Unmanned driving technologies have great potential in improving traffic safety and reducing driver workload, but the human driving mechanism is rarely considered. Human-like unmanned driving could meet the expectations of passengers and pedestrians, so that unmanned vehicles can be more widely accepted. However, current human-like unmanned driving methods rely heavily on historical data, only imitating the driver's behavior without fundamentally explaining the motivation behind his behavior. This study develops a driver risk perception model that describes drivers' perceptions of risk for providing an in-depth explanation of human driving mechanisms to lay a theoretical foundation for human-like driving methods. It also reveals the characteristics of the driver's perceived risk index during lane changing and its relationship with the lane change trajectory. Finally, it proposes a human-like autonomous lane change trajectory planning method, which achieves autonomous planning of human-like lane change trajectory with high fidelity by using the position and speed information of the ego vehicle and obstacles.


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    Title :

    Human-Like Autonomous Lane Change Trajectory Planning Based on Driver Risk Perception Model


    Contributors:
    Mei, Jiahao (author) / Luo, Longxi (author) / Liu, Minghao (author) / Chen, Yu (author)


    Publication date :

    2023-10-13


    Size :

    1211956 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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