| MODEL PREDICTIVE ADAPTIVE CRUISE CONTROL OF INTELLIGENT
ELECTRIC VEHICLES BASED ON DEEP REINFORCEMENT LEARNING
ALGORITHM FWOR DRIVER CHARACTERISTICS |
| Jinghua Guo 1,2, WenChang Li 1, Yugong Luo 2, Keqiang Li 2 |
1Xiamen University
2Tsinghua University |
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| ABSTRACT |
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This paper presents a novel model predictive adaptive cruise control strategy of intelligent electric vehicles
based on deep reinforcement learning algorithm for driver characteristics. Firstly, the influence mechanism of factors such as
inter-vehicle distance, relative speed and time headway (THW) on the driver’s behavior in the process of car following is
analyzed by the correlation coefficient method. Then, the driver behavior in the process of car following is learned from the
natural driving data, the car following model is established by the deep deterministic policy gradient (DDPG) algorithm, and
the output acceleration of the DDPG model is used as the reference trajectory of the ego vehicle’s acceleration. Next, in order
to reflect the driver behavior and achieve multi performance objective optimization of adaptive cruise control of intelligent
electric vehicles, the model predictive controller (MPC) is designed and used for tracking the desired acceleration produced
by the car following DDPG model. Finally, the performance of the proposed adaptive cruise control strategy is evaluated by
the experimental tests, and the results demonstrate the effectiveness of proposed control strategy. |
| Key Words:
Intelligent electric vehicles, Adaptive cruise control, Deep reinforcement learning algorithm, Naturalistic
driving data, Driver model |
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Corresponding Author.
Jinghua Guo , Email. 
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