In pursuit of autonomous vehicles, achieving human-like driving behavior is vital. This study introduces adaptive autopilot (AA), a unique framework utilizing constrained-deep reinforcement learning (C-DRL). AA aims to safely emulate human driving to reduce the necessity for driver intervention. Focusing on the car-following scenario, the process involves (i) extracting data from the highD natural driving study and categorizing it into three driving styles using a rule-based classifier; (ii) employing deep neural network (DNN) regressors to predict human-like acceleration across styles; and (iii) using C-DRL, specifically the soft actor-critic Lagrangian technique, to learn human-like safe driving policies. Results indicate effectiveness in each step, with the rule-based classifier distinguishing driving styles, the regressor model accurately predicting acceleration, outperforming traditional car-following models, and C-DRL agents learning optimal policies for humanlike driving across styles.

翻译：在自动驾驶领域，实现类人驾驶行为至关重要。本研究提出了自适应自动驾驶（AA）框架，该框架采用约束深度强化学习（C-DRL）技术。AA旨在安全地模拟人类驾驶，以减少对驾驶员干预的需求。研究聚焦于跟车场景，具体流程包括：（i）从highD自然驾驶研究中提取数据，并使用基于规则的分类器将其划分为三种驾驶风格；（ii）采用深度神经网络（DNN）回归器预测不同风格下的类人加速度；（iii）利用C-DRL，特别是软演员-评论家拉格朗日方法，学习类人安全驾驶策略。结果表明，各步骤均取得良好效果：基于规则的分类器能有效区分驾驶风格；回归模型能准确预测加速度，其性能优于传统跟车模型；C-DRL智能体成功学习了跨风格的类人最优驾驶策略。