A human factors perspective on automated driving Kyriakidis, M.; de Winter, J. C. F.; Stanton, N. ...
Theoretical issues in ergonomics science,
05/2019, Letnik:
20, Številka:
3
Journal Article
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Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews ...were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AV's capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees' statements - which appear to be in line with a long history of HFs research - and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.
Introduction: Many bicycle–car crashes are caused by the fact that the driver fails to give right of way to the cyclist. Although the car driver is to blame, the cyclist may have been able to prevent ...the crash by anticipating the safety-critical event and slowing-down. This study aimed to understand how accurate cyclists are in predicting a driver's right-of-way violation, which cues contribute to cyclists' predictions, and which factors contribute to their self-reported slowing-down behavior as a function of the temporal proximity to the conflict. Method: 1030 participants were presented with video clips of nine safety-critical intersection situations, with five different video freezing moments in a between-subjects design. After each video clip, participants completed a questionnaire to indicate what the car driver will do next, which bottom-up and top-down cues they think they used, as well as their intended slowing-down behavior and perceived risk. Results andconclusions: The results showed that participants' predictions of the driver's behavior develop over time, with more accurate predictions (i.e., reporting that the driver will not let the cyclist cross first) at later freezing moments. A regression analysis showed that perceived high speed and acceleration of the car were associated with correctly predicting that the driver will not let the cyclist cross first. Incorrect predictions were associated with believing that the car has a low speed or is decelerating, and with reporting that the cyclist has right of way. Correctly predicting that the driver will not let the cyclist cross first and perceived risk were significant predictors of intending to slow down in safety-critical intersection situations. Practical applications: Our findings add to the existing knowledge on cyclists' hazard anticipation and could be used for the development of training programs as well as for cycling support systems.
•Participants viewed videos of bicycle–car conflicts from the cyclist's perspective.•Participants were asked whether the driver would let the cyclist cross first.•Prediction accuracy was higher when the cyclist was closer to the conflict.•Perceived low speed of the car was related to incorrect predictions.•Correct predictions and perceived risk were associated with cyclists' slowing-down.
•Age differences in riding performance were investigated for three low-speed tasks.•Participants rode a pedelec and a conventional bicycle, and filled out self-reports.•Riders aged 65 years and over ...showed larger lateral motions than middle-aged riders.•Older cyclists in particular accelerated faster on a pedelec than on a normal bicycle.•Correlations between self-reported skills and actual cycling performance were small.
This study investigated cycling performance of middle-aged (30–45yearsold; 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 1and3, 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 16km/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.
•We investigated social identification among self-described car drivers and cyclists.•645 respondents completed a web-based survey.•Road users report having more in common with their “ingroup” than ...their “outgroup”.•Cyclists were less expectant of car drivers yielding to them than car drivers were.•Car drivers’ attributions style may lead them to react unfavorably to cyclists’ errors.
Research in different domains has shown that people categorize oneself and others as ingroup (“us”) and outgroup (“them”) members, resulting in group-based stereotyping and attribution errors that may adversely affect social behaviour. To determine whether such patterns also exist in road traffic, we conducted an experimental web-based survey using scenarios of unregulated traffic settings in which the type of other road user was varied (cyclist vs. car driver). We investigated whether road users who described themselves predominantly as either a car driver (N = 330) or a cyclist (N = 315) would (1) report having more in common with members of their respective ingroups than outgroups, (2) be more negative about their respective outgroup than ingroup in terms of their expectations about other road users, (3) make more dispositional and less circumstantial attributions about an outgroup member who failed to yield right of way than about an ingroup member, and (4) show more willingness to raise traffic fines for the outgroup than for the ingroup. Results showed both self-described car drivers and cyclists reported having more in common with their ingroup than with their outgroup. Self-described car drivers were also least inclined to expect to be given right of way by cyclists as compared to car drivers, while self-described cyclists were less inclined than self-described car drivers to expect car drivers to yield right of way. Self-described car drivers were more inclined to make dispositional attributions about cyclists’ rule breaking behaviour and less inclined to attribute these to circumstances compared to rule breaking on the part of car drivers, and were most inclined to disadvantage their outgroup compared to their ingroup in terms of raising traffic fines. Since dispositional attributions are more likely to lead people to behave aggressively, our findings suggest that cyclists, who are arguably among those most dependent on the goodwill and forgivingness of drivers of motorised vehicles, may be less likely to receive it. This means that although both self-described cyclists and car drivers may distinguish between ingroups and outgroups in traffic, this distinction may have much more complicated implications than the simple terms “us” and “them” might imply.
