Age-related differences in effects of non-driving related tasks on takeover performance in automated driving

Introduction: During SAE level 3 automated driving, the driver’s role changes from active driver to fallback-ready driver. Drowsiness is one of the factors that may degrade driver’s takeover performance. This study aimed to investigate effects of non-driving related tasks (NDRTs) to counter driver’s drowsiness with a Level 3 system activated and to improve successive takeover performance in a critical situation. A special focus was placed on age-related differences in the effects. Method: Participants of three age groups (younger, middle-aged, older) drove the Level 3 system implemented in a high-fidelity motion-based driving simulator for about 30 min under three experiment conditions: without NDRT, while watching a video clip, and while switching between watching a video clip and playing a game. The Karolinska Sleepiness Scale and eyeblink duration measured driver drowsiness. At the end of the drive, the drivers had to take over control of the vehicle and manually change the lane to avoid a collision. Reaction time and steering angle variability were measured to evaluate the two aspects of driving performance. Results: For younger drivers, both single and multiple NDRT engagements countered the development of driver drowsiness during automated driving, and their takeover performance was equivalent to or better than their performance without NDRT engagement. For older drivers, NDRT engagement did not affect the development of drowsiness but degraded takeover performance especially under the multiple NDRT engagement condition. The results for middle-aged drivers fell at an intermediate level between those for younger and older drivers. Practical Applications: The present findings do not support general recommendations of NDRT engagement to counter drowsiness during automated driving. This study is especially relevant to the automotive industry’s search for options that will ensure the safest interfaces between human drivers and automation systems.     

Exploring differences between self-report and electrophysiological indices of drowsy driving: A usability examination of a personal brain-computer interface device

IntroductionImpaired driving has resulted in numerous accidents, fatalities, and costly damage. One particularly concerning type of impairment is driver drowsiness. Despite advancements, modern vehicle safety systems remain ineffective at keeping drowsy drivers alert and aware of their state, even temporarily. Until recently the use of user-centric brain-computer interface (BCI) devices to capture electrophysiological data relating to driver drowsiness has been limited. Method: In this study, 25 participants drove on a simulated roadway under drowsy conditions. Results: Neither subjective nor electrophysiological measures differed between individuals who showed overt signs of drowsiness (prolonged eye closure) during the drive. However, the directionality and effect size estimates provided by the BCI device suggested the practicality and feasibility of its future implementation in vehicle safety systems. Practical applications: This research highlights opportunities for future BCI device research for use to assess the state of drowsy drivers in a real-world context.     

Improvement of driver active interventions during automated driving by displaying trajectory pointers—A driving simulator study

Abstract

Objective: The handover of vehicle control from automated to manual operation is a critical aspect of interaction between drivers and automated driving systems (ADS). In some cases, it is possible that the ADS may fail to detect an object. In this event, the driver must be aware of the situation and resume control of the vehicle without assistance from the system. Consequently, the driver must fulfill the following 2 main roles while driving: (1) monitor the vehicle trajectory and surrounding traffic environment and (2) actively take over vehicle control if the driver identifies a potential issue along the trajectory. An effective human–machine interface (HMI) is required that enables the driver to fulfill these roles. This article proposes an HMI that constantly indicates the future position of the vehicle.

Methods: This research used the Toyota Dynamic Driving Simulator to evaluate the effect of the proposed HMI and compares the proposed HMI with an HMI that notifies the driver when the vehicle trajectory changes. A total of 48 test subjects were divided into 2 groups of 24: One group used the HMI that constantly indicated the future position of the vehicle and the other group used the HMI that provided information when the vehicle trajectory changed.

The following instructions were given to the test subjects: (1) to not hold the steering wheel and to allow the vehicle to drive itself, (2) to constantly monitor the surrounding traffic environment because the functions of the ADS are limited, and (3) to take over driving if necessary.

The driving simulator experiments were composed of an initial 10-min acclimatization period and a 10-min evaluation period. Approximately 10?min after the start of the evaluation period, a scenario occurred in which the ADS failed to detect an object on the vehicle trajectory, potentially resulting in a collision if the driver did not actively take over control and manually avoid the object.

Results: The collision avoidance rate of the HMI that constantly indicated the future position of the vehicle was higher than that of the HMI that notified the driver of trajectory changes, χ² = 6.38, P < .05. The steering wheel hands-on and steering override timings were also faster with the proposed HMI (t test; P < .05).

Conclusions: This research confirmed that constantly indicating the position of the vehicle several seconds in the future facilitates active driver intervention when an ADS is in operation.     

