CORR:合作的道路声誉

Baik Hoh, Seyhan Uçar, Pratham Oza, Chinmaya Patnayak, K. Oguchi
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引用次数: 4

摘要

汽车越来越多地配备了传感器和驾驶员辅助系统。然而,无论是这些技术进步还是传统的交通执法系统,都不足以保护通勤者免受行为不端的司机的伤害,比如咄咄逼人、心烦意乱和醉酒的司机。这就是为什么近年来尽管有了这些技术进步,我们在道路安全和驾驶体验方面没有看到任何实质性的改善。受到声誉系统成功的激励(例如,HMD、eBay和Wikipedia),我们提出了协同道路声誉(CORR)系统的概念,并讨论了将CORR扩展到所有车辆时可能带来的好处和挑战。研究了如何识别异常驾驶行为,提出了一种协同异常检测方法,在这种方法中,附近的互联车辆协同发现异常驾驶行为。通过大量的仿真,我们证明在一定水平的联网车辆普及率下,CORR识别异常驾驶行为的准确率约为75%。
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CORR: Collaborative On-Road Reputation
Vehicles are getting more equipped with sensors and driver assistant systems. However, neither these technological advances nor traditional traffic enforcement systems are sufficient in protecting commuters from misbehaving drivers such as aggressive, distracted, and drunken drivers. That is why we have not observed any substantial improvement in road safety and driving experience in recent years despite those technological advances. Being motivated by the success of reputation systems (i.e., How’s My Driving (HMD), eBay, and Wikipedia), we present the concept of Collaborative On-Road Reputation (CORR) system and discuss the potential benefits and challenges ahead when we expand CORR to all vehicles. We focus on how to identify the anomalous driving behavior and propose a cooperative anomaly detection method where nearby connected vehicles collaborate to surface the anomalous driving behavior. Through extensive simulations, we demonstrate that CORR can identify the anomalous driving behavior by about 75% accuracy under a certain level of connected vehicle penetration rates.
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