•Cyclists’ eye movements at uncontrolled intersections were investigated.•Car approach scenario, traffic complexity, and cycling speed were manipulated.•Cyclists looked at the approaching car, ...especially when it was potentially hazardous.•Traffic complexity resulted in divided attention between two cars.•Effects of cycling speed on cyclists’ gaze behavior were small to moderate.
Research indicates that crashes between a cyclist and a car often occur even when the cyclist must have seen the approaching car, suggesting the importance of hazard anticipation skills. This study aimed to analyze cyclists’ eye movements and crossing judgments while approaching an intersection at different speeds. Thirty-six participants watched animated video clips with a car approaching an uncontrolled four-way intersection and continuously indicated whether they would cross the intersection first. We varied (1) car approach scenario (passing, colliding, stopping), (2) traffic complexity (one or two approaching cars), and (3) cyclist’s approach speed (15, 25, or 35 km/h). Results showed that participants looked at the approaching car when it was relevant to the task of crossing the intersection and posed an imminent hazard, and they directed less attention to the car after it had stopped or passed the intersection. Traffic complexity resulted in divided attention between the two cars, but participants retained most visual attention to the car that came from the right and had right of way. Effects of cycling speed on cyclists’ gaze behavior and crossing judgments were small to moderate. In conclusion, cyclists’ visual focus and crossing judgments are governed by situational factors (i.e., objects with priority and future collision potential), whereas cycling speed does not have substantial effects on eye movements and crossing judgments.
A large portion of road traffic crashes occur at intersections for the reason that drivers lack necessary visual information. This research examined the effects of an audio-visual display that ...provides real-time sonification and visualization of the speed and direction of another car approaching the crossroads on an intersecting road. The location of red blinking lights (left vs. right on the speedometer) and the lateral input direction of beeps (left vs. right ear in headphones) corresponded to the direction from where the other car approached, and the blink and beep rates were a function of the approaching car's speed. Two driving simulators were linked so that the participant and the experimenter drove in the same virtual world. Participants (N = 25) completed four sessions (two with the audio-visual display on, two with the audio-visual display off), each session consisting of 22 intersections at which the experimenter approached from the left or right and either maintained speed or slowed down. Compared to driving with the display off, the audio-visual display resulted in enhanced traffic efficiency (i.e., greater mean speed, less coasting) while not compromising safety (i.e., the time gap between the two vehicles was equivalent). A post-experiment questionnaire showed that the beeps were regarded as more useful than the lights. It is argued that the audio-visual display is a promising means of supporting drivers until fully automated driving is technically feasible.
•An in-vehicle audio-visual display informed participants about another driver approaching the intersection.•The display provided beeping and blinking feedback as a function of the other driver's speed and direction of approach.•Two driving simulators were linked, allowing the participant and experimenter to encounter each other in the virtual world.•Participants drove faster when the display was switched on. Interactions were not less safe, however.•Participants found the beeps more useful than the blinking lights.
•The role of auditory perception for cycling safety is explored.•Music and phone conversation negatively affect perception of traffic sounds.•No impact of music or phone conversation on cyclists’ ...involvement in traffic incidents.•Cyclists compensate for listening to music and talking on the phone.•Majority of cyclists never or seldom encounter quiet (electric) cars on the road.
Listening to music or talking on the phone while cycling as well as the growing number of quiet (electric) cars on the road can make the use of auditory cues challenging for cyclists. The present study examined to what extent and in which traffic situations traffic sounds are important for safe cycling. Furthermore, the study investigated the potential safety implications of limited auditory information caused by quiet (electric) cars and by cyclists listening to music or talking on the phone. An Internet survey among 2249 cyclists in three age groups (16–18, 30–40 and 65–70year old) was carried out to collect information on the following aspects: 1) the auditory perception of traffic sounds, including the sounds of quiet (electric) cars; 2) the possible compensatory behaviours of cyclists who listen to music or talk on their mobile phones; 3) the possible contribution of listening to music and talking on the phone to cycling crashes and incidents. Age differences with respect to those three aspects were analysed. Results show that listening to music and talking on the phone negatively affects perception of sounds crucial for safe cycling. However, taking into account the influence of confounding variables, no relationship was found between the frequency of listening to music or talking on the phone and the frequency of incidents among teenage cyclists. This may be due to cyclists’ compensating for the use of portable devices. Listening to music or talking on the phone whilst cycling may still pose a risk in the absence of compensatory behaviour or in a traffic environment with less extensive and less safe cycling infrastructure than the Dutch setting. With the increasing number of quiet (electric) cars on the road, cyclists in the future may also need to compensate for the limited auditory input of these cars.