Creating Pedestrian Crash Scenarios in a Driving Simulator Environment

Objective: In 2012 in the United States, pedestrian injuries accounted for 3.3% of all traffic injuries but, disproportionately, pedestrian fatalities accounted for roughly 14% of traffic-related deaths (NHTSA 2014 NHTSA. Traffic Safety Facts 2012 Pedestrians. Washington, DC: Author; 2014. DOT HS 811 888. [Google Scholar]). In many other countries, pedestrians make up more than 50% of those injured and killed in crashes. This research project examined driver response to crash-imminent situations involving pedestrians in a high-fidelity, full-motion driving simulator. This article presents a scenario development method and discusses experimental design and control issues in conducting pedestrian crash research in a simulation environment. Driving simulators offer a safe environment in which to test driver response and offer the advantage of having virtual pedestrian models that move realistically, unlike test track studies, which by nature must use pedestrian dummies on some moving track.

Methods: An analysis of pedestrian crash trajectories, speeds, roadside features, and pedestrian behavior was used to create 18 unique crash scenarios representative of the most frequent and most costly crash types. For the study reported here, we only considered scenarios where the car is traveling straight because these represent the majority of fatalities. We manipulated driver expectation of a pedestrian both by presenting intersection and mid-block crossing as well as by using features in the scene to direct the driver's visual attention toward or away from the crossing pedestrian. Three visual environments for the scenarios were used to provide a variety of roadside environments and speed: a 20–30 mph residential area, a 55 mph rural undivided highway, and a 40 mph urban area.

Results: Many variables of crash situations were considered in selecting and developing the scenarios, including vehicle and pedestrian movements; roadway and roadside features; environmental conditions; and characteristics of the pedestrian, driver, and vehicle. The driving simulator scenarios were subjected to iterative testing to adjust time to arrival triggers for the pedestrian actions. This article discusses the rationale behind creating the simulator scenarios and some of the procedural considerations for conducting this type of research.

Conclusions: Crash analyses can be used to construct test scenarios for driver behavior evaluations using driving simulators. By considering trajectories, roadway, and environmental conditions of real-world crashes, representative virtual scenarios can serve as safe test beds for advanced driver assistance systems. The results of such research can be used to inform pedestrian crash avoidance/mitigation systems by identifying driver error, driver response time, and driver response choice (i.e., steering vs. braking).     

Drowsiness and distraction while driving: A study based on smartphone app data

Introduction: Due to the negative impact on road safety from driver drowsiness and distraction, several studies have been conducted, usually under driving simulator and naturalistic conditions. Nevertheless, emerging technologies offer the opportunity to explore novel data. The present study explores retrospective data, which was gathered by an app designed to monitor the driver, which is available to any driver owning a smartphone. Method: Drowsiness and distraction alerts emitted during the journey were aggregated by continuous driving (called sub-journey). The data include 273 drivers who made 634 sub-journeys. Two binary logit models were used separately to analyze the probability of a drowsiness and distraction event occurring. Variables describing the continuous driving time (sub-journey time), the journey time (a set of sub-journeys), the number of breaks, the breaking duration time and the first sub-journey (categorical variable) were included. Additionally, categorical variables representing the gender and age of the drivers were also incorporated. Results: Despite the limitations of the retrospective data, interesting findings were obtained. The results indicate that the main risk factor of inattention is driving continuously (i.e., without stopping), but it is irrelevant whether the stop is long or short as well as the total time spent on the journey. The probability of distraction events occurring during the journey is higher than drowsiness events. Yet, the impact of increasing the driving time of the journey and stopping during the journey on the probability of drowsiness is higher than the probability of distraction. Additionally, this study reveals that the elderly are more prone to drowsiness. The data also include a group of drivers, who did not provide information on gender and age, who were found to be associated to drowsiness and distraction risk. Conclusions: The study shows that data gathered by an app have the potential to contribute to investigating drowsiness and distraction. Practical applications: Drivers are highly recommended to frequently stop during the journey, even for a short period of time to prevent drowsiness and distraction.     

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

Effect of alcohol use on accelerating and braking behaviors of drivers

Objective: The present study examines the accelerating and braking behaviors of drivers at different blood alcohol concentrations (BACs) in heterogeneous driving conditions using driving simulator experiments.

