Whereas driver fitness is widely recognized as a prerequisite for safety, the construct lacks a formal framework. We present the first steps towards its operationalization. We interpret availability and allocation of cognitive and physiological resources as fitness dimensions, and risk factors fatigue/drowsiness, distraction/inattention, intoxication, sudden incapacitation and speeding as state variables loading on these dimensions. We collect and synthesize US crash data, and calculate Relative Risks RR and Population Attributable Fractions AFp. Sudden incapacitation (RR = 32.112) is the most detrimental to individual safety, followed by alcohol intoxication (RR=11.277), fatigue/drowsiness (RR=4.966), speeding (RR=2.743), and inattention/distraction (RR=0.241). Taking into account prevalence, alcohol intoxication has the largest impact (AFp=0.068), followed by speeding (AFp=0.065), fatigue/drowsiness (AFp=0.059), incapacitation (AFp=0.013) and inattention/distraction (AFp=−0.416). Alcohol intoxication plays a major role in fatal crashes (RR=47.341, AFp=0.247), followed by speeding (RR=9.364, AFp=0.25). The analysis emphasizes the dangers of intoxicated driving and speeding, but also reveals shortcomings in census data, notably an under-representation of inattention, as well as the need for specific data collection on intoxication, reckless driving and sudden incapacitation in crashes. Taken together, the conceptual model and data synthesis provide a first step towards a framework of driver fitness; formalizing hypotheses on causal relations and providing crude estimates of factor loadings. This framework has practical applications as a model for multimodal driver monitoring systems, and to calculate risk factor impacts to inform policy makers.@en

With increasing implementation of automated driving technology it is expected that different automation modes will be present within the same vehicle and within a single trip. At all times during automated driving the driver needs to have ‘mode awareness’, which is an understanding of the automation mode and the corresponding responsibilities. Yet, research on HMI design to support mode awareness for multiple automation modes within a single vehicle and within a single trip is currently limited. The current work describes the development and evaluation of a Human Machine Interface (HMI) to support mode awareness while driving in different automation modes. The work exists of three phases: Phase 1 defines functional requirements for HMI design based on literature review and 5 experimental studies including 146 participants. Phase 2 implements the functional requirements in HMI design through expert and focus group sessions. Phase 3 evaluates and improves upon the HMI design employing virtual reality and the RITE (Rapid Iterative Testing and Evaluation) method with 18 participants. The result is a continuous and holistic HMI design creating mode awareness through ambience. Findings from Phase 3 and previous research indicate that this HMI is comprehended well, with a relatively low task load, and with a good experienced system usability. It is important to additionally evaluate the HMI design resulting from the current study in driving simulators and in on-road tests. Such tests will provide an opportunity to verify and expand on the current study's findings and to contribute to guidelines for HMI design.

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Advanced driver assistance systems such as adaptive cruise control (ACC) and lane keeping system (LKS) potentially contribute to reducing crash rates and traffic congestion. On-road studies based on early ACC systems operational at medium–high speeds only have shown that the system reduces the proportion of short time gaps when activated. Despite the effects on driver behaviour, most mathematical models assessing the impact of ACC and LKS systems on crash rates and traffic congestion are not based on empirical findings. This study examines the factors that influence changes in the longitudinal vehicle control when driving with ACC and LKS. The data were collected in a naturalistic driving experiment with full-range ACC and LKS and two different vehicle brands (BMW and Tesla) in the Netherlands. To capture changes that are relevant for traffic safety, speeding and a time gap shorter than one second were investigated. The factors influencing speeding and short time gaps were analysed using statistical tests and logistic regression models with random effects, that allow to control for the impact of different explanatory variables and correlations between repeated 10-s intervals over time. The findings revealed that, overall, drivers were less likely to speed and they were also less likely to have a time gap shorter than one second in the experimental condition with the ACC and the LKS than in the baseline condition in manual driving. Drivers were likely to speed in the following 10-s interval when the current speed was close to the speed limit, and/or when the next speed limit was lower than the current speed limit, and/or when the acceleration was high. Drivers were likely to have a short time gap in the following 10-s interval when approaching a slower leader, and/or when the current time gap was short and/or when the acceleration was high. Controlled for these main factors, drivers were less likely to speed and to have a short time gap when the ACC and the LKS were active. However, drivers were more likely to speed when overruling the ACC by pressing the gas pedal. When the systems were active, one vehicle brand showed a smaller probability of a short time gap than the other brand, suggesting differences in ACC system settings between brands. In addition, the speeding probability increased while the probability of a short time gap decreased over time during the trip after the activation of the systems. Although further studies including a larger sample of participants and a wider range of traffic situations are needed, the results are useful to the design of automated vehicles that prevent speeding and short time gaps, and to the implementation of traffic simulations that evaluate the impact of ACC and LKS on crash rates and traffic congestion according to realistic on-road data.

