Car following (CF) models are fundamental to describing traffic dynamics.
However, the CF behavior of human drivers is highly stochastic and nonlinear.
As a result, identifying the best CF model has been challenging and
controversial despite decades of research. Introduction of automated vehicles
has further complicated this matter as their CF controllers remain proprietary,
though their behavior appears different than human drivers. This paper develops
a stochastic learning approach to integrate multiple CF models, rather than
relying on a single model. The framework is based on approximate Bayesian
computation that probabilistically concatenates a pool of CF models based on
their relative likelihood of describing observed behavior. The approach, while
data-driven, retains physical tractability and interpretability. Evaluation
results using two datasets show that the proposed approach can better reproduce
vehicle trajectories for both human driven and automated vehicles than any
single CF model considered.

通过随机学习方法结合多个车辆跟随模型，该论文研究了交通动力学和自动驾驶车辆的行为，证明该方法可以更好地再现人类驾驶和自动驾驶的车辆轨迹。

基于近似贝叶斯推断的通用随机混合车辆跟驰模型

A Generic Stochastic Hybrid Car-following Model Based on Approximate  Bayesian Computation

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。