Methods: Eighty-two licensed drivers performed simulated driving in a rural road environment designed in the driving simulator at 4 BAC levels: 0.00, 0.03, 0.05, and 0.08%. Driving performance was analyzed using vehicle control variables such as mean acceleration and mean brake pedal force. Generalized linear mixed models were developed to quantify the effect of different alcohol levels and explanatory variables such as driver’s age, gender, and other factors on the driving performance indicators.

Results: Alcohol use was reported as a significant factor affecting the accelerating and braking performance of drivers. The acceleration model results indicated that drivers’ mean acceleration increased by 0.013, 0.026, and 0.027 m/s² for BAC levels of 0.03, 0.05, and 0.08%, respectively. Results of the brake pedal force model showed that drivers’ mean brake pedal force increased by 1.09, 1.32, and 1.44 N for BAC levels of 0.03, 0.05, and 0.08%, respectively. Age was a significant factor in both the models where a 1-year increase in driver age resulted in a 0.2% reduction in mean acceleration and a 19% reduction in mean brake pedal force. Driving experience could compensate for the negative effects of alcohol to some extent while driving.

Conclusions: The findings of the present study revealed that drivers tend to be more aggressive and impulsive under the influence of alcohol, which deteriorates their driving performance. Impairment in accelerating and braking behaviors of drivers under the influence of alcohol leads to increased crash probabilities. The conclusions may provide reference in making countermeasures against drinking and driving and contribute to traffic safety.     

Exploring simulated driving performance among varsity male soccer players

Background: It is documented that male athletes display riskier behaviors while driving (as well as in life in general) than female athletes and nonathletes. However, the literature has reported that athletes show better driving ability than nonathletes. This paradox between behaviors and abilities motivated the present study to further understand the collision risk of varsity athletes.

Objective: The current study estimates the performance differences between varsity male soccer players and male undergraduate nonathletes on (1) a driving task and (2) three perceptual–cognitive tasks (associated with collision risk prediction; i.e., Useful Field of View [UFOV] test).

Methods: Thirty-five male undergraduate students (15 varsity soccer players, 20 undergraduate nonathletes) took part in this study. Driving performance was assessed during 14?min of urban commuting using a driving simulator. While completing the simulated driving task and UFOV test, the physiological responses were monitored using an electrocardiograph (ECG) to document heart rate variability (HRV).

Results: Varsity soccer players showed more risky behaviors at the wheel compared to their nonathlete student peers. Varsity soccer players spent more time over the speed limit, committed more driving errors, and adopted fewer safe and legal behaviors. However, no difference was observed between both groups on driving skill variables (i.e., vehicle control, vehicle mobility, ecodriving). For subtests 1 and 2 of the UFOV (i.e., processing speed, divided attention), both groups performed identically (i.e., 17?ms). The nonathlete group tended to perform better on the selective attention task (i.e., subtest 3 of UFOV test; 63.2?±?6.2?ms vs. 87.2?±?10.7?ms, respectively; this difference was not significant, P = .76).

Conclusion: Preventive driving measures should be enforced in this high-risk population to develop strategies for risk reduction in male team athletes.     

Development of a method to predict crash risk using trend analysis of driver behavior changes over time

Objective: This study aimed at identifying and predicting in advance the point in time with a high risk of a virtual accident before a virtual accident actually occurs using the change of behavioral measures and subjective rating on drowsiness over time and the trend analysis of each behavioral measure.

Methods: Behavioral measures such as neck bending angle and tracking error in steering maneuvering during the simulated driving task were recorded under the low arousal condition of all participants who stayed up all night without sleeping. The trend analysis of each evaluation measure was conducted using a single regression model where time and each measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data, we proposed a method to predict in advance the point in time with a high risk of a virtual accident (in a real-world driving environment, this corresponds to a crash) before the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (we call this the point in time of a virtual accident).

Results: On the basis of applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could predict in advance the point in time with a high risk of a virtual accident before the point in time of a virtual accident.

Conclusion: The proposed method is a promising technique for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving and for warning drivers of such a drowsy and risky state.     

Driving performance comparing older versus younger drivers

Objectives: A cross-sectional study was conducted at the Touro University California campus to compare differences in reaction times and driving performance of younger adult drivers (18–40 years) and older adult drivers (60 years and older). Each test group consisted of 38 participants.

Methods: A Simple Visual Reaction Test (SVRT) tool was used to measure reaction times. The STISIM Drive M100 driving simulator was used to assess driving parameters. Driving performance parameters included mean lane position, standard deviation of mean lane position measured, mean speed, standard deviation of mean speed, car-following delay, car-following modulus, car-following coherence, off-road accidents, collisions, pedestrians hit, and traffic light tickets.

