For automated vehicles (AVs) to reliably navigate through crosswalks, they need to understand pedestrians crossing behaviors. Simple and reliable pedestrian behavior models aid in real-time AV control by allowing the AVs to predict future pedestrian behaviors. In this paper, we present a Behavior aware Model Predictive Controller (B-MPC) for AVs that incorporates long-term predictions of pedestrian crossing behavior using a previously developed pedestrian crossing model. The model incorporates pedestrians gap acceptance behavior and utilizes minimal pedestrian information, namely their position and speed, to predict pedestrians crossing behaviors. The BMPC controller is validated through simulations and compared to a rule-based controller. By incorporating predictions of pedestrian behavior, the B-MPC controller is able to efficiently plan for longer horizons and handle a wider range of pedestrian interaction scenarios than the rule-based controller. Results demonstrate the applicability of the controller for safe and efficient navigation at crossing scenarios.

中文翻译：

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使用最小信息行人预测模型在人行横道上对自动车辆进行有效的行为感知控制

为了让自动驾驶汽车 (AV) 可靠地通过人行横道，它们需要了解行人的横穿行为。简单可靠的行人行为模型允许自动驾驶汽车预测未来的行人行为，从而有助于实时自动驾驶汽车控制。在本文中，我们提出了一种用于 AV 的行为感知模型预测控制器 (B-MPC)，该控制器使用先前开发的行人过路模型结合了行人过路行为的长期预测。该模型结合了行人间隙接受行为，并利用最少的行人信息（即他们的位置和速度）来预测行人过马路的行为。BMPC 控制器通过仿真进行验证，并与基于规则的控制器进行比较。通过结合行人行为的预测，与基于规则的控制器相比，B-MPC 控制器能够有效地规划更长的视野并处理更广泛的行人交互场景。结果证明了控制器在穿越场景中安全高效导航的适用性。