Showing papers by "Joost C. F. de Winter published in 2020"
TL;DR: It is shown that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level, and it is concluded that the generalizable DRF model is scientifically satisfying and has applications in automated vehicles.
Abstract: Current driving behaviour models are designed for specific scenarios, such as curve driving, obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse scenarios. Can we find an underlying principle from which driving behaviour in different scenarios emerges? We propose the Driver’s Risk Field (DRF), a two-dimensional field that represents the driver’s belief about the probability of an event occurring. The DRF, when multiplied with the consequence of the event, provides an estimate of the driver’s perceived risk. Through human-in-the-loop and computer simulations, we show that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level. The DRF model predictions concur with driving behaviour reported in literature for seven different scenarios (curve radii, lane widths, obstacle avoidance, roadside furniture, car-following, overtaking, oncoming traffic). We conclude that our generalizable DRF model is scientifically satisfying and has applications in automated vehicles.
70 citations
TL;DR: This work examines the continuing use of subjective workload responses to index an operator’s state, either by themselves or as part of a collective suite of measurements, and considers three possible solutions to divergence.
Abstract: We examine the continuing use of subjective workload responses to index an operator’s state, either by themselves or as part of a collective suite of measurements. Lack of convergence of subjective...
61 citations
TL;DR: In this paper, the authors investigate pedestrians' misuse of an automated vehicle (AV) equipped with an external human-machine interface (eHMI), which occurs when a pedestrian enters the road because of uncriti...
Abstract: ObjectiveTo investigate pedestrians’ misuse of an automated vehicle (AV) equipped with an external human–machine interface (eHMI). Misuse occurs when a pedestrian enters the road because of uncriti...
42 citations
TL;DR: This study aims to quantitatively explain driver behavior while avoiding obstacles on a straight road, and quantify the 'Driver's Risk Field' (DRF), and shows that the propagation of the width of the DRF, along the longitudinal distance, resembled an hourglass shape.
20 citations
11 Oct 2020
TL;DR: Crowdourcing experiments with images depicting an automated vehicle equipped with an eHMI in the form of a rectangular display on the front bumper concluded that eHMIs should be green if the eH MI is intended to signal ‘please cross’, but green and red should be avoided if theeHMI is intendedto signal “please do NOT cross”.
Abstract: Future automated vehicles may be equipped with external human-machine interfaces (eHMIs) capable of signaling to pedestrians whether or not they can cross the road. There is currently no consensus on the correct colors for eHMIs. Industry and academia have already proposed a variety of eHMI colors, including red and green, as well as colors that are said to be neutral, such as cyan. A confusion that can arise with red and green is whether the color refers to the pedestrian (egocentric perspective) or the automated vehicle (allocentric perspective). We conducted two crowdsourcing experiments (N = 2000 each) with images depicting an automated vehicle equipped with an eHMI in the form of a rectangular display on the front bumper. The eHMI had one out of 729 colors from the RGB spectrum. In Experiment 1, participants rated the intuitiveness of a random subset of 100 of these eHMIs for signaling ‘please cross the road’, and in Experiment 2 for ‘please do NOT cross the road’. The results showed that for ‘please cross’, colors close to pure green were considered the most intuitive. For ‘please do NOT cross’, colors close to pure red were rated as the most intuitive, but with high standard deviations among participants. In addition, some participants rated green colors as intuitive for ‘please do NOT cross’. Results were consistent for men and women and for colorblind and non-colorblind persons. It is concluded that eHMIs should be green if the eHMI is intended to signal ‘please cross’, but green and red should be avoided if the eHMI is intended to signal ‘please do NOT cross’. Various neutral colors can be used for that purpose, including cyan, yellow, and purple.
20 citations
TL;DR: In this paper, 32 participants watched animated video clips from a driver's perspective while their eyes were recorded using eye-tracking equipment and found that the hazard situations were experienced as more dangerous than the non-hazard situations, as inferred from self-reported danger and pupil diameter.
Abstract: In highly automated driving, drivers occasionally need to take over control of the car due to limitations of the automated driving system Research has shown that visually distracted drivers need about 7 s to regain situation awareness (SA) However, it is unknown whether the presence of a hazard affects SA In the present experiment, 32 participants watched animated video clips from a driver’s perspective while their eyes were recorded using eye-tracking equipment The videos had lengths between 1 and 20 s and contained either no hazard or an impending crash in the form of a stationary car in the ego lane After each video, participants had to (1) decide (no need to take over, evade left, evade right, brake only), (2) rate the danger of the situation, (3) rebuild the situation from a top-down perspective, and (4) rate the difficulty of the rebuilding task The results showed that the hazard situations were experienced as more dangerous than the non-hazard situations, as inferred from self-reported danger and pupil diameter However, there were no major differences in SA: hazard and non-hazard situations yielded equivalent speed and distance errors in the rebuilding task and equivalent self-reported difficulty scores An exception occurred for the shortest time budget (1 s) videos, where participants showed impaired SA in the hazard condition, presumably because the threat inhibited participants from looking into the rear-view mirror Correlations between measures of SA and decision-making accuracy were low to moderate It is concluded that hazards do not substantially affect the global awareness of the traffic situation, except for short time budgets
19 citations
TL;DR: A novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner shows that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration is optimal.
