This paper constructs an interpretable driving model from the perspective of human cognition, which can not only imitate human driving behavior through ...
Jul 18, 2024 · Request PDF | On Jun 2, 2024, Yijia Li and others published Interpretable Autonomous Driving Model Based on Cognitive Reinforcement Learning ...
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Oct 7, 2024 · This work presents an interpretable decision-making framework for autonomous vehicles that integrates traffic regulations, norms, and safety guidelines ...
To address this issue, this paper proposes cognitive reinforcement learning that can both simulate the human driver's deliberation and provide interpretability ...
May 31, 2024 · We propose a decision-making model explicitly tailored for autonomous vehicles, comprising three distinct modules: needs assessment, motivation generation, and ...
Jul 26, 2022 · Welcome to IJCAI-ECAI 2022 AI4AD Workshop! https://learn-to-race.org/workshop-ai4ad-ijcai2022/ Title: An Interpretable Deep Reinforcement ...
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This paper provides an initial review of the current state and prospective advancements of world models in autonomous driving.
In this work, we present a novel tree-based model for autonomous vehicle control. Our Interpretable Continuous. Control Trees (ICCTs) have competitive ...
May 31, 2024 · We propose a decision-making model explicitly tailored for autonomous vehicles, comprising three distinct modules: needs assessment, motivation generation, and ...
This paper proposes a hierarchical reinforcement learning (HRL)-based framework with sound biological plausibility named Cog-MP.