International Conference on Transportation and Development 2020
Lane-Changing Decision Model for Connected and Automated Vehicle Based on Back-Propagation Neural Network
Publication: International Conference on Transportation and Development 2020
ABSTRACT
Connected and automated vehicle (CAV) plays an important role in the intelligent transportation system. The decision of CAV’s lane-changing behavior in heterogeneous traffic flow composed of CAV and regular vehicle (RV) becomes a key link. This paper builds a framework of lane-changing decision (LCDF) for CAV training and leaning. It consists of two parts: a micro simulation model based on cellular automata and a back-propagation neural network model that can quickly decide to change lane or still travel on the current lane. In LCDF, CAV targets a higher average speed in a certain time in the future based on what the on-board sensors and on-board communication equipment currently have access to. Then, the decision of CAV’s lane-changing behavior is trained within LCDF. In addition, normal prediction index (RMSE and MAPE) and the simulation result index (Ek and Er) we defined were conducted to evaluate the influence of LCDF’s lane-changing decision on traffic. The results show that the efficiency of the trained CAV’s speed increases 6.7% compared to traditional model. It indicates that our framework is effective for CAV’s lane-changing decision.
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Published In
International Conference on Transportation and Development 2020
Pages: 163 - 173
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8313-8
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Aug 31, 2020
Published in print: Aug 31, 2020
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