Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification

Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psychological disorders can be identified from driving data, and usually analyses utilize simple models and aggregated data. This study aims at utilizing microscopic data from a driving simulator to detect sessions belonging to “depressed” drivers by utilizing powerful machine learning classifiers. Driving simulator sessions from 11 older drivers with symptoms of depression and 65 healthy drivers were utilized towards that aim. Random Forests, an ensemble classifier, with proven efficiency among transportation applications, are then trained on highly disaggregated data describing the mean and standard deviation of speed and lateral or longitudinal acceleration of drivers in the simulator. The kinematic data were aggregated in 30-seconds, 1-minute and 5-minute intervals, but the corresponding time-series of the measurements were also taken into account. Furthermore, classifiers were treated with imbalanced learning techniques to address the scarcity of depressed drivers among the healthy. Time-series of mean speed and the standard deviation of longitudinal acceleration even with a duration of 30-seconds have proven to be the best predictors of driving sessions belonging to depressed drivers with a very low rate of false alarms. The results outperform previous approaches, and indicate that naturalistic driving data or deep learning could prove even more efficient in detecting depression.     

Behavioural adaptation and effectiveness of a Forward Collision Warning System depending on a secondary cognitive task

Forward Collision Warning Systems (FCWS) have been designed to enhance road safety by reducing the number of rear-end collisions. Nevertheless, little is known about how drivers adapt their behaviour over time when using this kind of system. In addition, these systems are expected to aid particularly distracted drivers. However, previous research has suggested that the effectiveness of the system could depend on the difficulty level of the secondary task. The objective of this study on driving simulator was twofold. Firstly, it consisted in evaluating the behavioural adaptation to an FCWS as well as analysing the possible consequences of driving without the system after a short period of adaptation. Secondly, it was to evaluate the effectiveness of the system according to two different difficulty levels of a cognitive secondary task. The results showed that drivers adapted their behaviour positively when the system was introduced. Nevertheless, both the effectiveness and the behavioural adaptation in the short term were dependent on the cognitive load induced by the secondary task. These findings suggest that the warning needs some attentional resources to be processed. Finally, no negative or transfer effect was observed following the removal of the system after a short period of adaptation.     

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.     

Effect of multimodal takeover request issued through A-pillar LED light,earcon, speech message,and haptic seat in conditionally automated driving

The driver of a conditionally automated vehicle equivalent to level 3 of the SAE is obligated to accept a takeover request (TOR) issued by the vehicle. Considerable research has been conducted on the TOR, especially in terms of the effectiveness of multimodal methods. Therefore, in this study, the effectiveness of various multimodalities was compared and analyzed. Thirty-six volunteers were recruited to compare the effects of the multimodalities, and vehicle and physiological data were obtained using a driving simulator. Eight combinations of TOR warnings, including those implemented through LED lights on the A-pillar, earcon, speech message, or vibrations in the back support and seat pan, were analyzed to clarify the corresponding effects. When the LED lights were implemented on the A-pillar, the driver reaction was faster (p = 0.022) and steering deviation was larger (p = 0.024) than those in the case in which no LED lights were implemented. The speech message resulted in a larger steering deviation than that in the case of the earcon (p = 0.044). When vibrations were provided through the haptic seat, the reaction time (p < 0.001) was faster, and the steering deviation (p = 0.001) was larger in the presence of vibrations in the haptic seat than no vibration. An interaction effect was noted between the visual and auditory modalities; notably, the earcon resulted in a small steering deviation and skin conductance response amplitude (SCR amplitude) when implemented with LED lights on the A-pillar, whereas the speech message led to a small steering deviation and SCR amplitude without the LED lights. In the design of a multimodal warning to be used to issue a TOR, the effects of each individual modality and corresponding interaction effects must be considered. These effects must be evaluated through application to various takeover situations.     

