A major challenge for autonomous vehicles is handling interactive scenarios, such as highway merging, with human-driven vehicles. A better understanding of human interactive behaviour could help address this challenge. Such understanding could be obtained through modelling human behaviour. However, existing modelling approaches predominantly neglect communication between drivers and assume that some drivers in the interaction only respond to others, but do not actively influence them. Here we argue that addressing these two limitations is crucial for accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model the interaction in an integral way rather than modelling an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication and bounded rationality. We demonstrate the model in a simplified merging scenario, illustrating that it generates plausible interactive behaviour (e.g., aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

翻译：自动驾驶汽车面临的主要挑战之一，是与人类驾驶车辆处理诸如高速公路汇流等交互场景。对人类交互行为的更深入理解有助于应对这一挑战。这种理解可通过人类行为建模来获取。然而，现有建模方法普遍忽略了驾驶员之间的通信，并假设交互中的部分驾驶员仅对他人的行为作出反应，而非主动影响他人。本文论证，解决这两个局限性对于精准建模交互至关重要。我们提出了一种新的计算框架，以解决这些局限。与博弈论方法类似，我们以整体方式对交互进行建模，而非仅对孤立地、仅响应环境的驾驶员进行建模。与博弈论不同，我们的框架明确纳入了通信与有限理性。我们在简化汇流场景中演示了该模型，结果表明它能生成合理的交互行为（例如，激进与保守的汇流）。此外，在跟车场景中，无需在模型决策中显式实现时间或距离间隔，仅通过风险感知就涌现了类人的跟车间距保持行为。这些结果表明，我们的框架是一种有前景的交互建模方法，可支持发展具交互感知能力的自动驾驶车辆。