Understanding human driving behaviors quantitatively is critical even in the era when connected and autonomous vehicles and smart infrastructure are becoming ever more prevalent. This is particularly so as that mixed traffic settings, where autonomous vehicles and human driven vehicles co-exist, are expected to persist for quite some time. Towards this end it is necessary that we have a comprehensive modeling framework for decision-making within which human driving preferences can be inferred statistically from observed driving behaviors in realistic and naturalistic traffic settings. Leveraging a recently proposed computational framework for smart vehicles in a smart world using multi-agent based simulation and optimization, we first recapitulate how the forward problem of driving decision-making is modeled as a state space model. We then show how the model can be inverted to estimate driver preferences from naturalistic traffic data using the standard Kalman filter technique. We explicitly illustrate our approach using the vehicle trajectory data from Sugiyama experiment that was originally meant to demonstrate how stop-and-go shockwave can arise spontaneously without bottlenecks. Not only the estimated state filter can fit the observed data well for each individual vehicle, the inferred utility functions can also re-produce quantitatively similar pattern of the observed collective behaviors. One distinct advantage of our approach is the drastically reduced computational burden. This is possible because our forward model treats driving decision process, which is intrinsically dynamic with multi-agent interactions, as a sequence of independent static optimization problems contingent on the state with a finite look ahead anticipation. Consequently we can practically sidestep solving an interacting dynamic inversion problem that would have been much more computationally demanding.

中文翻译：

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使用自然交通数据校准人类驾驶行为和偏好

即使在互联自动驾驶汽车和智能基础设施日益普及的时代，定量地了解人类驾驶行为也至关重要。尤其是在自动驾驶汽车和人类驾驶汽车并存的混合交通环境下，预计将持续相当长的一段时间。为此，我们必须有一个全面的决策建模框架，在该框架内，可以从现实和自然交通环境中观察到的驾驶行为统计地推断出人类的驾驶偏好。利用最近提出的使用基于多智能体的仿真和优化的智能世界中智能汽车的计算框架，我们首先总结一下如何将驾驶决策的正向问题建模为状态空间模型。然后，我们展示了如何使用标准的卡尔曼滤波技术将模型反演以从自然交通数据中估算驾驶员的偏好。我们使用杉山实验的车辆轨迹数据明确说明了我们的方法，该数据最初旨在证明停止走走的冲击波如何自发产生而不会出现瓶颈。不仅估计状态过滤器可以很好地拟合每个车辆的观测数据，而且推断的效用函数还可以在数量上重现观测到的集体行为的相似模式。我们的方法的一个明显优势是大大减少了计算负担。这是有可能的，因为我们的前向模型会处理驾驶决策过程，该过程在多智能体交互中本质上是动态的，一系列独立的静态优化问题取决于状态，并具有有限的前瞻性预期。因此，我们实际上可以回避解决一个相互作用的动态反演问题，该问题在计算上会更加苛刻。