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.

翻译：驾驶员过程模型在自动驾驶与自主车辆技术的测试、验证及开发中发挥着核心作用。早期基于控制理论和物理规则开发的模型因行为库受限，在自动驾驶车辆应用中存在局限。数据驱动的机器学习模型较规则模型更具能力，但受限于对大规模训练数据集的需求及缺乏可解释性（即输入数据与输出行为之间可理解的关联）。我们提出了一种采用主动推理的新型跟驰建模方法，该方法兼具数据驱动模型的行为灵活性，同时保持可解释性。我们通过基准分析评估了所提出的主动推理驾驶主体（AIDA）模型，并将其与基于规则的智能驾驶员模型及两个神经网络行为克隆模型进行比较。采用一致流程在真实驾驶数据集上对模型进行训练与测试。测试结果表明，AIDA预测的驾驶控制效果显著优于基于规则的智能驾驶员模型，且在四项评估中有三项与数据驱动的神经网络模型精度相当。随后的可解释性分析显示，AIDA学习到的分布与驾驶员行为理论一致，且分布可视化可直接用于理解模型的决策过程，并纠正因训练数据不足导致的模型误差。研究结果表明，AIDA是黑箱数据驱动模型的有力替代方案，并提示需进一步研究驾驶风格建模及基于更多样化数据集的模型训练。