[1]LI Haiqing,LI Yongfu,ZHENG Taixiong,et al.Obstacle avoidance method for intelligent vehicles in complex environments[J].CAAI Transactions on Intelligent Systems,2023,18(6):1275-1286.[doi:10.11992/tis.202210033]
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Obstacle avoidance method for intelligent vehicles in complex environments

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 6 Page number: 1275-1286 Column: 学术论文—智能系统 Public date: 2023-11-05
Title:
Obstacle avoidance method for intelligent vehicles in complex environments
Author(s):
LI Haiqing1;  LI Yongfu2;  ZHENG Taixiong1;  LI Hongcheng1
1. School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, Chongqing 400065, China
Keywords:
intelligent vehicle; potential model; HMM; dynamic obstacle avoidance; path planning; tracking control; MPC; complex environments
CLC:
TP27；U491.25
DOI:
10.11992/tis.202210033
Abstract:
An obstacle avoidance method based on an improved potential field model and hidden Markov model (HMM) is proposed to enhance the safety and comfort of intelligent vehicles during obstacle avoidance. First, the method constructed an improved field model by considering typical elements of vehicle attributes, road environment, and driving states to predict the dynamic changes in collision risk. Further, HMM was used for obstacle avoidance decision making, and path planning was performed by incorporating HMM into the improved potential field model. Next, the model predictive control was used to track the planning paths in real time, and a relaxation factor and constraints were added to the controller to prevent the problem of unavailable optimal solutions. Finally, the cosimulations by CarSim and MATLAB/Simulink were conducted to verify the effectiveness of the proposed method. Results show that the proposed method can enhance driving safety and ride comfort while obtaining collision-free driving paths in various situations and realizing dynamic obstacle avoidance.
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