Driver process models play a central role in the testing, verification, and
development of automated and autonomous vehicle technologies. Prior models
developed from control theory and physics-based rules are limited in automated
vehicle applications due to their restricted behavioral repertoire. Data-driven
machine learning models are more capable than rule-based models but are limited
by the need for large training datasets and their lack of interpretability,
i.e., an understandable link between input data and output behaviors. We
propose a novel car following modeling approach using active inference, which
has comparable behavioral flexibility to data-driven models while maintaining
interpretability. We assessed the proposed model, the Active Inference Driving
Agent (AIDA), through a benchmark analysis against the rule-based Intelligent
Driver Model, and two neural network Behavior Cloning models. The models were
trained and tested on a real-world driving dataset using a consistent process.
The testing results showed that the AIDA predicted driving controls
significantly better than the rule-based Intelligent Driver Model and had
similar accuracy to the data-driven neural network models in three out of four
evaluations. Subsequent interpretability analyses illustrated that the AIDA's
learned distributions were consistent with driver behavior theory and that
visualizations of the distributions could be used to directly comprehend the
model's decision making process and correct model errors attributable to
limited training data. The results indicate that the AIDA is a promising
alternative to black-box data-driven models and suggest a need for further
research focused on modeling driving style and model training with more diverse
datasets.

该研究提出了使用主动推理的一种新型车辆跟随建模方法，该方法具有与数据驱动的模型相当的行为灵活性，同时保持可解释性。通过基准分析，该模型被证明可以显着地预测驾驶控制，并表明需要进一步研究以建立行驶风格并利用更多样化的数据集进行模型训练。

一种车辆跟随的主动推断模型：优势和应用

An active inference model of car following: Advantages and applications

Animal brains evolved to optimize behavior in dynamically changing
environments, selecting actions that maximize future rewards. A large body of
experimental work indicates that such optimization changes the wiring of neural
circuits, appropriately mapping environmental input onto behavioral outputs. A
major unsolved scientific question is how optimal wiring adjustments, which
must target the connections responsible for rewards, can be accomplished when
the relation between sensory inputs, action taken, environmental context with
rewards is ambiguous. The computational problem of properly targeting cues,
contexts and actions that lead to reward is known as structural, contextual and
temporal credit assignment respectively. In this review, we survey prior
approaches to these three types of problems and advance the notion that the
brain's specialized neural architectures provide efficient solutions. Within
this framework, the thalamus with its cortical and basal ganglia interactions
serve as a systems-level solution to credit assignment. Specifically, we
propose that thalamocortical interaction is the locus of meta-learning where
the thalamus provides cortical control functions that parametrize the cortical
activity association space. By selecting among these control functions, the
basal ganglia hierarchically guide thalamocortical plasticity across two
timescales to enable meta-learning. The faster timescale establishes contextual
associations to enable rapid behavioral flexibility while the slower one
enables generalization to new contexts. Incorporating different thalamic
control functions under this framework clarifies how thalamocortical-basal
ganglia interactions may simultaneously solve the three credit assignment
problems.

本综述研究了动物大脑进化的过程和神经电路的构建，以及如何解决结构、内容和时间信用分配的计算问题，概括了指出丘脑在元学习中发挥着关键作用，同时提出丘脑的皮层和基底节相互作用可以同时解决三个信用分配问题的观点。