Research on decision fusion model for driving unmanned logistic vehicles based on complex road conditions

Yong-Jie Zhang

doi:10.1117/12.2684722

21 July 2023 Research on decision fusion model for driving unmanned logistic vehicles based on complex road conditions

Yong-Jie Zhang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127170B (2023) https://doi.org/10.1117/12.2684722
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Traditional unmanned logistics vehicles mainly adopt a driving strategy of running along the track, which cannot adapt to complex roads and traffic. This paper introduces a fusion decision model for the behavior of unmanned logistics vehicles in complex road conditions, which can support intersections with traffic lights and complex traffic scenarios with multiple vehicles and multiple pedestrian flows. The paper implements a multi-state machine behavior fusion decision model for unmanned logistics vehicles by separately modeling the vehicle, pedestrian, and navigation situations at intersections with finite state machines, and then implementing model fusion through a fusion decision model. The paper use test vehicles to test in an indoor open road test site, and the experimental results show that the model can effectively avoid unmanned logistics vehicle blockage and route deviation at intersections, improve unmanned logistics vehicle intersection passage efficiency, and solve the technical problem that the traditional unmanned logistics vehicle cannot adapt to complex traffic road conditions.

Citation Download Citation

Yong-Jie Zhang "Research on decision fusion model for driving unmanned logistic vehicles based on complex road conditions", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127170B (21 July 2023); https://doi.org/10.1117/12.2684722

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