用高斯过程建模驾驶员在无名路口的自然驾驶反馈行为。
Characterizing Human Feedback-Based Control in Naturalistic Driving Interactions via Gaussian Process Regression with Linear Feedback
- 基于高斯过程回归，将驾驶员行为建模为状态依赖的反馈控制器。
- 通过加权线性和非线性先验计算反馈增益，揭示行为差异。
- 发现不同驾驶员群体的控制策略存在显著差异，适用于自动驾驶社交化设计。
理解驾驶员交互对设计能与人类驾驶车辆安全协作的自动驾驶汽车至关重要。本文研究了驾驶员在驾驶模拟器中通过无名交叉口时的决策与行为数据，利用这些自然主义数据，采用高斯过程回归方法学习驾驶员的状态反馈控制策略。通过线性和非线性先验的加权组合计算控制器的反馈增益，并分析各增益如何反映在实际驾驶行为中。同时，评估了不同驾驶员群体间控制策略的差异。本研究的数据驱动分析有助于未来社会响应式自动驾驶系统的开发。
原文摘要 · Abstract (English)
Understanding driver interactions is critical to designing autonomous vehicles to interoperate safely with human-driven cars. We consider the impact of these interactions on the policies drivers employ when navigating unsigned intersections in a driving simulator. The simulator allows the collection of naturalistic decision-making and behavior data in a controlled environment. Using these data, we model the human driver responses as state-based feedback controllers learned via Gaussian Process regression methods. We compute the feedback gain of the controller using a weighted combination of linear and nonlinear priors. We then analyze how the individual gains are reflected in driver behavior. We also assess differences in these controllers across populations of drivers. Our work in data-driven analyses of how drivers determine their policies can facilitate future work in the design of socially responsive autonomy for vehicles.
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