Siebinga Olger, Zgonnikov Arkady, Abbink David A
Mechanical Engineering - Cognitive Robotics, TU Delft, Delft, CD 2628, The Netherlands.
PNAS Nexus. 2024 Sep 24;3(10):pgae420. doi: 10.1093/pnasnexus/pgae420. eCollection 2024 Oct.
Safe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions. These interactions entail complex dynamic joint behaviors composed of individual driver contributions in terms of high-level decisions, safety margins, and low-level control inputs. Existing driver models typically focus on one of these aspects, limiting our understanding of the underlying principles of traffic interactions. Here, we present a Communication-Enabled Interaction model based on risk perception, that does not assume humans are rational and explicitly accounts for communication between drivers. Our model can explain and reproduce observed human interactions in a simplified merging scenario on all three levels. Thereby improving our understanding of the underlying mechanisms of human traffic interactions and posing a step towards interaction-aware automated driving.
与人类驾驶车辆进行安全且符合社会规范的交互是自动驾驶中的一项重大挑战。深入理解此类交通交互的潜在原理有助于应对这一挑战。特别是，准确的驾驶员模型可用于在交互过程中为自动驾驶车辆提供信息。这些交互涉及复杂的动态联合行为，这些行为由驾驶员在高级决策、安全裕度和低级控制输入方面的个体贡献组成。现有的驾驶员模型通常只关注这些方面中的一个，限制了我们对交通交互潜在原理的理解。在此，我们提出一种基于风险感知的支持通信的交互模型，该模型不假定人类是理性的，并且明确考虑了驾驶员之间的通信。我们的模型可以在简化的合并场景中在所有三个层面上解释和重现观察到的人类交互。从而增进我们对人类交通交互潜在机制的理解，并朝着具备交互感知能力的自动驾驶迈出一步。
