Affiliation(s): The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; School of Artificial Intelligence, Anhui University, Hefei 230039, China; Shanghai AI Laboratory, Shanghai 200232, China; less
Received: 2022-07-28
Accepted: 2022-12-14
Available online: 2022-12-14
Abstract
is shifting from passive control to proactive control, which enables the controller to direct current traffic flow to reach its expected destinations. To this end, an effective prediction model is needed for signal controllers. What to predict, how to predict, and how to leverage the prediction for control policy optimization are critical problems for proactive . In this paper, we use an image that contains vehicle positions to describe intersection traffic states. Then, inspired by a model-based method, DreamerV2, we introduce a novel learning-based . The that describes traffic dynamics in image form is used as an abstract alternative to the traffic environment to generate multi-step planning data for control policy optimization. In the execution phase, the optimized traffic controller directly outputs actions in real time based on abstract representations of traffic states, and the world model can also predict the impact of different control behaviors on future traffic conditions. Experimental results indicate that the enables the optimized real-time control policy to outperform common baselines, and the model achieves accurate image-based prediction, showing promising applications in futuristic .
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