面向以人为中心自动驾驶控制的神经认知奖励建模
Neuro-Cognitive Reward Modeling for Human-Centered Autonomous Vehicle Control
把脑电信号中的事件相关电位强度作为可预测的认知奖励信号，用视觉场景输入回归 ERP 强度并注入强化学习奖励，为视觉驾驶策略提供一种无需人工排序偏好数据的隐式人类对齐监督。
原文依据 we propose an electroencephalography (EEG)-guided decision-making framework to incorporate human cognitive insights into reinforcement learning (RL) for autonomous driving
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论计算机视觉的最新进展加速了自动驾驶的发展。尽管取得了这些进展，训练机器以符合人类期望的方式驾驶仍然是一项重大挑战。人的因素依然至关重要，因为人类拥有复杂的认知系统，能够快速解读场景信息并做出准确决策。使机器与人类意图对齐这一问题已通过基于人类反馈的强化学习（RLHF）得到探索。传统的 RLHF 方法依赖通过人工对 AI 生成输出进行排序来收集人类偏好数据，这一过程耗时且间接。在本工作中，我们提出一种脑电（EEG）引导的决策框架，将人类认知洞见融入自动驾驶的强化学习（RL）之中。我们在逼真的驾驶模拟器中采集了 20 名参与者的脑电信号，并分析了其对突发环境变化的事件相关电位（ERP）。我们提出的框架采用神经网络，基于来自视觉场景信息的认知信息预测 ERP 的强度。此外，我们探索了将此类认知信息整合进 RL 算法奖励信号的方式。实验结果表明，我们的框架能够提升 RL 算法的避碰能力，凸显了神经认知反馈在增强自动驾驶系统方面的潜力。
英文原文摘要 1467 字符
Recent advancements in computer vision have accelerated the development of autonomous driving. Despite these advancements, training machines to drive in a way that aligns with human expectations remains a significant challenge. Human factors are still essential, as humans possess a sophisticated cognitive system capable of rapidly interpreting scene information and making accurate decisions. Aligning machine with human intent has been explored with Reinforcement Learning with Human Feedback (RLHF). Conventional RLHF methods rely on collecting human preference data by manually ranking AI-generated outputs, which is time-consuming and indirect. In this work, we propose an electroencephalography (EEG)-guided decision-making framework to incorporate human cognitive insights into reinforcement learning (RL) for autonomous driving. We collected EEG signals from 20 participants in a realistic driving simulator and analyzed event-related potentials (ERP) in response to sudden environmental changes. Our proposed framework employs a neural network to predict the strength of ERP based on the cognitive information from visual scene information. Moreover, we explore the integration of such cognitive information into the reward signal of the RL algorithm. Experimental results show that our framework can improve the collision avoidance ability of the RL algorithm, highlighting the potential of neuro-cognitive feedback in enhancing autonomous driving systems.