Results: Compared to younger participants, older drivers experienced significantly slower reaction times (510.0 ± 208.8 vs. 372.4 ± 96.1 ms, P =.0004), had more collisions (0.18 ± 0.39 vs. none, P =.0044), drove slower (44.6 ± 6.6 vs. 54.9 ± 11.7 mph, P <.0001), deviated less in speed (12.6 ± 4.3 vs. 16.8 ± 6.3, P =.0011), and were less able to maintain a constant distance behind a pace car (0.42 ± 0.23 vs. 0.59 ± 0.24; P =.0025).

Conclusions: Differences exist in driving patterns of older and younger drivers as measured by reaction times and driving simulator outcomes. These results are the first to compare these 2 specific adult age groups' driving performance as measured by a standardized driving simulator scenario. Identifying these differences is essential in addressing them and preventing future traffic injuries.     

Are older drivers’ driving patterns during an on-road driving task representative of their real-world driving patterns?

Abstract

Objective: The current study investigated whether older drivers’ driving patterns during a customized on-road driving task were representative of their real-world driving patterns.

Methods: Two hundred and eight participants (male: 68.80%; mean age?=?81.52 years, SD?=?3.37 years, range?=?76.00–96.00 years) completed a customized on-road driving task that commenced from their home and was conducted in their own vehicle. Participants’ real-world driving patterns for the preceding 4-month period were also collected via an in-car recording device (ICRD) that was installed in each participant’s vehicle.

Results: During the 4-month period prior to completing the on-road driving task, participants’ median real-world driving trip distance was 2.66?km (interquartile range [IQR]?=?1.14–5.79?km) and their median on-road driving task trip distance was 4.41?km (IQR?=?2.83–6.35?km). Most participants’ on-road driving task trip distances were classified as representative of their real-world driving trip distances (95.2%, n?=?198).

Conclusions: These findings suggest that most older drivers were able to devise a driving route that was representative of their real-world driving trip distance. Future research will examine whether additional aspects of the on-road driving task (e.g., average speed, proportion of trips in different speed zones) are representative of participants’ real-world driving patterns.     

Analysis of Driver Evasive Maneuvering Prior to Intersection Crashes Using Event Data Recorders

Objective: Intersection crashes account for over 4,500 fatalities in the United States each year. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging vehicle-based active safety systems that have the potential to help drivers safely navigate across intersections and prevent intersection crashes and injuries. The performance of an I-ADAS is expected to be highly dependent upon driver evasive maneuvering prior to an intersection crash. Little has been published, however, on the detailed evasive kinematics followed by drivers prior to real-world intersection crashes. The objective of this study was to characterize the frequency, timing, and kinematics of driver evasive maneuvers prior to intersection crashes.

Methods: Event data recorders (EDRs) downloaded from vehicles involved in intersection crashes were investigated as part of NASS-CDS years 2001 to 2013. A total of 135 EDRs with precrash vehicle speed and braking application were downloaded to investigate evasive braking. A smaller subset of 59 EDRs that collected vehicle yaw rate was additionally analyzed to investigate evasive steering. Each vehicle was assigned to one of 3 precrash movement classifiers (traveling through the intersection, completely stopped, or rolling stop) based on the vehicle's calculated acceleration and observed velocity profile. To ensure that any significant steering input observed was an attempted evasive maneuver, the analysis excluded vehicles at intersections that were turning, driving on a curved road, or performing a lane change. Braking application at the last EDR-recorded time point was assumed to indicate evasive braking. A vehicle yaw rate greater than 4° per second was assumed to indicate an evasive steering maneuver.

Results: Drivers executed crash avoidance maneuvers in four-fifths of intersection crashes. A more detailed analysis of evasive braking frequency by precrash maneuver revealed that drivers performing complete or rolling stops (61.3%) braked less often than drivers traveling through the intersection without yielding (79.0%). After accounting for uncertainty in the timing of braking and steering data, the median evasive braking time was found to be between 0.5 to 1.5 s prior to impact, and the median initial evasive steering time was found to occur between 0.5 and 0.9 s prior to impact. The median average evasive braking deceleration for all cases was found to be 0.58 g. The median of the maximum evasive vehicle yaw rates was found to be 8.2° per second. Evasive steering direction was found to be most frequently in the direction of travel of the approaching vehicle.

Conclusions: The majority of drivers involved in intersection crashes were alert enough to perform an evasive action. Most drivers used a combination of steering and braking to avoid a crash. The average driver attempted to steer and brake at approximately the same time prior to the crash.     

