Design of Intelligent Connected Cruise Control With Vehicle-to-Vehicle Communication Delays

Connected cruise control (CCC) refers to a type of advanced driver assistance system combined with wireless vehicle-to-vehicle (V2V) communication technology to improve control stability and driving safety. However, it is urgent to investigate intelligent control algorithms to improve the adaptabili...

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Published in:IEEE transactions on vehicular technology Vol. 71; no. 8; pp. 9011 - 9025
Main Authors: Wang, Zhuwei, Jin, Senfan, Liu, Lihan, Fang, Chao, Li, Meng, Guo, Song
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Advanced driver assistance systems
Algorithms
Communication
Communication delays
connected cruise control
Control algorithms
Control stability
Controllers
Cruise control
Decision analysis
Delays
dynamic traffic
Heuristic algorithms
intelligent control algorithm
Machine learning
Markov processes
Optimal control
Optimization
Performance degradation
Real-time systems
Stability analysis
Vehicle dynamics
Vehicle safety
Wireless communications
ISSN:0018-9545, 1939-9359
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Abstract Connected cruise control (CCC) refers to a type of advanced driver assistance system combined with wireless vehicle-to-vehicle (V2V) communication technology to improve control stability and driving safety. However, it is urgent to investigate intelligent control algorithms to improve the adaptability of CCC in complex traffic environment. In addition, the effect of communication delays attributed to shared wireless communications on the performance degradation of the intelligent CCC design cannot be ignored. In this study, the design of deep reinforcement learning (DRL) controller for CCC system in high-dynamic traffic scenarios is investigated, which considers both time-varying leading velocity and communication delays. To be more specific, an intelligent CCC algorithm based on deep deterministic policy gradient (DDPG) is developed. According to the training samples obtained from interacting with the traffic environment, the actor network and the critic network are trained to maximize the quadratic reward function determined by state errors and control inputs for generating intelligent control strategies. In particular, the effect of previous control strategies is considered in vehicle dynamics analysis, optimization problem formulation and Markov decision process (MDP) definition to compensate the performance degradation attributed to communication delays. Lastly, the effectiveness and convergence of the proposed DRL-based CCC controller are verified through numerical simulations with various conditions. The superior performance of the proposed algorithm is shown by comparing with existing traditional algorithms and state-of-the-art DRL algorithms.
AbstractList Connected cruise control (CCC) refers to a type of advanced driver assistance system combined with wireless vehicle-to-vehicle (V2V) communication technology to improve control stability and driving safety. However, it is urgent to investigate intelligent control algorithms to improve the adaptability of CCC in complex traffic environment. In addition, the effect of communication delays attributed to shared wireless communications on the performance degradation of the intelligent CCC design cannot be ignored. In this study, the design of deep reinforcement learning (DRL) controller for CCC system in high-dynamic traffic scenarios is investigated, which considers both time-varying leading velocity and communication delays. To be more specific, an intelligent CCC algorithm based on deep deterministic policy gradient (DDPG) is developed. According to the training samples obtained from interacting with the traffic environment, the actor network and the critic network are trained to maximize the quadratic reward function determined by state errors and control inputs for generating intelligent control strategies. In particular, the effect of previous control strategies is considered in vehicle dynamics analysis, optimization problem formulation and Markov decision process (MDP) definition to compensate the performance degradation attributed to communication delays. Lastly, the effectiveness and convergence of the proposed DRL-based CCC controller are verified through numerical simulations with various conditions. The superior performance of the proposed algorithm is shown by comparing with existing traditional algorithms and state-of-the-art DRL algorithms.
Author Jin, Senfan
Liu, Lihan
Wang, Zhuwei
Fang, Chao
Guo, Song
Li, Meng
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Snippet Connected cruise control (CCC) refers to a type of advanced driver assistance system combined with wireless vehicle-to-vehicle (V2V) communication technology...
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SubjectTerms Advanced driver assistance systems
Algorithms
Communication
Communication delays
connected cruise control
Control algorithms
Control stability
Controllers
Cruise control
Decision analysis
Delays
dynamic traffic
Heuristic algorithms
intelligent control algorithm
Machine learning
Markov processes
Optimal control
Optimization
Performance degradation
Real-time systems
Stability analysis
Vehicle dynamics
Vehicle safety
Wireless communications
Title Design of Intelligent Connected Cruise Control With Vehicle-to-Vehicle Communication Delays
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