Plebe Alice, Rosati Papini Gastone Pietro, Cherubini Antonello, Da Lio Mauro
Department of Industrial Engineering, University of Trento, Trento, Italy.
Front Artif Intell. 2022 Aug 25;5:910801. doi: 10.3389/frai.2022.910801. eCollection 2022.
This paper focuses on the collaboration between human drivers and intelligent vehicles. We propose a collaboration mechanism grounded on the concept of distributed cognition. With distributed cognition, intelligence does not lie just in the single entity but also in the interaction with the other cognitive components in a system. We apply this idea to vehicle intelligence, proposing a system distributed into two cognitive entities-the human and the autonomous agent-that together contribute to drive the vehicle. This account of vehicle intelligence differs from the mainstream research effort on highly autonomous cars. The proposed mechanism follows one of the paradigm derived from distributed cognition, the metaphor: just like the rider communicates their intention to the horse through the reins, the human influences the agent using the pedals and the steering wheel. We use a driving simulator to demonstrate the collaboration in action, showing how the human can communicate and interact with the agent in various ways with safe outcomes.
本文聚焦于人类驾驶员与智能车辆之间的协作。我们提出了一种基于分布式认知概念的协作机制。借助分布式认知，智能不仅存在于单个实体中，还存在于系统中与其他认知组件的交互中。我们将这一理念应用于车辆智能，提出一个分为两个认知实体——人类和自主代理——的系统，它们共同助力驾驶车辆。这种对车辆智能的描述不同于关于高度自动驾驶汽车的主流研究工作。所提出的机制遵循源自分布式认知的一种范式，即隐喻：就像骑手通过缰绳向马传达意图一样，人类使用踏板和方向盘影响代理。我们使用驾驶模拟器来展示实际中的协作，展示人类如何能够以各种方式与代理进行通信和交互并获得安全结果。