Other road users’ adaptations to increase safety in response to older drivers’ behaviour

Changes in physical and cognitive abilities not only challenge the driving ability of older adults, in some situations age-related changes in driving behaviour require other road users to adapt their behaviour to maintain a safe traffic situation. In this study, we aimed to map age-related differences in driving behaviour and assess the impact on other road users. A group younger and a group older adults drove four different routes containing challenging situations (e.g., merging into motorway traffic) in a driving simulator while measures of driving behaviour were collected. Other road users’ deceleration responses to the driver’s behaviour were also collected as a measure of behavioural adaptation. Our results showed similar driving performance between young and older drivers when task complexity was low, but reduced performance in older drivers when tasks requirements increased. Lower driving speed and longer waiting times that were observed in older drivers can be interpreted as compensatory behaviour aimed at creating more time to lower task requirements. Crucially, in a non-time critical situation this compensatory behaviour was found to be successful, however in a time-critical situation (merging onto a motorway) this strategy had negative side effects because other road users had to decelerate in order to keep a safe distance. Our results show the importance of anticipation and adaptation by other road users for the success of older driver’s strategies and traffic safety.     

Behavior model and guidance strategies of the crossing behavior at unsignalized intersections in the connected vehicle environment

The connected vehicle environment is considered to be a disruptive technology that reconstructs the way people travel and road transport, and more importantly, ensures traffic safety. This study aims on investigating drivers’ interactive behavior at an unsignalized intersection in the connected vehicle environment. Specifically, a simplified iterative behavior model was established to predict the potential conflicting vehicles’ behavior. Furthermore, based on principles of safety, efficiency, and comfort, the guidance strategies were proposed to help the subject vehicles cross intersections. A multi-user driving simulator experiment was carried out and 48 participants were divided into 24 pairs to complete the test. The simplified iterative behavior model was constructed based on the dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions. The comparison results showed the behavior model was an effective microscopic simulation tool for vehicles at unsignalized intersections with high accuracy and good applicability. Then, the guidance strategies under different compliance rates (baseline, 50 % compliance, 100% compliance) were evaluated based on three indexes i.e. standard deviation of speed (SDS), duration of crossing the intersection (DCI), and time exposed post-encroachment-time (TEP). The numerical simulation results showed that the guidance strategies could effectively improve the safety and efficiency of drivers crossing the intersection under both 50% and 100% compliance rates. Besides, with the increase of compliance rate, the comfort level also increased evidently. This study can provide theoretical and algorithmic references for microscopic simulation and guidance strategy at unsignalized intersections in the connected vehicle environment.     

Driving around bends with or without shoulders: The influence of bend direction

Paved shoulders have long been used to create “forgiving” roads where drivers can maintain control of their vehicles even when as they drift out of the lane. While the safety benefits of shoulders have been well documented, their effects on driver behavior around curves have scarcely been examined. The purpose of this paper is to fill this gap by assessing whether the addition of shoulders affects driver behavior differently as a function of bend direction. Driver behavior in a driving simulator was analyzed on left and right curves of two-lane rural roads in the presence and absence of 0.75-m and 1.25-m shoulders. The results demonstrated significant changes in drivers’ lateral control when shoulders were provided. In the absence of oncoming traffic, the shoulders caused participants to deviate more toward the inner lane edge at curve entry, at the apex and at the innermost position on right bends but not left ones. In the presence of oncoming traffic, this also occurred at the apex and the innermost position, leading participants to spend more time off the lane on right curves. Participants did not slow down in either traffic condition to compensate for steering farther inside, thereby increasing the risk of lane departure on right curves equipped with shoulders. These findings highlight the direction-specific influence of shoulders on a driver’s steering control when driving around bends. They provide arguments supporting the idea that drivers view paved shoulders as a new field of safe travel on right curves. Recommendations are made to encourage drivers to keep their vehicle within the lane on right bends and to prevent potential interference with cyclists when a shoulder is present.     

