Classification of Fatigued and Drunk Driving Based on Decision Tree Methods
A Simulator Study
Bibliographic Data
| ID | 15464245 |
|---|---|
| Authors | Ying Yao (0000-0001-7619-3898, Beijing University of Technology), Xiaohua Zhao (0000-0002-6383-0939, Beijing University of Technology, corresponding author), Hongji Du (Baidu (China)), Yunlong Zhang (0000-0002-6162-8307, Texas A&M University), Guohui Zhang (0000-0001-5194-9222, University of Hawaiʻi at Mānoa), Jian Rong (0000-0002-3960-1669, Beijing University of Technology) |
| Year | 2019 |
| Volume | 16 |
| Issue | 11 |
| Pages | 1935-1935 |
| Publication date | 2019-05-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph16111935 |
| PMID | 31159221 |
| OpenAlex | W2946906913 |
| Language | EN |
| Citations received | 1 |
| References cited | 37 |
It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of "alert" and "abnormal" driving was 90.9%, and that of "fatigued" and "drunk" driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states
Benchmark (surveying · Blood alcohol · Decision tree · Decision tree learning · Driving simulator · Driving under the influence · Drunk driving · Injury prevention · Medical emergency · Poison control · Simulation · Statistics · Computer Science · Mathematics · Medicine · Sleep and Work-Related Fatigue · Traffic and Road Safety · Vehicle emissions and performance · Artificial Intelligence
| Citations per year | 0.2 |
|---|---|
| Citation span | 2021 - 2021 (1) |
| Highly cited | No |