As part of the HASTE European Project, effects of visual and cognitive demand on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). The present paper reports results from simulated and real motorway driving. Data were collected in a fixed base simulator, a moving base simulator and an instrumented vehicle driven in real traffic. The data collected included speed, lane keeping performance, steering wheel movements, eye movements, physiological signals and self-reported driving performance. The results show that the effects of visual and cognitive load affect driving performance in qualitatively different ways. Visual demand led to reduced speed and increased lane keeping variation. By contrast, cognitive load did not affect speed and resulted in reduced lane keeping variation. Moreover, the cognitive load resulted in increased gaze concentration towards the road centre. Both S-IVIS had an effect on physiological signals and the drivers’ assessment of their own driving performance. The study also investigated differences between the three experimental settings (static simulator, moving base simulator and field). The results are discussed with respect to the development of a generic safety test regime for In-vehicle Information Systems.

Effects of visual and cognitive load in real and simulated motorway driving

BackgroundDistracted driving attributable to the performance of secondary tasks is a major cause of motor vehicle crashes both among teenagers who are novice drivers and among adults who are experienced drivers MethodsWe conducted two studies on the relationship between the performance of secondary tasks, including cell-phone use, and the risk of crashes and near-crashes To facilitate objective assessment, accelerometers, cameras, global positioning systems, and other sensors were installed in the vehicles of 42 newly licensed drivers (163 to 170 years of age) and 109 adults with more driving experience ResultsDuring the study periods, 167 crashes and near-crashes among novice drivers and 518 crashes and near-crashes among experienced drivers were identified The risk of a crash or near-crash among novice drivers increased significantly if they were dialing a cell phone (odds ratio, 832; 95% confidence interval [CI], 283 to 2442), reaching for a cell phone (odds ratio, 705; 95% CI, 264 to 1883)

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Distracted driving and risk of road crashes among novice and experienced drivers

A method for controlling a vehicle which includes obtaining, via at least one detecting device, behavior information of a driver in the vehicle, and transitioning control statuses of the vehicle according to the driver behavior information.

Method and system for controlling a vehicle

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers' workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.

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Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence

A driving simulator study was designed to investigate drivers’ ability to resume control from a highly automated vehicle in two conditions: (i) when automation was switched off and manual control was required at a system-based, regular interval and (ii) when transition to manual was based on the length of time drivers were looking away from the road ahead. In addition to studying the time it took drivers to successfully resume control from the automated system, eye tracking data were used to observe visual attention to the surrounding environment and the pattern of drivers’ eye fixations as manual control was resumed in the two conditions. Results showed that drivers’ pattern of eye movement fixations remained variable for some time after automation was switched off, if disengagement was actually based on drivers’ distractions away from the road ahead. When disengagement was more predictable and system-based, drivers’ attention towards the road centre was higher and more stable. Following a lag of around 10 s, drivers’ lateral control of driving and steering corrections (as measured by SDLP and high frequency component of steering, respectively) were more stable when transition to manual control was predictable and based on a fixed time. Whether automation transition to manual was based on a fixed or variable interval, it took drivers around 35–40 s to stabilise their lateral control of the vehicle. The results of this study indicate that if drivers are out of the loop due to control of the vehicle in a limited self-driving situation (Level 3 automation), their ability to regain control of the vehicle is better if they are expecting automation to be switched off. As regular disengagement of automation is not a particularly practical method for keeping drivers in the loop, future research should consider how to best inform drivers of their obligation to resume control of driving from an automated system.

Transition to manual: driver behaviour when resuming control from a highly automated vehicle

Eye-movement measures were found to be highly sensitive to the demands of visual and auditory in-vehicle tasks as well as driving task demands. Two newer measures, Percent road centre and Standard deviation of gaze, were found to be more sensitive, more robust, more reliable, and easier to calculate than established glance-based measures. The eye-movement measures were collected by two partners within the EU project HASTE to determine their sensitivity to increasingly demanding in-vehicle tasks by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). Data from 119 subjects were collected from four routes: a motorway in real traffic with an instrumented vehicle, a motorway in a fixed base simulator, and from rural roads in two different fixed base simulators. As the visual task became more difficult, drivers looked less at the road centre area ahead, and looked at the display more often, for longer periods, and for more varied durations. The auditory task led to an increasing gaze concentration to road centre. Gaze concentration to the road centre area was also found as driving task complexity increased, as shown in differences between rural curved- and straight sections, between rural and motorway road types, and between simulator and field motorways.

