增强型情感驱动认知代理基于后方碰撞避免控制器用于自动驾驶车辆
Enhanced Emotion Enabled Cognitive Agent Based Rear End Collision Avoidance Controller for Autonomous Vehicles
AI总结：
本文提出一种基于增强型情感驱动认知代理的后方碰撞避免控制器，通过引入恐惧情绪生成机制，提高自动驾驶车辆的碰撞避免效率和规则数量。
摘要：
后方碰撞是自然中最致命的事故，导致大多数交通伤亡和伤害。现有研究提出了许多后方碰撞避免解决方案，但这些方案高度依赖精确的数学模型。然而，实际道路驾驶受非线性因素如路面状况、驾驶员反应时间、行人流量和车辆动力学影响，因此获得车辆控制系统精确数学模型具有挑战性。这个问题通过模糊逻辑解决了，但过多的模糊规则直接影响其效率。此外，这些基于模糊逻辑的控制器未使用适当的代理建模来模拟人工驾驶员执行这些模糊规则的功能。鉴于这些限制，我们提出了一种增强型情感驱动认知代理（EEEC_Agent）控制器，帮助自动驾驶车辆（AVs）以较少的规则进行后方碰撞避免，设计基于恐惧情绪，并具有高效率。为了在EEEC_Agent中引入恐惧情绪生成机制，采用了Orton, Clore & Collins（OCC）模型。EEEC_Agent的恐惧生成机制通过NetLogo模拟验证。此外，通过特别设计的原型AV平台对EEEC_Agent的功能进行了实际验证。最终，与现有最先进研究的定性比较研究表明，所提出的模型优于近期研究。
英文摘要：
Rear end collisions are deadliest in nature and cause most of traffic casualties and injuries. In the existing research, many rear end collision avoidance solutions have been proposed. However, the problem with these proposed solutions is that they are highly dependent on precise mathematical models. Whereas, the real road driving is influenced by non-linear factors such as road surface situations, driver reaction time, pedestrian flow and vehicle dynamics, hence obtaining the accurate mathematical model of the vehicle control system is challenging. This problem with precise control based rear end collision avoidance schemes has been addressed using fuzzy logic, but the excessive number of fuzzy rules straightforwardly prejudice their efficiency. Furthermore, these fuzzy logic based controllers have been proposed without using proper agent based modeling that helps in mimicking the functions of an artificial human driver executing these fuzzy rules. Keeping in view these limitations, we have proposed an Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent) based controller that helps the Autonomous Vehicles (AVs) to perform rear end collision avoidance with less number of rules, designed after fear emotion, and high efficiency. To introduce a fear emotion generation mechanism in EEEC_Agent, Orton, Clore & Collins (OCC) model has been employed. The fear generation mechanism of EEEC_Agent has been verified using NetLogo simulation. Furthermore, practical validation of EEEC_Agent functions has been performed using specially built prototype AV platform. Eventually, the qualitative comparative study with existing state of the art research works reflect that proposed model outperforms recent research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
