Driving context influences drivers\u27 decision to engage in visual-manual phone tasks: evidence from a naturalistic driving study

Visual-manual phone tasks (i.e., texting, dialing, reading) are associated with anincreased crash risk. This study investigated how the driving context influences drivers\u27 decisions toengage in visual-manual phone tasks in naturalistic driving. Method: Video-recordings of 1432 car tripswere viewed to identify visual-manual phone tasks and passenger presence. Video, vehicle signals, andmap data were used to classify driving context (i.e., curvature, other vehicles) before and during thephone tasks (N=374). Vehicle signals (i.e., speed, yaw rate, forward radar) were available for alldriving. Results: The drivers were more likely to engage in phone tasks while standing still, and lesslikely while driving at high speeds or executing sharp turns, or when a passenger was present. Leadvehicle presence did not influence how likely drivers were to engage, but they adjusted their tasktiming to situations when the lead vehicle was increasing speed, resulting in increasing time headway.The drivers adjusted task timing until after making sharp turns and lane change maneuvers. Incontrast to previous driving simulator studies, there was no evidence of drivers reducing speed as aconsequence of phone task engagement. Conclusions: The results show that experienced drivers areskilled at using information about current and upcoming driving context to decide when to safelyengage in visual-manual phone tasks. However, drivers may fail to sufficiently increase safety marginsto allow time to respond to possible unpredictable events (e.g., lead vehicle braking). PracticalApplications: Advanced driver assistance systems should facilitate and possibly boost drivers\u27 selfregulatingbehavior. For instance, they might recognize when appropriate adaptive behavior is missingand advice or alert accordingly. The results from this study could also inspire training programs fornovice drivers, or locally classify roads in terms of the risk associated with secondary task engagementwhile driving

It’s about time! Earlier take-over requests in automated driving enable safer responses to conflicts

Automated driving (AD), which takes full responsibility for the driving task in certain conditions, is currently being developed. An important concern in AD is how to design a take-over request (TOR) that mitigates automation effects (e.g., delayed responses to conflict scenarios) that previous literature from simulator experiments has shown can occur. To address this concern, this study aims to investigate and compare driver responses to TORs and a lead-vehicle cut-out scenario under three conditions: (1) after a period of AD with a TOR issued early (18 s time-to-collision), (2) same as (1) except with a TOR issued late (9 s time-to-collision), and (3) baseline, with adaptive cruise control (ACC). This paper also compares the results to those of a previous study using the same conflict scenario but with near-perfect assisted driving system (SAE Level 2). The lead-vehicle cut-out scenario was encountered on a test track after 30 minutes driving with either ACC or AD. In AD the TOR was issued prior to the conflict object was revealed to the participants when the lead vehicle performed the cut-out (at conflict onset). This TOR strategy differed from previous driving-simulator studies that issued the TOR at conflict onset. The participants had to respond by steering and/or braking to avoid a crash. Our findings show that, independent of TOR timing, the drivers required similar amounts of time to 1) direct their first glance to the human–machine interface, 2) look forward, 3) end their secondary task, 4) put their hands on the steering wheel, and 5) deactivate automation. However, when the TOR was issued early rather than late, they started to brake earlier (even before conflict onset). All participants successfully managed to avoid crashing with the object, independent of the condition. AD with an early TOR resulted in the earliest response, while ACC drivers responded slightly earlier than the drivers in AD with the late TOR. Our findings do not support the findings of severe automation effects in previous driving-simulator studies. One reason for the difference is that when a TOR is issued prior to conflict onset, drivers are given the time needed for their preparatory actions (e.g., placing hands on the wheel, deactivating AD) that is not needed when driving with ACC or in manual driving (baseline), before having to respond to the conflict scenario. Thus, at conflict onset the drivers in AD are as ready to act (hands on wheel, eyes forward) as the drivers in the baseline and can perform an avoidance manoeuvre similar as to the baseline drive. Overall, the present study shows that AD does not need to end up in a highly critical situation if the TOR is issued early enough. In fact, AD with an early TOR may be safer than driving with ACC, because in the former drivers are more likely to brake earlier in preparation for the conflict. Finally, a TOR clearly communicates the need for drivers to resume manual control and handle potential events when AD has been deactivated. In our study, once the drivers had taken control, they clearly understood their responsibilities to respond to the conflict, in contrast to a previous study with a similar, near-perfect assisted driving system

Driver Visual Attention Before and After Take-Over Requests During Automated Driving on Public Roads

ObjectiveThis study aims to understand drivers’ visual attention before and after take-over requests during automated driving (AD), when the vehicle is fully responsible for the driving task on public roads.BackgroundExisting research on transitions of control from AD to manual driving has mainly focused on take-over times. Despite its relevance for vehicle safety, drivers’ visual attention has received little consideration.MethodThirty participants took part in a Wizard of Oz study on public roads. Drivers’ visual attention was analyzed before and after four take-over requests. Visual attention during manual driving was also recorded to serve as a baseline for comparison.ResultsDuring AD, the participants showed reduced visual attention to the forward road and increased duration of single off-road glances compared to manual driving. In response to take-over requests, the participants looked away from the forward road toward the instrument cluster. Levels of visual attention towards the forward road did not return to the levels observed during manual driving until after 15\ua0s had passed.ConclusionDuring AD, drivers may look toward non-driving related task items (e.g., mobile phone) instead of forward. Further, when a transition of control is required, drivers may take over control before they are aware of the driving environment or potential threat(s). Thus, it cannot be assumed that drivers are ready to respond to events shortly after the take-over request.ApplicationIt is important to consider the effect of the design of take-over requests on drivers’ visual attention alongside take-over times

Driver conflict response during supervised automation: Do hands on wheel matter?

