The near-future deployment of high-level automation vehicles (
The term “driver fatigue” is defined as a state of reduced mental alertness [
Despite this public contribution, there remain ongoing challenges that should be addressed to prevent and reduce vehicle accidents. One of these challenges is to integrate drivers’ task-related fatigue into the risk management of traffic accidents on actual road sections. To achieve this, it is essential to monitor fatigued drivers on road sections. However, advanced technologies to monitor physiological and driving-performance measures have several limitations [
Fortunately, it is expected that automated vehicles (
There is an academic consensus that prolonged driving durations increase the level of drivers’ task-related fatigue [
Fortunately, detailed information about the operation of
To take advantage of this upcoming opportunity, a method that directly measures fatigued drivers on actual road sections using driving durations is proposed in this research. The method is developed based on the following two concepts. The first is that the distribution of the driving durations, collected from AV trajectory data, is a direct subset of the distribution of driving durations of all vehicles or is at least strongly related to this dataset in some way. This concept is also supported by the fact that vehicle GPS traffic volumes are direct subsets of the total vehicular traffic volume for a road section [
It is expected that the degree of fatigue felt by a driver in an automated vehicle will differ according to the automation level (i.e., 0–5), as defined in earlier work [
Contrary to drivers’ expectations, it was reported that drivers of future
To measure fatigued drivers related to manual and low-level automated driving, the ratio of potentially fatigued drivers to all drivers (henceforth,
Concept of
Let us define the nature of the driving duration as a frequency function of
Individual driving duration values are integrated into the frequency distribution of the driving duration (i.e.,
Once
A suitable
During the real-life computation process using the frequency of the driving duration with
To verify the potential of high-level
Test bed.
To compile the distribution of the driving durations, point-to-point vehicle trajectory big data were used, the number of points of which was as high as to 5.91
The distributions of the driving durations for the target road sections were extracted from the collected vehicle trajectory data. Figure
Variety of distributions of driving durations.
On the other hand, it can be seen that a significant percentage of drivers suffer from task-related fatigue considering a conservative safe limit for continued manual driving on a motorway. The
Wide variation in
In order to calculate
A numeric simulation that maximizes the coefficient of the correlation between
Effects of
The relationship between
Relationship between
The trend of
The effects of high-level
Summary of the effects of high-level
Reduction (%) | Penetration rate of high-level automation ( | |||||
---|---|---|---|---|---|---|
High ( | Ave. | 12.1 | 32.3 | 47.3 | 57.2 | 62.0 |
Max. | 13.3 | 35.6 | 52.3 | 63.3 | 68.5 | |
Min. | 10.5 | 28.2 | 41.3 | 49.9 | 54.0 | |
Middle (0.2 ≤ | Ave. | 6.4 | 17.1 | 25.0 | 30.1 | 32.5 |
Max. | 7.9 | 21.2 | 31.1 | 37.5 | 40.5 | |
Min. | 5.2 | 13.8 | 20.1 | 24.2 | 26.1 | |
Low ( | Ave. | 0.9 | 2.5 | 3.6 | 4.2 | 4.5 |
Max. | 3.1 | 8.4 | 12.3 | 14.7 | 15.8 | |
Min. | 0.1 | 0.3 | 0.4 | 0.5 | 0.5 |
Effects of
Effects of
These results are summarized in Table
In contrast, the reduction in
It should be noted that driving fatigue has a strong and complex relationship with multiple casual factors, including driving environments (e.g., visibility, road geometry, and traffic volume) related to roads and traffic flows [
It is expected that the upcoming introduction of highly automated vehicles on real-world roads will present promising opportunities to solve many ongoing hindrances in modern safety-related research. One of these challenges is the successful monitoring of fatigued drivers on actual road sections. Fortunately, vehicle trajectory big data can be collected through autonomous vehicles, which closely rely on advanced vehicle GPS and communication systems. The vehicle trajectory data include key information with which to monitor driver fatigue on road sections, particularly the driving durations from departure locations to any road segment.
To harness this opportunity, a new concept for the direct monitoring of fatigue drivers on any road section was introduced in this study. A data-driven method which directly surveys fatigued drivers on road segments was developed based on driving durations extracted from vehicle trajectory big data. The potential of high-level automation vehicles was demonstrated using the characteristics of the driving durations and real-life vehicle accident data. It was found that the ratio of potentially fatigued drivers to all drivers on any road section can be easily and effectively monitored through driving durations as included in vehicle trajectory big data. It was also discovered that the monitored degree of fatigued drivers has strong explanatory power with regard to traffic accidents. Therefore, it is expected that the proposed approach for the direct monitoring of on-road fatigued drivers will be feasible in the upcoming era of autonomous vehicles. In fact, the proposed approach is instantly feasible when vehicle trajectory big data collected with a penetration rate of 1.0% are available.
This investigation constitutes a first step in presenting a feasible solution to the direct monitoring of fatigued drivers on road segments. In addition to the effective results presented here, there are other opportunities to improve the reliability of the proposed approach in academic and practical fields related to road safety research. For instance, the temporal progression of driving fatigue, which differs between individuals according to circadian rhythms, was not considered. This is a viable area for future research.
The vehicle trajectory data used to support the findings of this study were provided only for academic research by the Smart Big Data Center of Korea Transportation Institute and QBICWARE.
The authors declare that they have no conflicts of interest regarding the publication of this paper.
This work was supported by the Incheon National University (International Cooperative) Research Grant in 2014.