This paper develops a learning model for personalized adaptive cruise control that can learn from human demonstration online and mimic a human driver's driving strategies in the dynamic traffic environment. Under the framework of the proposed model, reinforcement learning is used to capture the human-desired driving strategy, and the proportion-integration-differentiation controller is adopted to convert the learning strategy to low-level control commands. The performance of the learning model is tested in the simulation environment built in a driving simulator using PreScan. Experimental results show that the learning model can duplicate human driving strategies with acceptable errors. Moreover, compared with the traditional adaptive cruise control, the proposed model can provide better driving comfort and smoothness in the dynamic situation.
A learning model for personalized adaptive cruise control
2017-06-01
380765 byte
Conference paper
Electronic Resource
English
A Learning Model for Personalized Adaptive Cruise Control
British Library Conference Proceedings | 2017
|Personalized Adaptive Cruise Control Considering Drivers’ Characteristics
SAE Technical Papers | 2018
|Personalized Adaptive Cruise Control Considering Drivers' Characteristics
British Library Conference Proceedings | 2018
|