Abstract:

As the number of connected vehicles increases, the intelligence levels become more and more uneven, so the problem how to determine the dynamic safety of autonomous driving behavior in the mixed-flow traffic system is significantly increased. To solve many difficult problems related to human-vehicle-road perception, decision, control coordination, and reliability evaluation in the intelligent networked scenario, this study establishes a dynamic Game model with multi-source information in the intelligent networked environment, to carry out the measurements and control evaluations for autonomous vehicle dynamics models, forward and backward active safety control, and mixed traffic trajectory optimization planning based on the optimal solution strategy. A digital twin test verification platform with semi-physical environment and hardware-in-the-loop (HIL) simulation to control the accuracy for making dynamic safety decision for the intelligent networked vehicle is developed by combining with the V2X real intelligent transportation system and smart laboratory virtual simulation test technology, which realizes the complex and dynamic safety decision goals for autonomous vehicle in different connected levels of the mixed-flow traffic environments, such as multi-agent perception, multi-source information transmission, vehicle control, vehicle-to-vehicle communication, and vehicle-to-road coordination. The study is conducive to improving the test efficiency and index evaluation integrity of the intelligent networked system, reducing the test costs, proving the behaviors of Game interaction and stress safety response under vehicle-to-road environment, enhancing the robustness and applicability of automatic driving technology, and improving the traffic efficiency and safety of mixed-flow traffic in the intelligent network.

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