There is evidence that the driving style of an autonomous vehicle is
important to increase the acceptance and trust of the passengers. The driving
situation has been found to have a significant influence on human driving
behavior. However, current driving style models only partially incorporate
driving environment information, limiting the alignment between an agent and
the given situation. Therefore, we propose a situation-aware driving style
model based on different visual feature encoders pretrained on fleet data, as
well as driving behavior predictors, which are adapted to the driving style of
a specific driver. Our experiments show that the proposed method outperforms
static driving styles significantly and forms plausible situation clusters.
Furthermore, we found that feature encoders pretrained on our dataset lead to
more precise driving behavior modeling. In contrast, feature encoders
pretrained supervised and unsupervised on different data sources lead to more
specific situation clusters, which can be utilized to constrain and control the
driving style adaptation for specific situations. Moreover, in a real-world
setting, where driving style adaptation is happening iteratively, we found the
MLP-based behavior predictors achieve good performance initially but suffer
from catastrophic forgetting. In contrast, behavior predictors based on
situationdependent statistics can learn iteratively from continuous data
streams by design. Overall, our experiments show that important information for
driving behavior prediction is contained within the visual feature encoder. The
dataset is publicly available at
huggingface.co/datasets/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation.

基于不同的视觉特征编码器和驾驶行为预测器，我们提出了一种情境感知的驾驶风格模型，可以适应特定司机的驾驶风格，并且在驾驶行为预测中取得了显著的性能优势。

司机为中心的驾驶风格适应中的情境认知

Situation Awareness for Driver-Centric Driving Style Adaptation

The driving style of an Autonomous Vehicle (AV) refers to how it behaves and
interacts with other AVs. In a multi-vehicle autonomous driving system, an AV
capable of identifying the driving styles of its nearby AVs can reliably
evaluate the risk of collisions and make more reasonable driving decisions.
However, there has not been a consistent definition of driving styles for an AV
in the literature, although it is considered that the driving style is encoded
in the AV's trajectories and can be identified using Maximum Entropy Inverse
Reinforcement Learning (ME-IRL) methods as a cost function. Nevertheless, an
important indicator of the driving style, i.e., how an AV reacts to its nearby
AVs, is not fully incorporated in the feature design of previous ME-IRL
methods. In this paper, we describe the driving style as a cost function of a
series of weighted features. We design additional novel features to capture the
AV's reaction-aware characteristics. Then, we identify the driving styles from
the demonstration trajectories generated by the Stochastic Model Predictive
Control (SMPC) using a modified ME-IRL method with our newly proposed features.
The proposed method is validated using MATLAB simulation and an off-the-shelf
experiment.

自动驾驶车辆中的驾驶风格通过以加权特征描述并捕捉车辆的反应感知特征，使用改进的最大熵逆强化学习方法从演示轨迹中识别驾驶风格，并通过 MATLAB 模拟和现有实验进行验证。

通过逆强化学习识别基于随机模型预测控制车辆的反应感知驾驶风格

Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive  Controlled Vehicles by Inverse Reinforcement Learning

Personalization of autonomous vehicles (AV) may significantly increase trust,
use, and acceptance. In particular, we hypothesize that the similarity of an
AV's driving style compared to the end-user's driving style will have a major
impact on end-user's willingness to use the AV. To investigate the impact of
driving style on user acceptance, we 1) develop a data-driven approach to
personalize driving style and 2) demonstrate that personalization significantly
impacts attitudes towards AVs. Our approach learns a high-level model that
tunes low-level controllers to ensure safe and personalized control of the AV.
The key to our approach is learning an informative, personalized embedding that
represents a user's driving style. Our framework is capable of calibrating the
level of aggression so as to optimize driving style based upon driver
preference. Across two human subject studies (n = 54), we first demonstrate our
approach mimics the driving styles of end-users and can tune attributes of
style (e.g., aggressiveness). Second, we investigate the factors (e.g., trust,
personality etc.) that impact homophily, i.e. an individual's preference for a
driving style similar to their own. We find that our approach generates driving
styles consistent with end-user styles (p<.001) and participants rate our
approach as more similar to their level of aggressiveness (p=.002). We find
that personality (p<.001), perceived similarity (p<.001), and high-velocity
driving style (p=.0031) significantly modulate the effect of homophily.

利用数据驱动的方法调整控制器以确保 AV 的安全和个性化控制，进而提高用户的信任度和接受度，我们研究了驾驶风格对用户接受度的影响，并证明了个性化能显著影响 AV 的态度。

MAVERIC: 以数据驱动的个性化自动驾驶方法

MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving

With progress in enabling autonomous cars to drive safely on the road, it is
time to start asking how they should be driving. A common answer is that they
should be adopting their users' driving style. This makes the assumption that
users want their autonomous cars to drive like they drive - aggressive drivers
want aggressive cars, defensive drivers want defensive cars. In this paper, we
put that assumption to the test. We find that users tend to prefer a
significantly more defensive driving style than their own. Interestingly, they
prefer the style they think is their own, even though their actual driving
style tends to be more aggressive. We also find that preferences do depend on
the specific driving scenario, opening the door for new ways of learning
driving style preference.

本研究测试了用户对自动驾驶汽车采用何种驾驶风格的偏好，并发现用户倾向于更为保守的驾驶风格。研究结果表明，用户对于所处的具体驾驶情境有不同的偏好，为学习个性化驾驶风格提供了新的契机。