[论文解读] Designing Autonomous Vehicles: Evaluating the Role of Human Emotions and Social Norms

Faisal Riaz, Muaz A. Niazi|arXiv (Cornell University)|Aug 6, 2017

Ethics and Social Impacts of AI参考文献 5被引用 3

一句话总结

本文提出了一种基于情绪驱动决策的自动驾驶汽车（AV）社会规范合规机制，特别聚焦于源自OCC模型的恐惧情绪，并通过模糊逻辑进行量化。在NetLogo中使用SimConnect进行模拟，该人工社会中的AV在碰撞规避方面优于随机游走基线模型，表明基于情绪的社会规范可提升AV行为的安全性与社会可接受性。

ABSTRACT

Humans are going to delegate the rights of driving to the autonomous vehicles in near future. However, to fulfill this complicated task, there is a need for a mechanism, which enforces the autonomous vehicles to obey the road and social rules that have been practiced by well-behaved drivers. This task can be achieved by introducing social norms compliance mechanism in the autonomous vehicles. This research paper is proposing an artificial society of autonomous vehicles as an analogy of human social society. Each AV has been assigned a social personality having different social influence. Social norms have been introduced which help the AVs in making the decisions, influenced by emotions, regarding road collision avoidance. Furthermore, social norms compliance mechanism, by artificial social AVs, has been proposed using prospect based emotion i.e. fear, which is conceived from OCC model. Fuzzy logic has been employed to compute the emotions quantitatively. Then, using SimConnect approach, fuzzy values of fear has been provided to the Netlogo simulation environment to simulate artificial society of AVs. Extensive testing has been performed using the behavior space tool to find out the performance of the proposed approach in terms of the number of collisions. For comparison, the random-walk model based artificial society of AVs has been proposed as well. A comparative study with a random walk, prove that proposed approach provides a better option to tailor the autopilots of future AVS, Which will be more socially acceptable and trustworthy by their riders in terms of safe road travel.

研究动机与目标

• 为通过整合类人情绪与规范行为来解决自动驾驶汽车的社会可接受性挑战。
• 建模情绪（尤其是恐惧）如何指导AV在碰撞规避场景中的决策。
• 开发一种社会规范合规机制，使AV能够模拟具有社会责任感的驾驶行为。
• 评估基于情绪的AV是否在安全性与可靠性方面优于非情绪化、基于随机游走的AV。

提出的方法

• 作者将AV建模为具有独特社会人格与情绪状态的智能体，使用OCC模型定义恐惧为基于前景的情绪。
• 应用模糊逻辑，基于交通与环境状况定量计算恐惧的强度。
• 使用SimConnect将模糊情绪值与NetLogo模拟环境接口，以驱动AV行为。
• 实施社会规范合规机制，AV根据情绪状态与社会规则调整行为以避免碰撞。
• 使用NetLogo的BehaviorSpace工具对人工社会进行测试，评估多次模拟运行中的碰撞率。
• 采用随机游走模型作为基线，代表非情绪化、非规范化的AV行为。

实验结果

研究问题

• RQ1 如何建模并整合人类情绪（如恐惧）至自动驾驶汽车决策中，以实现更安全的驾驶？
• RQ2 与非情绪化AV相比，基于情绪的AV在多大程度上降低了碰撞率？
• RQ3 由情绪状态驱动的社会规范合规是否能提升自动驾驶汽车的社会可接受性与可信度？
• RQ4 模糊逻辑的整合如何增强AV中情绪反应的量化与应用？

主要发现

• 所提出的基于情绪的AV系统相较于随机游走基线模型，显著降低了碰撞率。
• 模糊逻辑的应用有效实现了恐惧的量化，使AV能够作出细致且情境敏感的响应。
• 由情绪状态驱动的社会规范合规使AV人工社会展现出更优的安全性能。
• 模拟结果证实，由情绪与社会规范引导的AV比采用随机行为的AV更具可靠性与可信度。