TY - JOUR
T1 - 基于模型预测控制的可变目标距离自适应巡航控制研究
AU - Zhao, Ya'nan
AU - Wang, Taixiang
AU - Gao, Li
AU - Sun, Haixin
AU - Wang, Xiancai
N1 - Publisher Copyright:
© 2023 Beijing Institute of Technology. All rights reserved.
PY - 2023/5
Y1 - 2023/5
N2 - To solve the problems presented to the adaptive cruise system in the complex traffic environment, an adaptive cruise control algorithm with variable target distance control algorithm was proposed. The complex traffic conditions mainly consider as following two aspects. A side car cut into the front of the vehicle, causing the expected relative distance obtained from the target expected distance calculation model emerge a step change from the actual relative distance. Closing to the car in front, the target vehicle must start and stop constantly in congested road conditions, causing the speed, acceleration and relative distance of the target vehicle continue change, resulting in driving smoothness. For driving comfort and safety, the adaptive cruise control algorithm was arranged firstly to establish a discrete longitudinal kinematics prediction model based on the model predictive control theory. And then, considering the chassis acceleration response, the ultimate safe longitudinal following distance, the physical limitations of the vehicle itself, the driver's riding comfort and other optimal control objectives, the soft factor was introduced to obtain the feasible solution online. The algorithm was simulated and tested in real vehicles under different cut in conditions, comprehensive driving conditions and traffic jam creeping conditions, and the open-loop experiment of IDM algorithm was conducted with data. The comparison of research results shows that the adaptive cruise control algorithm considering the variable target distance of the cut in by the side vehicle can provide a maximum impact of −0.25 m/s3 on the longitudinal control under the acceleration cut in conditions of the side vehicle, lowering 50% than the IDM model . The maximum deceleration generated by longitudinal control in the traffic jam creeping condition can achieve −0.3 m/s2, lowering 30% than the IDM model. In the comprehensive condition and constant speed cruise condition, the algorithm can achieve stable longitudinal control of the vehicle, maintaining a safe distance, and the acceleration amplitude is not more than −0.36 m/s2, improving the driver's comfort, smoothness and safety effectively.
AB - To solve the problems presented to the adaptive cruise system in the complex traffic environment, an adaptive cruise control algorithm with variable target distance control algorithm was proposed. The complex traffic conditions mainly consider as following two aspects. A side car cut into the front of the vehicle, causing the expected relative distance obtained from the target expected distance calculation model emerge a step change from the actual relative distance. Closing to the car in front, the target vehicle must start and stop constantly in congested road conditions, causing the speed, acceleration and relative distance of the target vehicle continue change, resulting in driving smoothness. For driving comfort and safety, the adaptive cruise control algorithm was arranged firstly to establish a discrete longitudinal kinematics prediction model based on the model predictive control theory. And then, considering the chassis acceleration response, the ultimate safe longitudinal following distance, the physical limitations of the vehicle itself, the driver's riding comfort and other optimal control objectives, the soft factor was introduced to obtain the feasible solution online. The algorithm was simulated and tested in real vehicles under different cut in conditions, comprehensive driving conditions and traffic jam creeping conditions, and the open-loop experiment of IDM algorithm was conducted with data. The comparison of research results shows that the adaptive cruise control algorithm considering the variable target distance of the cut in by the side vehicle can provide a maximum impact of −0.25 m/s3 on the longitudinal control under the acceleration cut in conditions of the side vehicle, lowering 50% than the IDM model . The maximum deceleration generated by longitudinal control in the traffic jam creeping condition can achieve −0.3 m/s2, lowering 30% than the IDM model. In the comprehensive condition and constant speed cruise condition, the algorithm can achieve stable longitudinal control of the vehicle, maintaining a safe distance, and the acceleration amplitude is not more than −0.36 m/s2, improving the driver's comfort, smoothness and safety effectively.
KW - adaptive cruise control
KW - model predict control
KW - side car cut in
KW - traffic jam creeping
KW - variable target distanc
UR - http://www.scopus.com/inward/record.url?scp=85170217561&partnerID=8YFLogxK
U2 - 10.15918/j.tbit1001-0645.2022.136
DO - 10.15918/j.tbit1001-0645.2022.136
M3 - 文章
AN - SCOPUS:85170217561
SN - 1001-0645
VL - 43
SP - 499
EP - 509
JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
IS - 5
ER -