Sensitivity of eye-movement measures to in-vehicle task difficulty

Method and arrangement for controlling a subsystem of a vehicle dependent upon a sensed level of driver inattentiveness to vehicle driving tasks. A variable characteristic is measured, on a substantially real-time basis, which correlates to the driver's inattentiveness. The level of inattentiveness is assessed based at least in part on the measurement. The performance of a subsystem of the vehicle, such as cruise control or lane keeping support, is tailored, based thereupon, to assure that behavior of the vehicle appropriately matches the driver's present level of inattentiveness. The subsystem's operation is controlled in an effort to avoid or prevent the establishment of driving conditions that become inherently more dangerous as the driver's level of inattentiveness increases.

Method and arrangement for controlling vehicular subsystems based on interpreted driver activity

This work was sponsored by the second Strategic Highway Research Program (SHRP 2), which is administered by the Transportation Research Board of the National Academies. This project was managed by Ken Campbell, Chief Program Officer for SHRP 2 Safety , and Jim Hedlund, SHRP 2 Safety Coordinator . The research reported on herein was performed by the main contractor SAFER Vehicle and Traffic Safety Centre at Chalmers, Gothenburg, Sweden. SAFER is a joint research unit where 25 partners from the Swedish automotive industry, academia and authorities cooperate to make a center of excellence within the field of vehicle and traffic safety (see www.chalmers.se/safer ). The host and legal entity SAFER is Chalmers University of Technology. Principle Investigator Tr ent Victor is Adjunct Professor at Chalmers and worked on the project as borrowed personnel to Chalmers but his main employer is Volvo Cars. The other authors of this report are Co - PI Marco Dozza, Jonas Bargman, and Christian - Nils Boda of Chalmers Universi ty of Technology (as a SAFER partner) ; Johan Engstrom and Gustav Markkula of Volvo Group Trucks Technology (as a SAFER partner) ; John D. Lee of University of Wisconsin - Madison (as a consultant to SAFER); and Carol Flannagan of University of Michigan Transp ortation Research Institute (UMTRI) (as a consultant to SAFER). The authors acknowledge the contributions to this research from Ines Heinig, Vera Lisovskaja, Olle Nerman, Holger Rootzen, Dmitrii Zholud, Helena Gellerman , Leyla Vujic, Martin Rensfeldt, Stefan Venbrant, Akhil Krishnan, Bharat Mohan Redrouthu, Daniel Nilsson of Chalmers; Mikael Ljung - Aust of Volvo Cars; Erwin Boer; Christer Ahlstrom and Omar Bagdadi of VTI.

Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk

System and method for real-time, automatic, recognition of large time-scale driving patterns employs a statistical pattern recognition framework, implemented by means of feed-forward neural network utilizing models developed for recognizing, for example, four classes of driving environments, namely highway, main road, suburban traffic and city traffic, from vehicle performance data. A vehicle control application effects changes in vehicle performance aspects based on the recognized driving environment.

System and method for real-time recognition of driving patterns

A method of analyzing data based on the physiological orientation of a driver is provided. Data is descriptive of a driver's gaze-direction is processing and criteria defining a location of driver interest is determined. Based on the determined criteria, gaze-direction instances are classified as either on-location or off-location. The classified instances can then be used for further analysis, generally relating to times of elevated driver workload and not driver drowsiness. The classified instances are transformed into one of two binary values (e.g., 1 and 0) representative of whether the respective classified instance is on or off location. The uses of a binary value makes processing and analysis of the data faster and more efficient. Furthermore, classification of at least some of the off-location gaze direction instances can be inferred from the failure to meet the determined criteria for being classified as an on-location driver gaze direction instance.

Trent Victor

Papers

Sensitivity of eye-movement measures to in-vehicle task difficulty

Method and arrangement for controlling vehicular subsystems based on interpreted driver activity

Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk

System and method for real-time recognition of driving patterns

Method and apparatus for determining and analyzing a location of visual interest