Effect of electronic device use while driving on cardiovascular reactivity

Motor vehicle collisions are the leading cause of death in people ages 5–34 in the US, and secondary task engagement, such as talking on a cell phone, is a leading contributor to motor vehicle collisions. The negative effects of secondary task engagement on driving performance has become a prominent recent topic of study given the increasing amount of time drivers engage in distracted driving. However, few studies have examined the effects of secondary task engagement while driving on health related outcomes such as cardiovascular reactivity. Cardiovascular reactivity, as measured by heart rate and blood pressure, has been used in previous studies as a means of measuring effort in task engagement as well as a means to predict cardiovascular disease and stroke. This study investigates the effect of secondary task (talking on a cell phone, texting, and driving with no task) while driving in a simulator on cardiovascular reactivity. Using difference scores between baseline (a period of inactivity) and stimulus (driving with no task and driving with secondary tasks), a repeated measures analysis of variance using a mixed model approach was used to determine the effect of secondary task on cardiovascular reactivity. Findings indicated that talking on a cell phone while driving significantly increased cardiovascular reactivity via heart rate and blood pressure compared to driving with no task. Texting while driving did not differ significantly from driving with no task. This study demonstrates the need for more research on the long term effects of secondary tasks while driving on cardiovascular reactivity and for assessing the risks associated with secondary task use while driving on developing cardiovascular disease or stroke.     

Investigating the effectiveness of perceptual treatments on a crest vertical curve: A driving simulator study

Rural roads are characterized by a high percentage of run-off-the-road accidents and head-on collisions, mainly caused by inappropriate speeds and failure to maintain a proper lateral position along the roadway alignment. Among several road safety treatments, low-cost perceptual measures are considered an effective tool, as they generally increase the risk perceived by drivers, or even alter the drivers’ speed perception, and consequently tempting them to decrease their speeds. Their effectiveness has been widely recognized in a number of studies, especially with respect to road intersections and curves.The overall aim of this study is to investigate the effects of different perceptual treatments on driving speed, along a crest vertical curve of an existing two-lane rural road, in order to identify the most effective measure to reduce speed and define its subsequent implementation in the field. Three perceptual treatments were tested using a driving simulator: white peripheral transverse bars, red peripheral transverse bars and optical speed bars, with each one being painted along the approaching tangent to the crest vertical curve. The effects of these speed-reducing measures were investigated using a sample of forty-four participants, by comparing the driving speeds with those recorded under a baseline condition (without a treatment); these were also used to validate the driving simulator’s speed measurements with those found in the field. Moreover, subjective measures were collected, consisting of the driver’s static evaluation of the desired speed, risk perception and markings comprehension, based on screen shot pictures that represented the simulated configurations of the treatments.The findings demonstrated an overall effectiveness of the perceptual treatments, although only the red peripheral transverse bars were found to significantly reduce the driving speeds (−6 km/h). The analysis of the questionnaire yielded interesting information and demonstrated the importance of performing driving simulation tests for evaluating the effectiveness of perceptual treatments.Finally, the results confirmed the enormous potential of using driving simulators to pinpoint a number of speed-reducing measures, and consequently select the most effective one that reduces cost and promotes safety before its actual implementation in the field.     

Valid representation of a highly dynamic collision avoidance scenario in a driving simulator

In Germany the second-most frequent accidents in road traffic are rear-end collisions. For this reason rear-end collisions are quite important for accident research and the development of driving safety systems. To examine the functionality and to design the human–machine-interface of new driving safety systems, especially in the early development phase, subject tests are necessary. Because of the great hazard potential of such safety critical scenarios for the test persons, they are often conducted in a driving simulator (DS). Accordingly, validity is an important qualification to ensure that the findings collected in a simulated test environment can be directly transferred to the real world.This paper regards the question of driving behavior validity of DS in critical situations. There are hardly any validation studies which analyze the driving behavior in a specific collision avoidance situation.The validation study described in this paper aims to evaluate the behavioral validity of a fixed-base simulator in a collision avoidance situation. For this reason a field study from 2007 was replicated in a fixed-base simulator environment.The main questions of this validation study were if the driver can notice an active hazard braking system and if the driving behavior in a static simulator can be valid in such a critical situation.The key finding of the study states that there is no driving behavior validity in a static driving simulator for the tested dynamic scenario. The missing vestibular feedback causes a different behavior of the participants in field and simulator. The resulting absence of comparability leads to non-valid performance indicators. But these indicators are key parameters for analyzing the function and acceptance of active braking systems. So the question arises, which motion performance does a motion base have to provide in order to achieve valid acceleration simulation of such a highly dynamic collision avoidance scenario. The DS’s performance is measured in workspace, velocity and acceleration.