3564.
[12]	Qian CAO,Zhi-hui LI,Peng-fei TAO,Yong-jian MA,Chen-xi YANG. Traffic accident risk assessment method for road network considering risk heterogeneity [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(10): 2817-2825.
[13]	Xiao-hua WU,Zhong-wei YU,Zhang-ling ZHU,Xin-mei GAO. Fuzzy energy management strategy of fuel cell buses [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2077-2084.
[14]	Jie-yu ZHU,Yan-li MA. Real-time risk assessment method of multi-vehicle interaction at merging area [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1574-1581.
[15]	Xing-tao LIU,Si-yuan LIN,Ji WU,Yao HE,Xin-tian LIU. Regenerative braking optimization strategy considering battery state of power [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2796-2805.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Obstacle avoidance method for autonomous vehicles based on risk field in blind spot scenarios

RICH HTML

PDF (PC)