Abstract: In highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal.
17 citations
TL;DR: Investigating how riders perform an emergency braking maneuver in response to an oncoming car and whether longitudinal motion cues provided by a motion platform influence riders' braking performance showed that the more dangerous the situation, the more likely riders were to initiate braking.
14 citations
30 Sep 2020
TL;DR: Performance at the IT task is affected by task familiarity and involves motor activity in the form of blinking, and visual illusions may be an epiphenomenon of understanding the ITtask.
Abstract: In the inspection time (IT) paradigm, participants view two lines of unequal length (called the Pi-figure) for a short exposure time, and then judge which of the two lines was longer. Early research has interpreted IT as a simple index of mental speed, which does not involve motor activity. However, more recent studies have associated IT with higher-level cognitive mechanisms, including focused attention, task experience, and the strategic use of visual illusions. The extent to which these factors affect IT is still a source of debate. We used an eye-tracker to capture participants’ (N = 147) visual attention while performing IT trials. Results showed that blinking was time-dependent, with participants blinking less when the Pi-figure was visible as compared to before and after. Blinking during the presentation of the Pi-figure correlated negatively with response accuracy. Also, participants who reported seeing a brightness illusion had a higher response accuracy than those who did not. The first experiment was repeated with new participants (N = 159), enhanced task instructions, and the inclusion of practice trials. Results showed substantially improved response accuracy compared to the first experiment, and no significant difference in response accuracy between those who did and did not report illusions. IT response accuracy correlated modestly (r = 0.18) with performance on a short Raven’s advanced progressive matrices task. In conclusion, performance at the IT task is affected by task familiarity and involves motor activity in the form of blinking. Visual illusions may be an epiphenomenon of understanding the IT task.
9 citations
TL;DR: Preliminary feasibility is demonstrated of DMS designs that incorporate driving context information for distraction assessment and suppress their status indication that enable benefits for automated driving as a backup.
Abstract: Objective: We investigated a driver monitoring system (DMS) designed to adaptively back up distracted drivers with automated driving. Background: Humans are likely inadequate for supervising today’s on-road driving automation. Conversely, backup concepts can use eye-tracker DMS to retain the human as the primary driver and use computerized control only if needed. A distraction DMS where perceived false alarms are minimized and the status of the backup is unannounced might reduce problems of distrust and overreliance, respectively. Experimental research is needed to assess the viability of such designs. Methods: In a driving simulator, 91 participants either supervised driving automation (auto-hand-on-wheel vs. auto-hands-off-wheel), drove with different forms of DMS-induced backup control (eyes-only-backup vs. eyes-plus-context-backup; visible-backup vs. invisible-backup), or drove without any automation. All participants performed a visual N-back task throughout. Results: Supervised driving automation increased visual distraction and hazard non-responses compared to backup and conventional driving. Auto-hand-on-wheel improved response generation compared to auto-hands-off-wheel. Across entire driving trials, the backup improved lateral performance compared to conventional driving. Without negatively impacting safety, the eyes-plus-context-backup DMS reduced unnecessary automated control compared to the eyes-only-backup DMS conditions. Eyes-only-backup produced low satisfaction ratings, whereas eyes-plus-context-backup satisfaction was on par with automated driving. There were no appreciable negative consequences attributable to the invisible-backup driving automation. Conclusions: We have demonstrated preliminary feasibility of DMS designs that incorporate driving context information for distraction assessment and suppress their status indication. Application: An appropriately designed DMS can enable benefits for automated driving as a backup.
8 citations
TL;DR: A vibrotactile interface that communicates spatiotemporal information about surrounding vehicles and encodes a representation of spatial uncertainty in a novel way and successfully extended the operating range of the assistance system is designed.