•Hazard anticipation training for experienced cyclists was developed and evaluated.•The evaluation was conducted among electric bicycle users.•Training group participants detected hazards faster ...compared to the controls.•No effect of the hazard anticipation training on perceived danger and risk was found.
Research shows that the ability to anticipate safety-critical situations is predictive of safe performance in traffic. Thus far, hazard anticipation training has been developed mainly for car drivers. These training programs may not be appropriate for cyclists who are exposed to different types of hazards. This study aimed to develop a PC-based hazard anticipation training for experienced cyclists, and evaluate its short-term effectiveness using hazard anticipation tests. Sixty-six electric bicycle users completed either a hazard anticipation training or a control intervention. The hazard anticipation training consisted of videos divided into two modules (instructions and practice) and was designed using various evidence-based hazard anticipation educational methods such as a ‘What happens next?’ task, expert commentary, performance feedback, and analogical transfer between hazardous traffic situations. The evaluation of the training showed that cyclists from the training group identified hazards faster compared to the control group cyclists, but no significant difference was found in the number of detected hazards between the two groups. The training had a small positive effect on cyclists’ prediction accuracy at safety-critical intersection situations. No effect was found on perceived danger and risk in hazardous traffic situations. Our results suggest that experienced cyclists’ hazard anticipation skills can be improved with the developed PC-based training. Future research should evaluate the retention and transfer of learned skills.
In the present study, the frequency, determinants and consequences of three relevant emotions in traffic were investigated. Based on appraisal theory, it was predicted that the combination of three ...appraisal components (goal congruence, blame and threat) affects the occurrence of anger, anxiety and happiness. Participants (
n
=
44) filled in a questionnaire containing background and personality variables, and performed a test drive in an instrumented car. During the drive, speed and heart rate were registered and the traffic environment was recorded on video. Participants verbally reported scores for emotions and perceived risk. The most frequently occurring emotion was anxiety, followed by anger and happiness. Emotions while driving were related to emotional traits. Emotions while driving were also related to traffic events: anger and anxiety were both associated with goal incongruent events, and happiness with goal congruent events. Anger was mostly associated with other-blame and anxiety with situation-blame. Anger was mostly associated with events affecting impeded progress, and anxiety with events affecting safety. Anxiety, but not anger or happiness, was associated with increased perceived risk and with increased heart rate. Participants who reported anger drove faster and exceeded the speed limit more often on a 100
km/road section than participants who did not report anger. These and other results are discussed in terms of appraisal theory and state-trait differences in emotion.
•Glance behaviour of teenage cyclists while listening to music is explored.•Ethical dilemmas related to performing research in real traffic are presented.•Cyclists’ visual behaviour was not affected ...by listening to music.•Experimental set-up did not allow us to study glance behaviour in complex situations.•We argue for the development of ethical standards for road safety research.
Listening to music while cycling impairs cyclists’ auditory perception and may decrease their awareness of approaching vehicles. If the impaired auditory perception is not compensated by the cyclist himself or other road users involved, crashes may occur. The first aim of this study was to investigate in real traffic whether teenage cyclists (aged 16–18) compensate for listening to music by increasing their visual performance. Research in real traffic may pose a risk for participants. Although no standard ethical codes exist for road safety research, we took a number of ethical considerations into account to protect participants. Our second aim was to present this study as a case study demonstrating ethical dilemmas related to performing research in real traffic. The third aim was to examine to what extent the applied experimental set-up is suitable to examine bicyclists’ visual behaviour in situations crucial for their safety. Semi-naturalistic data was gathered. Participants’ eye movements were recorded by a head-mounted eye-tracker during two of their regular trips in urban environments. During one of the trips, cyclists were listening to music (music condition); during the other trip they were ‘just’ cycling (the baseline condition). As for cyclists’ visual behaviour, overall results show that it was not affected by listening to music. Descriptive statistics showed that 21–36% of participants increased their visual performance in the music condition, while 43–64% decreased their visual performance while listening to music. Due to ethical considerations, the study was therefore terminated after fourteen cyclists had participated. Potential implications of these results for cycling safety and cycling safety research are discussed. The methodology used in this study did not allow us to investigate cyclists’ behaviour in demanding traffic environment. However, for now, no other research method seems suitable to address this research gap.
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