Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CAVS51000.2020.9334679 | 2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) |
Keywords | DocType | ISBN |
cooperative anomaly detection,connected vehicle collaboration,traffic enforcement systems,sensors,collaborative on-road reputation,connected vehicle penetration rates,anomalous driving behavior,driving experience,road safety,driver assistant systems,CORR | Conference | 978-1-7281-9002-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baik Hoh | 1 | 0 | 0.68 |
| Seyhan Ucar | 2 | 0 | 1.35 |
| Pratham Oza | 3 | 0 | 1.01 |
| Chinmaya Patnayak | 4 | 0 | 0.34 |
| Kentaro Oguchi | 5 | 0 | 3.38 |