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This study reports usage of supervised automation and driver attention from longitudinal naturalistic driving observations. Automation inexperienced drivers were provided with instrumented vehicles with adaptive cruise control (ACC) and lane keeping (LK) features (SAE level 2). Data was collected comparing one month of driving without support to two months where drivers were instructed to use automation as desired. On highways, level 2 automation was used respectively 63% and 57% of the time by Tesla and BMW users, with peak usage during slow stop-and-go traffic (0–30 km/h) and higher speeds (>80 km/h). On roads with speed limits below 70 km/h, automation was used less than 8%, and use on urban roads was incidental rather than habitual. Automation usage increased with time in trip, but no clear time of day effects were found. Head pose data could not classify driver attention, and we recommend gaze tracking in future studies. Head pose deviation was selected as alternative indicator for monitoring activity. Comparing among forms of automation usage on the highway, head heading deviation was smallest during ACC use, but did not differ between automation and baseline manual driving. Head heading deviation during manual driving was smaller in the baseline than the experimental phase, which suggests that motives for manual highway driving may be attention related. Automation usage did not change much over the first 12 weeks of the experimental condition, and there were no longitudinal changes in head pose deviation.

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Objective: In this study, we assessed the use of portable navigation systems in everyday driving by applying in-vehicle naturalistic driving. Method: Experienced users of navigation systems, 7 females and 14 males, were provided with a specially equipped vehicle for approximately 1 month. Their trips were recorded using 4 cameras, Global Positioning System (GPS) data, and other sensor data. The drivers’ navigation system use data were coded from the video recordings, which showed how often and for how long the system was activated and how often and for how long a driver operated the system. Results: The system was activated for 23% of trips, predominantly on longer and unique trips. Analyses of the percentage of time for which the speed limit was exceeded showed no evidence of differences between trips for which the navigation system was used or not used. On trips for which the navigation system was activated, participants spent about 5% of trip time interacting with the device. About 40% of interacting behavior took place in the first 10% of the trip time, and about 35% took place while the car was standing still or moving at a very low speed; that is, 0–10 km/h. Conclusion: These results shed light on how and when drivers use navigation systems. They suggest that although drivers regulate their use of such systems to some extent, they often perform risky tasks while driving.

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This study investigated cycling performance of middle-aged (30–45 years old; n = 30) versus older (65+ years; n = 31) participants during low-speed tasks for which stabilization skills are known to be important. Additionally, participants’ self-ratings of their cycling skills and performance were assessed. Participants rode once on a conventional bicycle and once on a pedelec, in counterbalanced order. Three standardized tasks were performed: (1) low-speed cycling, (2) acceleration from a standstill, and (3) shoulder check. During Tasks 1 and 3, the mean absolute steering angle (a measure of the cyclist's steering activity) and the mean absolute roll rate (a measure of the amount of angular movement of the frame) were significantly greater for older participants than for middle-aged participants. These large lateral motions among older cyclists may indicate a difficulty to control the inherently unstable system. Comparing the conventional bicycle and the pedelec, participants reached a 16 km/h threshold speed in Task 2 sooner on the pedelec, an effect that was most pronounced among the older participants. Correlations between skills assessed with the Cycling Skill Inventory and actual measures of cycling performance were mostly not statistically significant. This indicates that self-reported motor-tactical and safety skills are not strongly predictive of measures of actual cycling performance. Our findings add to the existing knowledge on self-assessment of cycling skills, and suggest that age-related changes in psychomotor and sensory functions pose hazards for cycling safety.

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