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LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking.

Yukai Ma, Tiantian Wei, Naiting Zhong

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    LeapVAD enhances autonomous driving by mimicking human cognition. This novel method uses dual-process thinking and a human-attentional mechanism for improved decision-making in complex traffic scenarios.

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    Area of Science:

    • Artificial Intelligence
    • Robotics
    • Computer Vision

    Background:

    • Data-driven autonomous driving struggles with complex scenarios due to limited reasoning.
    • Knowledge-driven systems are advancing with visual language models.

    Purpose of the Study:

    • To introduce LeapVAD, a novel cognitive perception and dual-process thinking method for autonomous driving.
    • To improve environmental representation and decision-making in autonomous vehicles.

    Main Methods:

    • Human-attentional mechanism to focus on critical traffic elements.
    • Dual-process decision-making (analytic System-II and heuristic System-I).
    • Reflective mechanisms, memory bank, and scene encoder for continuous learning and efficiency.

    Main Results:

    • LeapVAD demonstrates superior performance over camera-only methods in CARLA and DriveArena simulators, even with limited data.
    • Effectiveness in continuous learning and domain adaptation confirmed through ablation studies.

    Conclusions:

    • LeapVAD offers a promising approach for more robust and adaptable autonomous driving systems.
    • The cognitive perception and dual-process thinking framework significantly enhances decision-making capabilities.