Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach
Artikel i vetenskaplig tidskrift, 2020

The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle's driving behaviour will no longer be solely based on the driver's limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.


Neural network

deep deterministic policy gradient


reinforcement learning

machine learning

traffic light

traffic oscillation


M. F. Zhou


Yang Yu

School of Civil and Environmental Engineering

Xiaobo Qu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 21 1 433-443 8848852


Transportteknik och logistik





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