Fatigued and drowsy driving: a survey of attitudes, opinions and behaviors

INTRODUCTION: There is evidence suggesting that the problem of fatigued or drowsy driving is an important contributor to road crashes. However, not much is known about public perceptions of the issue. The purpose of this study was to obtain information on attitudes, opinions, and professed practices related to fatigued or drowsy driving. METHODS: The data were gathered by means of a public opinion poll among a representative sample of 750 Ontario drivers. RESULTS: A majority of drivers (58.6%) admitted that they occasionally drive while fatigued or drowsy. Of greater importance, 14.5% of respondents admitted that they had fallen asleep or "nodded off" while driving during the past year. Nearly 2% were involved in a fatigue or drowsy driving related crash in the past year. Respondents were also asked about measures they take to overcome fatigue or drowsiness. Results indicate that relatively ineffective measures such as opening the window or playing music are the most popular; the most effective preventive measure--taking a rest--is the least popular. DISCUSSION: The prevalence of the behavior, coupled with the ineffective prevention measures favored by the public suggest there is a need for increasing their level of awareness and knowledge about the problem. IMPACT ON INDUSTRY: Results from this study further emphasize the importance of increasing the fatigued and drowsy driving knowledge base and the need to educate the public about it.     

Fatal crash between a car operating with automated control systems and a tractor-semitrailer truck

Abstract

Objective: The objective of this investigation was to evaluate the interaction between an SAE level 2 automated vehicle and the driver, including the limitations imposed by the vehicle on the driver.

Methods: A case study of the first fatal crash involving a vehicle operating with an automated control system was performed using scene evidence, vehicle damage, and recorded data available from the vehicle, and information from both drivers, including experience, phone records, computer systems, and medical information, was reviewed.

Results: System performance data downloaded from the car indicated that the driver was operating it using the Traffic-Aware Cruise Control and Autosteer lane-keeping systems, which are automated vehicle control systems within Tesla’s Autopilot suite. As the car crested the hill, a tractor trailer began its left turn onto a crossing roadway. Although reconstruction of the crash determined that there was sufficient sight distance for both drivers to see each other and take action, neither responded to the circumstances leading to the collision. Further, based on the speeds of the vehicles and simulations of the truck’s path, the car driver had at least 10.4?s to detect the truck and take evasive action. Neither the car driver nor the Autopilot system changed the vehicle’s velocity.

?At the time of the crash, the system performance data indicated that the last driver interaction with the system was 1?min 51?s prior when the cruise control speed was set to 74?mph. The driver was operating the vehicle using the Autopilot system for 37 of the 41?min in the last trip. During this period, the vehicle detected the driver’s hands on the steering wheel for a total of 25?s; each time his hands were detected on the wheel was preceded by a visual alert or auditory warning.

Conclusions: The National Transportation Safety Board (NTSB) determined that the probable cause of the Williston, Florida, crash was the truck driver’s failure to yield the right of way to the car, combined with the car driver’s inattention due to overreliance on vehicle automation, which resulted in the car driver’s lack of reaction to the presence of the truck. Contributing to the car driver’s overreliance on the vehicle automation was the car’s operational design, which permitted the driver’s prolonged disengagement from the driving task and his use of the automation in ways inconsistent with guidance and warnings from the manufacturer.     

Assessing drivers' response during automated driver support system failures with non-driving tasks

IntroductionWith the increase in automated driver support systems, drivers are shifting from operating their vehicles to supervising their automation. As a result, it is important to understand how drivers interact with these automated systems and evaluate their effect on driver responses to safety critical events. This study aimed to identify how drivers responded when experiencing a safety critical event in automated vehicles while also engaged in non-driving tasks.MethodIn total 48 participants were included in this driving simulator study with two levels of automated driving: (a) driving with no automation and (b) driving with adaptive cruise control (ACC) and lane keeping (LK) systems engaged; and also two levels of a non-driving task (a) watching a movie or (b) no non-driving task. In addition to driving performance measures, non-driving task performance and the mean glance duration for the non-driving task were compared between the two levels of automated driving.ResultsDrivers using the automated systems responded worse than those manually driving in terms of reaction time, lane departure duration, and maximum steering wheel angle to an induced lane departure event. These results also found that non-driving tasks further impaired driver responses to a safety critical event in the automated system condition.ConclusionIn the automated driving condition, driver responses to the safety critical events were slower, especially when engaged in a non-driving task.Practical applicationTraditional driver performance variables may not necessarily effectively and accurately evaluate driver responses to events when supervising autonomous vehicle systems. Thus, it is important to develop and use appropriate variables to quantify drivers' performance under these conditions.     

