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.

通过自适应自动驾驶系统（AA）和约束深度强化学习（C-DRL）实现了模拟人类驾驶行为的独特框架，从而减少驾驶员干预的需求。研究以车辆跟随场景为重点，通过规则分类器提取自然驾驶数据并将其分为三种驾驶风格，采用深度神经网络（DNN）回归器预测跟随风格下的人类加速度，并使用 C-DRL 中的软行动者 - 评论家拉格朗日技术来学习人类安全驾驶策略，实验结果表明每个步骤的有效性，规则分类器能够区分驾驶风格，回归模型准确预测加速度，优于传统车辆跟随模型，并且 C-DRL 代理能够学习到适用于不同驾驶风格的最优驾驶策略。

自适应自动驾驶：多样驾驶行为的约束强化学习

Adaptive Autopilot: Constrained DRL for Diverse Driving Behaviors

Autonomous vehicles trained through Multi-Agent Reinforcement Learning (MARL)
have shown impressive results in many driving scenarios. However, the
performance of these trained policies can be impacted when faced with diverse
driving styles and personalities, particularly in highly interactive
situations. This is because conventional MARL algorithms usually operate under
the assumption of fully cooperative behavior among all agents and focus on
maximizing team rewards during training. To address this issue, we introduce
the Personality Modeling Network (PeMN), which includes a cooperation value
function and personality parameters to model the varied interactions in
high-interactive scenarios. The PeMN also enables the training of a background
traffic flow with diverse behaviors, thereby improving the performance and
generalization of the ego vehicle. Our extensive experimental studies, which
incorporate different personality parameters in high-interactive driving
scenarios, demonstrate that the personality parameters effectively model
diverse driving styles and that policies trained with PeMN demonstrate better
generalization compared to traditional MARL methods.

通过引入个性建模网络（PeMN），以及合作价值函数和个性参数，我们在高互动场景中模拟车辆间各种交互，并改进了自车的性能和泛化能力。实验证明，PeMN 可以有效地模拟多样化的驾驶风格，并且相比传统的多智能体强化学习方法，使用 PeMN 进行训练的策略具有更好的泛化性能。

高度互动自动驾驶场景中的多智能体强化学习模型多样驾驶行为

Learning to Model Diverse Driving Behaviors in Highly Interactive  Autonomous Driving Scenarios with Multi-Agent Reinforcement Learning

Identifying driving styles is the task of analyzing the behavior of drivers
in order to capture variations that will serve to discriminate different
drivers from each other. This task has become a prerequisite for a variety of
applications, including usage-based insurance, driver coaching, driver action
prediction, and even in designing autonomous vehicles; because driving style
encodes essential information needed by these applications. In this paper, we
present a deep-neural-network architecture, we term D-CRNN, for building
high-fidelity representations for driving style, that combine the power of
convolutional neural networks (CNN) and recurrent neural networks (RNN). Using
CNN, we capture semantic patterns of driver behavior from trajectories (such as
a turn or a braking event). We then find temporal dependencies between these
semantic patterns using RNN to encode driving style. We demonstrate the
effectiveness of these techniques for driver identification by learning driving
style through extensive experiments conducted on several large, real-world
datasets, and comparing the results with the state-of-the-art deep-learning and
non-deep-learning solutions. These experiments also demonstrate a useful
example of bias removal, by presenting how we preprocess the input data by
sampling dissimilar trajectories for each driver to prevent spatial
memorization. Finally, this paper presents an analysis of the contribution of
different attributes for driver identification; we find that engine RPM, Speed,
and Acceleration are the best combination of features.

本文提出了一种深度神经网络结构 D-CRNN，通过结合卷积神经网络（CNN）和循环神经网络（RNN）的优势，建立了高保真度的驾驶风格表示，通过几个大型实时数据库的广泛实验和与先进的深度学习和非深度学习解决方案的比较，证明了这些技术在驾驶员识别方面的有效性。