This paper presents a white-box intention-aware decision-making for the handling of interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street crossing scenario. Moreover, a design framework has been developed, which enables automated parameterization of the decision-making. This decision-making is designed in such a manner that it can understand pedestrians in urban traffic and can react accordingly to their intentions. That way, a human-like response to the actions of the pedestrian is ensured, leading to a higher acceptance of AVs. The core notion of this paper is that the intention prediction of the pedestrian to cross the street and decision-making are divided into two subsystems. On the one hand, the intention detection is a data-driven, black-box model. Thus, it can model the complex behavior of the pedestrians. On the other hand, the decision-making is a white-box model to ensure traceability and to enable a rapid verification and validation of AVs. This white-box decision-making provides human-like behavior and a guaranteed prevention of deadlocks. An additional benefit is that the proposed decision-making requires low computational resources only enabling real world usage. The automated parameterization uses a particle swarm optimization and compares two different models of the pedestrian: The social force model and the Markov decision process model. Consequently, a rapid design of the decision-making is possible and different pedestrian behaviors can be taken into account. The results reinforce the applicability of the proposed intention-aware decision-making.

翻译：本文提出了一种白盒意图感知决策方法，用于处理无信号灯街道过街场景中行人与自动驾驶车辆（AV）之间的交互。此外，还开发了一个设计框架，能够实现决策过程的自动参数化。该决策机制的设计使其能够理解城市交通中的行人，并针对其意图做出相应反应。由此确保了对行人行为的类人响应，从而提高了AV的接受度。本文的核心观点是：行人过街意图预测与决策制定被划分为两个子系统。一方面，意图检测采用数据驱动的黑盒模型，从而能够模拟行人的复杂行为；另一方面，决策制定采用白盒模型，以确保可追溯性并实现AV的快速验证与确认。这种白盒决策机制提供了类人行为，并保证了死锁的预防。另一个优势在于，所提出的决策机制仅需较低的计算资源，从而支持实际应用。自动参数化过程采用粒子群优化算法，并比较了两种行人模型：社会力模型与马尔可夫决策过程模型。因此，能够实现决策机制的快速设计，并考虑不同的行人行为。结果验证了所提出的意图感知决策的适用性。