Driving in mild cognitive impairment: The role of depressive symptoms

Objectives: Previous studies indicate a negative association between depression and driving fitness in the general population. Our goal was to cover a gap in the literature and to explore the link between depressive symptoms and driving behavior in individuals with mild cognitive impairment (MCI) through the use of a driving simulator experiment.

Methods: Twenty-four individuals with MCI (mean age = 67.42, SD = 7.13) and 23 cognitively healthy individuals (mean age = 65.13, SD = 7.21) were introduced in the study. A valid driving license and regular car use served as main inclusion criteria. Data collection included a neurological/neuropsychological assessment and a driving simulator evaluation. Depressive symptomatology was assessed with the Patient Health Questionnaire (PHQ-9).

Results: Significant interaction effects indicating a greater negative impact of depressive symptoms in drivers with MCI than in cognitively healthy drivers were observed in the case of various driving indexes, namely, average speed, accident risk, side bar hits, headway distance, headway distance variation, and lateral position variation. The associations between depressive symptoms and driving behavior remained significant after controlling for daytime sleepiness and cognition.

Conclusions: Depressive symptoms could be a factor explaining why certain patients with MCI present altered driving skills. Therefore, interventions for treating the depressive symptoms of individuals with MCI could prove to be beneficial regarding their driving performance.     

Drowsy drivers: The effect of light and circadian rhythm on crash occurrence

Drowsiness has been recognized as a pervasive problem for drivers, with effects comparable to alcohol. Alcohol, however, has a clear legal limit for impairment; there are no comparable criteria to suggest sleepiness. Drowsiness has been associated with light and circadian rhythm. To investigate the joint effects of these factors on crash occurrence, along with other factors, single vehicle crashes reported in Ontario (1999–2004) were analyzed. Crashes occurring at four times of day, when light varies and circadian rhythm is low (2–5 a.m. and 2–4 p.m.) and with similar light conditions and higher circadian rhythm (9–11 p.m. and 10 a.m.–12 noon). Logistic regression was used to see how light and other factors are associated with single vehicle crashes occurring at times of low circadian rhythm, when drowsiness is more likely.Initial results indicated many circumstances associated with occurrence at these times: the age and sex of the driver and reported driver condition as well as weather. There is, however, an interaction between light and presumed alertness. In separate analyses for daytime and night time crashes most variables were significant for nighttime crashes but not for daytime events. The effects of alcohol and youth remained. A lack of light may exacerbate the effects of other factors at times of low alertness; this should be further investigated in controlled environments such as sleep laboratories and/or driving simulators.     

A framework for definition of logical scenarios for safety assurance of automated driving

Abstract

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments.

Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains.

Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project.

Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.     

The role of self-regulation in the context of driver distraction: A simulator study

Objective: There is considerable evidence for the negative effects of driver distraction on road safety. In many experimental studies, drivers have been primarily viewed as passive receivers of distraction. Thus, there is a lack of research on the mediating role of their self-regulatory behavior. The aim of the current study was to compare drivers' performance when engaged in a system-paced secondary task with a self-paced version of this task and how both differed from baseline driving performance without distraction.

Methods: Thirty-nine participants drove in a simulator while performing a secondary visual–manual task. One group of drivers had to work on this task in predefined situations under time pressure, whereas the other group was free to decide when to work on the secondary task (self-regulation group). Drivers' performance (e.g., lateral and longitudinal control, brake reaction times) was also compared with a baseline condition without any secondary task.

Results: For the system-paced secondary task, distraction was associated with high decrements in driving performance (especially in keeping the lateral position). No effects were found for the number of collisions, probably because of the lower driving speeds while distracted (compensatory behavior). For the self-regulation group, only small impairments in driving performance were found. Drivers engaged less in the secondary task during foreseeable demanding or critical driving situations.

Conclusions: Overall, drivers in the self-regulation group were able to anticipate the demands of different traffic situations and to adapt their engagement in the secondary task, so that only small impairments in driving performance occurred. Because in real traffic drivers are mostly free to decide when to engage in secondary tasks, it can be concluded that self-regulation should be considered in driver distraction research to ensure ecological validity.     

Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.

Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.

Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between ?0.0011 and 0.056 for a car driver and between ?0.00086 and 0.067 for a truck driver.

Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering.