TY - JOUR
T1 - 智能车辆多信号灯路口快速节能车速规划
AU - Leng, Jianghao
AU - Sun, Chao
AU - Lu, Bing
N1 - Publisher Copyright:
© 2021, Society of Automotive Engineers of China. All right reserved.
PY - 2021/10/25
Y1 - 2021/10/25
N2 - The existing energy⁃saving speed planning method of intelligent vehicles for complex scenes at multi signal intersections is usually with big computational burden that cannot be implemented in real-time. This paper proposes an optimized intelligent vehicle energy⁃saving speed planning method combining dynamic programming and the interior point method, which can rapidly calculate the road energy⁃saving reference speed based on the vehicle fuel consumption model and the traffic light information, and realize real⁃time application. Firstly, in this paper, the vehicle fuel consumption model and the signal phase and time mathematical model are established. A weighted orientation graph based on energy consumption per unit distance at cruise speed is constructed by sampling the arrival time at each intersection. The weighted orientation graph is solved via dynamic programming to obtain the optimal period of green light passing phase. Then, taking the optimal green light passing phase as the constraint, the optimal control problem is formulated and transformed into a nonlinear optimization problem, which is solved via the interior point method. To accelerate convergence speed, the initial guess of the interior point method is set to the solution of the weighted orientation graph solved by dynamic programming. Finally, simulation is carried out for evaluation. The results show that: (1) the proposed method can reduce energy consumption by over 30% compared with the improved intelligent driver model; (2) the initial guess close to optimal solution calculated by dynamic programming improves computational efficiency by more than 40%, compared with the rule⁃based iterative initial guess generation method.
AB - The existing energy⁃saving speed planning method of intelligent vehicles for complex scenes at multi signal intersections is usually with big computational burden that cannot be implemented in real-time. This paper proposes an optimized intelligent vehicle energy⁃saving speed planning method combining dynamic programming and the interior point method, which can rapidly calculate the road energy⁃saving reference speed based on the vehicle fuel consumption model and the traffic light information, and realize real⁃time application. Firstly, in this paper, the vehicle fuel consumption model and the signal phase and time mathematical model are established. A weighted orientation graph based on energy consumption per unit distance at cruise speed is constructed by sampling the arrival time at each intersection. The weighted orientation graph is solved via dynamic programming to obtain the optimal period of green light passing phase. Then, taking the optimal green light passing phase as the constraint, the optimal control problem is formulated and transformed into a nonlinear optimization problem, which is solved via the interior point method. To accelerate convergence speed, the initial guess of the interior point method is set to the solution of the weighted orientation graph solved by dynamic programming. Finally, simulation is carried out for evaluation. The results show that: (1) the proposed method can reduce energy consumption by over 30% compared with the improved intelligent driver model; (2) the initial guess close to optimal solution calculated by dynamic programming improves computational efficiency by more than 40%, compared with the rule⁃based iterative initial guess generation method.
KW - Dynamic programming
KW - Interior point method
KW - Multi signal intersections
KW - Speed planning
UR - http://www.scopus.com/inward/record.url?scp=85118196551&partnerID=8YFLogxK
U2 - 10.19562/j.chinasae.qcgc.2021.10.004
DO - 10.19562/j.chinasae.qcgc.2021.10.004
M3 - 文章
AN - SCOPUS:85118196551
SN - 1000-680X
VL - 43
SP - 1442
EP - 1447
JO - Qiche Gongcheng/Automotive Engineering
JF - Qiche Gongcheng/Automotive Engineering
IS - 10
ER -