Algorithm on lane changing and tracking control technology for Intelligent Vehicle

2007 ◽  
Author(s):  
You Feng ◽  
Wang Rongben ◽  
Zhang Ronghui
Keyword(s):  
Tracking Control ◽  
Intelligent Vehicle ◽  
Control Technology ◽  
Lane Changing

A Review on Lane Changing for Intelligent Vehicle

2015 ◽  
Vol 8 (3) ◽  
pp. 184-194 ◽  
Author(s):  
Ronghui Zhang ◽  
Fuliang Li ◽  
Xuecai Yu ◽  
Zhonghua Zhang ◽  
Feng You ◽  
...  
Keyword(s):  
Intelligent Vehicle ◽  
Lane Changing

Research on active collision avoidance algorithm for intelligent vehicle based on improved artificial potential field model

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091123
Author(s):  
Chaochun Yuan ◽  
Shuofeng Weng ◽  
Jie Shen ◽  
Long Chen ◽  
Youguo He ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Field Method ◽  
Superior Performance ◽  
Intelligent Vehicle ◽  
Artificial Potential Field ◽  
Sideslip Angle ◽  
Lane Changing ◽  
Model Based ◽  
Braking Process

In this article, an active collision avoidance based on improved artificial potential field is proposed to satisfy collision avoidance for intelligent vehicle. A longitudinal safety distance model based on analysis of braking process and a lane-changing safety spacing model based on minimum time of lane changing under the constraint of sideslip angle are presented. In addition, an improved artificial potential field method is introduced, which represents the influence of environmental information with artificial force. Simulation results demonstrate the superior performance of the proposed algorithm over collision avoidance for intelligent vehicle.

scholarly journals Trajectory planning and optimisation method for intelligent vehicle lane changing emergently

2018 ◽  
Vol 12 (10) ◽  
pp. 1336-1344 ◽  
Author(s):  
Haobin Jiang ◽  
Kaijin Shi ◽  
Junyu Cai ◽  
Long Chen
Keyword(s):  
Trajectory Planning ◽  
Intelligent Vehicle ◽  
Lane Changing

scholarly journals Expected yaw rate–based trajectory tracking control with vision delay for intelligent vehicle

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042093427
Author(s):  
Qiu Xia ◽  
Long Chen ◽  
Xing Xu ◽  
Yingfeng Cai ◽  
Haobin Jiang ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Intelligent Vehicle ◽  
Automatic Identification ◽  
Identification System ◽  
Vehicle Stability ◽  
Trajectory Tracking Control ◽  
Automatic Identification System ◽  
Kalman Predictor ◽  
Simulation Results

Accurate and real-time position of preview point is significant to trajectory tracking control of vision-guided intelligent vehicle. The unavoidable delay of road automatic identification system weakens trajectory tracking control performance, and even deteriorates the vehicle stability. Therefore, a compensator for the delay of road automatic identification system was proposed which combines the current statistical model and adaptive Kalman predictor to estimate the state of preview point position. The trajectory tracking sliding mode controller of intelligent vehicle is established through a 2–degrees of freedom vehicle dynamic model and motion model by using MATLAB/Simulink and CarSim. The trajectory tracking performance under 20–100 ms delay is analyzed. The simulation results show that the trajectory tracking performance of intelligent vehicle will be affected by the delay of road automatic identification system, reducing tracking accuracy. And when the delay is too large, it will deteriorate the vehicle stability and safety. In addition, the simulation results also verify the effectiveness of current statistical–adaptive Kalman predictor compensator at different delays.

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