Name
Abstract | ||
|---|---|---|
As the primary transportation in the urban traffic, bus plays a significant role in road safety. It would cause severe casualties if an accident occurred. To improve the safety of bus driving, we classify the specific types of latent abnormal driving behavior, which include sudden braking, lane changing casually, quick turn, fast U-turn and long time parking, and propose a method to identify the abnormal driving behavior of the bus. Firstly, we collect the acceleration, orientation and timestamp bus driving data through smartphone's accelerometer and orientation sensor. After de-nosing the collected data, we extract features in thirteen dimensions and train the naive Bayesian classifier, which is employed to detect and identify abnormal driving behaviors. The experimental results have been compared with support vector machine, and shows that the naive Bayesian classifier has an better performance than support vector machine on detecting and identifying various types of the abnormal bus driving behavior with the accuracy at 98.40%. |
Year | Venue | Keywords |
|---|---|---|
2018 | PROCEEDINGS OF 2018 TENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | naive Bayesian classifier, s upport v ector machine, abnormal driving behavior, data collection, feature extraction |
Field | DocType | Citations |
Data collection,Naive Bayes classifier,Pattern recognition,Accelerometer,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Acceleration,Timestamp,Naive bayesian classifier | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinrong Wu | 1 | 0 | 0.68 |
| Junwei Zhou | 2 | 118 | 16.64 |
| Jinghe An | 3 | 0 | 0.34 |
| Yanchao Yang | 4 | 13 | 6.14 |