In the realm of driving technologies, fully autonomous vehicles have not been
widely adopted yet, making advanced driver assistance systems (ADAS) crucial
for enhancing driving experiences. Adaptive Cruise Control (ACC) emerges as a
pivotal component of ADAS. However, current ACC systems often employ fixed
settings, failing to intuitively capture drivers' social preferences and
leading to potential function disengagement. To overcome these limitations, we
propose the Editable Behavior Generation (EBG) model, a data-driven
car-following model that allows for adjusting driving discourtesy levels. The
framework integrates diverse courtesy calculation methods into long short-term
memory (LSTM) and Transformer architectures, offering a comprehensive approach
to capture nuanced driving dynamics. By integrating various discourtesy values
during the training process, our model generates realistic agent trajectories
with different levels of courtesy in car-following behavior. Experimental
results on the HighD and Waymo datasets showcase a reduction in Mean Squared
Error (MSE) of spacing and MSE of speed compared to baselines, establishing
style controllability. To the best of our knowledge, this work represents the
first data-driven car-following model capable of dynamically adjusting
discourtesy levels. Our model provides valuable insights for the development of
ACC systems that take into account drivers' social preferences.

我们提出了可编辑行为生成（EBG）模型，这是一种数据驱动的车辆跟随模型，允许调整驾驶不礼貌水平，该模型整合了多种礼貌计算方法，通过 LSTM 和 Transformer 架构提供了全面的方法来捕捉细微的驾驶动力学。该模型是第一个可以动态调整不礼貌水平的数据驱动车辆跟随模型，为考虑驾驶员社交偏好的 ACC 系统开发提供了有价值的见解。

EditFollower：可调节的车跟随模型用于可定制的自适应巡航控制系统

EditFollower: Tunable Car Following Models for Customizable Adaptive  Cruise Control Systems

Traffic simulation software is used by transportation researchers and
engineers to design and evaluate changes to roadways. These simulators are
driven by models of microscopic driver behavior from which macroscopic measures
like flow and congestion can be derived. Many models are designed for a subset
of possible traffic scenarios and roadway configurations, while others have no
explicit constraints on their application. Work zones (WZs) are one scenario
for which no model to date has reproduced realistic driving behavior. This
makes it difficult to optimize for safety and other metrics when designing a
WZ. The Federal Highway Administration commissioned the USDOT Volpe Center to
develop a car-following (CF) model for use in microscopic simulators that can
capture and reproduce driver behavior accurately within and outside of WZs.
Volpe also performed a naturalistic driving study to collect telematics data
from vehicles driven on roads with WZs for use in model calibration. During
model development, Volpe researchers observed difficulties in calibrating their
model, leaving them to question whether there existed flaws in their model, in
the data, or in the procedure used to calibrate the model using the data. In
this thesis, I use Bayesian methods for data analysis and parameter estimation
to explore and, where possible, address these questions. First, I use Bayesian
inference to measure the sufficiency of the size of the data set. Second, I
compare the procedure and results of the genetic algorithm based calibration
performed by the Volpe researchers with those of Bayesian calibration. Third, I
explore the benefits of modeling CF hierarchically. Finally, I apply what was
learned in the first three phases using an established CF model, Wiedemann 99,
to the probabilistic modeling of the Volpe model. Validation is performed using
information criteria as an estimate of predictive accuracy.

使用贝叶斯方法进行数据分析和参数估计，评估交通仿真软件中基于遗传算法的校准过程，研究车辆跟驰模型在模型校准中的层次化建模优势，并应用概率建模对车辆跟驰模型进行验证。

一种基于贝叶斯编程的有限数据下的车辆跟随模型校准和验证方法

A Bayesian Programming Approach to Car-following Model Calibration and  Validation using Limited Data

The car-following (CF) model is the core component for traffic simulations
and has been built-in in many production vehicles with Advanced Driving
Assistance Systems (ADAS). Research of CF behavior allows us to identify the
sources of different macro phenomena induced by the basic process of pairwise
vehicle interaction. The CF behavior and control model encompasses various
fields, such as traffic engineering, physics, cognitive science, machine
learning, and reinforcement learning. This paper provides a comprehensive
survey highlighting differences, complementarities, and overlaps among various
CF models according to their underlying logic and principles. We reviewed
representative algorithms, ranging from the theory-based kinematic models,
stimulus-response models, and cruise control models to data-driven Behavior
Cloning (BC) and Imitation Learning (IL) and outlined their strengths and
limitations. This review categorizes CF models that are conceptualized in
varying principles and summarize the vast literature with a holistic framework.

综述了车辆跟随模型在交通仿真与自动驾驶中的广泛应用及其不同调节算法之间的差异与共性，并归纳总结了该领域相关文献。

纵向车辆跟随模型述评

A Review on Longitudinal Car-Following Model

With an increasing number of vehicles equipped with adaptive cruise control
(ACC), the impact of such vehicles on the collective dynamics of traffic flow
becomes relevant. By means of simulation, we investigate the influence of
variable percentages of ACC vehicles on traffic flow characteristics. For
simulating the ACC vehicles, we propose a new car-following model that also
serves as basis of an ACC implementation in real cars. The model is based on
the Intelligent Driver Model [Treiber et al., Physical Review E 62, 1805
(2000)] and inherits its intuitive behavioural parameters: desired velocity,
acceleration, comfortable deceleration, and desired minimum time headway. It
eliminates, however, the sometimes unrealistic behaviour of the Intelligent
Driver Model in cut-in situations with ensuing small gaps that regularly are
caused by lane changes of other vehicles in dense or congested traffic. We
simulate the influence of different ACC strategies on the maximum capacity
before breakdown, and the (dynamic) bottleneck capacity after breakdown. With a
suitable strategy, we find sensitivities of the order of 0.3, i.e., 1% more ACC
vehicles will lead to an increase of the capacities by about 0.3%. This
sensitivity multiplies when considering travel times at actual breakdowns.

使用新的汽车跟随模型模拟自适应巡航控制的车辆对交通流的影响，发现不同的自适应巡航控制策略会显著地改变交通的最大容量和动态瓶颈容量。