Abstract: With the rise of partially automated cars, drivers are more and more required to judge the degree of responsibility that can be delegated to vehicle assistant systems. This can be supported by utilizing interfaces that intuitively convey real-time reliabilities of system functions such as environment sensing. We designed a vibrotactile interface that communicates spatiotemporal information about surrounding vehicles and encodes a representation of spatial uncertainty in a novel way. We evaluated this interface in a driving simulator experiment with high and low levels of human and machine confidence respectively caused by simulated degraded vehicle sensor precision and limited human visibility range. Thereby we were interested in whether drivers (i) could perceive and understand the vibrotactile encoding of spatial uncertainty, (ii) would subjectively benefit from the encoded information, (iii) would be disturbed in cases of information redundancy, and (iv) would gain objective safety benefits from the encoded information. To measure subjective understanding and benefit, a custom questionnaire, Van der Laan acceptance ratings and NASA TLX scores were used. To measure the objective benefit, we computed the minimum time-to-contact as a measure of safety and gaze distributions as an indicator for attention guidance. Results indicate that participants were able to understand the encoded uncertainty and spatiotemporal information and purposefully utilized it when needed. The tactile interface provided meaningful support despite sensory restrictions. By encoding spatial uncertainties, it successfully extended the operating range of the assistance system.
TL;DR: The present regression equations establish quantifiable relations between visible driving scene components with both subjective effort and objective eye movement measures that can help inform road-facing and driver-facing cameras to jointly establish the readiness of would-be drivers ahead of receiving control.
Abstract: For transitions of control in automated vehicles, driver monitoring systems (DMS) may need to discern task difficulty and driver preparedness. Such DMS require models that relate driving scene components, driver effort, and eye measurements. Across two sessions, 15 participants enacted receiving control within 60 randomly ordered dashcam videos (3-second duration) with variations in visible scene components: road curve angle, road surface area, road users, symbols, infrastructure, and vegetation/trees while their eyes were measured for pupil diameter, fixation duration, and saccade amplitude. The subjective measure of effort and the objective measure of saccade amplitude evidenced the highest correlations (r = 0.34 and r = 0.42, respectively) with the scene component of road curve angle. In person-specific regression analyses combining all visual scene components as predictors, average predictive correlations ranged between 0.49 and 0.58 for subjective effort and between 0.36 and 0.49 for saccade amplitude, depending on cross-validation techniques of generalization and repetition. In conclusion, the present regression equations establish quantifiable relations between visible driving scene components with both subjective effort and objective eye movement measures. In future DMS, such knowledge can help inform road-facing and driver-facing cameras to jointly establish the readiness of would-be drivers ahead of receiving control.
TL;DR: Results show that the reported execution of merging varies substantially between drivers, and recommend the introduction of driver support and automation systems which facilitate cooperative behaviour and support effective communication.
Abstract: Freeway merging of heavy goods vehicles (HGV) is a safety–critical manoeuvre. However, at present, it is largely unknown how HGV drivers perceive and execute the merging manoeuvre, and how current advanced driver support and automation systems (ADAS) contribute. We performed semi-structured in-depth interviews with 15 HGV drivers to assess their visual and cognitive processes while merging, interactions with other road users, and attitudes towards ADAS as a basis for future support and automation system design. Results show that the reported execution of merging varies substantially between drivers. Drivers reported reliance on courtesy of other traffic but stated that car drivers are often causing conflicts, whereas other HGV drivers are more cooperative. Current ADAS were perceived as useful in general, with remarks about misuse and abundance of systems. We recommend the introduction of driver support and automation systems which facilitate cooperative behaviour and support effective communication.
22 Jul 2020
TL;DR: Shallow conflict angles yield the best performance, an effect that can be explained using basic perceptual heuristics, such as the ‘closer is first’ strategy, and displays should provide continuous rather than discrete update rates.
Abstract: In many domains, including air traffic control, observers have to detect conflicts between moving objects. However, it is unclear what the effect of conflict angle is on observers’ conflict detection performance. In addition, it has been speculated that observers use specific viewing techniques while performing a conflict detection task, but evidence for this is lacking. In this study, participants (N = 35) observed two converging objects while their eyes were recorded. They were tasked to continuously indicate whether a conflict between the two objects was present. Independent variables were conflict angle (30, 100, 150 deg), update rate (discrete, continuous), and conflict occurrence. Results showed that 30 deg conflict angles yielded the best performance, and 100 deg conflict angles the worst. For 30 deg conflict angles, participants applied smooth pursuit while attending to the objects. In comparison, for 100 and especially 150 deg conflict angles, participants showed a high fixation rate and glances towards the conflict point. Finally, the continuous update rate was found to yield shorter fixation durations and better performance than the discrete update rate. In conclusion, shallow conflict angles yield the best performance, an effect that can be explained using basic perceptual heuristics, such as the ‘closer is first’ strategy. Displays should provide continuous rather than discrete update rates.