The performance improvement of perception algorithms and integrated validation of driving decision making remains challenging in the fields of computer vision and intelligent transportation systems. In this paper, we propose a novel vision-based framework for driving decision making, which is composed of three stages: object perception, lane line perception and driving decision making. For the object perception stage, an improved object perception model named CenterNet-ARA is developed, composing of a new adversarial training method, a receptive field enhancement module and an adaptive sample allocation equalization strategy to fuse multi-scale feature maps. For the lane line perception stage, a lane line perception method named Lite-MobileTR is proposed, which contains an improved Lite-MobileNetV3 encoder and an improved lite-transformer decoder. Moreover, a noise removal task is incorporated to alleviate the problem of slow convergence speed caused by Hungarian loss function. For the driving decision making stage, a new Multi-Action DQN is proposed utilizing a vehicle curriculum learning strategy and a curiosity exploration strategy to alleviate the problem of random exploration in the learning process. The proposed framework is evaluated on the Tusimple, CULane, TSD-max, and KITTI datasets. Finally, an integration verification is performed in Carla simulator to validate the driving decision making process. The experimental results well demonstrate the effectiveness of the proposed framework.


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    Title :

    Vision-Based Driving Decision Making Using Multi-Action Deep Q Network


    Contributors:
    Yuan, Sheng (author) / Li, Yaochen (author) / Zhao, Kai (author) / Zhu, Li (author) / Guo, Jiaxin (author) / Ma, Xinnan (author) / Xu, Yuncheng (author)


    Publication date :

    2025-05-01


    Size :

    4332074 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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