Siebinga O, Zgonnikov A, Abbink D A
Department of Cognitive Robotics, Delft University of Technology Mekelweg 2, Delft, The Netherlands.
R Soc Open Sci. 2023 May 24;10(5):230537. doi: 10.1098/rsos.230537. eCollection 2023 May.
A major challenge for autonomous vehicles is handling interactions with human-driven vehicles-for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver's behaviours. We demonstrate our model's potential in a simplified merging scenario of two vehicles, 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.
自动驾驶车辆面临的一个主要挑战是处理与人类驾驶车辆的交互，例如在高速公路上合并车道时。对人类交互行为有更好的理解和计算建模有助于应对这一挑战。然而，现有的建模方法主要忽略了驾驶员之间的通信，并假设交互中一个建模的驾驶员对另一个驾驶员做出反应，但不会积极影响他们的行为。在这里，我们认为解决这两个限制对于准确建模交互至关重要。我们提出了一个解决这些限制的新计算框架。与博弈论方法类似，我们对一个联合交互系统进行建模，而不是对一个只对其环境做出反应的孤立驾驶员进行建模。与博弈论相反，我们的框架明确纳入了两个驾驶员之间的通信以及每个驾驶员行为中的有限理性。我们在两辆车的简化合并场景中展示了我们模型的潜力，说明它产生了合理的交互行为（例如激进和保守的合并）。此外，在跟车场景中，类似人类的保持车距行为直接从风险感知中出现，而在模型决策中没有明确实施时间或距离间隔。这些结果表明，我们的框架是一种有前途的交互建模方法，可以支持具有交互感知能力的自动驾驶车辆的开发。