Securing appropriate driver responses to conflicts is essential in automation that is not perfect (because the driver is needed as a fall-back for system limitations and failures). However, this is recognized as a major challenge in the human factors literature. Moreover, in-depth knowledge is lacking regarding mechanisms affecting the driver response process. The first aim of this study was to investigate how driver conflict response while using highly reliable (but not perfect) supervised automation differ for drivers that (a) crash or avoid a conflict object and (b) report high trust or low trust in automation to avoid the conflict object. The second aim was to understand the influence on the driver conflict response of two specific factors: a hands-on-wheel requirement (with vs. without), and the conflict object type (garbage bag vs. stationary vehicle). Seventy-six participants drove with highly reliable but supervised automation for 30 minutes on a test track. Thereafter they needed to avoid a static object that was revealed by a lead-vehicle cut-out. The driver conflict response was assessed through the response process: timepoints for driver surprise reaction, hands-on-wheel, driver steering, and driver braking. Crashers generally responded later in all actions of the response process compared to non-crashers. In fact, some crashers collided with the conflict object without even putting their hands on the wheel. Driver conflict response was independent of the hands-on-wheel requirement. High-trust drivers generally responded later than the low-trust drivers or not at all, and only high trust drivers crashed. The larger stationary vehicle triggered an earlier surprise reaction compared to the garbage bag, while hands-on-wheel and steering response were similar for the two conflict object types. To conclude, crashing is associated with a delay in all actions of the response process. In addition, driver conflict response does not change with a hands-on-wheel requirement but changes with trust-level and conflict object type. Simply holding the hands on the wheel is not sufficient to prevent collisions or elicit earlier responses. High trust in automation is associated with late response and crashing, whereas low trust is associated with appropriate driver response

Automation aftereffects: the influence of automation duration, test track and timings

Automation aftereffects (i.e., degraded manual driving performance, delayed responses, and more aggressive avoidance maneuvers) have been found in driving simulator studies. In addition, longer automation duration seems to result in more severe aftereffects, compared to shorter duration. The extent to which these findings generalize to real-world driving is currently unknown. The present study investigated how automation duration affects drivers\u27 take-over response quality and driving performance in a road-work zone. Seventeen participants followed a lead vehicle on test track. They encountered the road-work zone four times: two times while driving manually, and after a short and a long automation duration. The take-over request was issued before the lead vehicle changed lane to reveal the road-work zone. After both short and long automation durations, all drivers deactivated automation well ahead of the road-work zone. Compared to manual, drivers started their steering maneuvers earlier or at similar times after automation (independently of duration), and none of the drivers crashed. However, slight increases in vehicle speed and accelerations were observed after exposure to automation. In sum, the present study did not observe as large automation aftereffects on the test track as previously found in driving simulator studies. The extent to which these results are a consequence of a more realistic test environment, or due to the duration between the timings for the take-over request and the conflict appearance, is still unknown

Driver response to take-over requests in real traffic

Existing research on control-transitions from automateddriving (AD) to manual driving mainly stems from studiesin virtual settings. There is a need for studies conducted in realsettings to better understand the impacts of increasing vehicleautomation on traffic safety. This study aims specifically to understandhow drivers respond to take-over requests (TORs) in realtraffic by investigating the associations between 1) where driverslook when receiving the TOR, 2) repeated exposure to TORs, and3) the drivers’ response process. In total, thirty participants wereexposed to four TORs after about 5–6 min of driving with AD onpublic roads. While in AD, participants could choose to engage innon-driving-related tasks (NDRTs).When they received the TOR,for 38% of TORs, participants were already looking on path. Forthose TORs where drivers looked off path at the time of the TOR,the off-path glance was most commonly towards an NDRT item.Then, for 72% of TORs (independent on gaze direction), driversstarted their response process to the TOR by looking towardsthe instrument cluster before placing their hands on the steeringwheel and their foot on the accelerator pedal, and deactivatingautomation. Both timing and order of these actions varied amongparticipants, but all participants deactivated AD within 10 s fromthe TOR. The drivers’ gaze direction at the TOR had a strongerassociation with the response process than the repeated exposureto TORs did. Drivers can respond to TORs in real traffic. However,the response should be considered as a sequence of actions thatrequires a certain amount of time

Real World Data on Driver Behaviour in Accidents and Incidents: Evaluating data collection and analysis methods for car safety development

Real world data is important for safety development within the road transportation system. For car safety development in particular, methods to collect and analyse real world data on driver behaviour from normal driving, incidents and accidents are needed to address safety in driving. This thesis investigates what different analysis methods applied to self-report and observation data can provide about driver safety issues (e.g., drowsiness, distraction) in accidents and incidents. Nonresponse analysis and adjustment in an accident mail survey was performed by using insurance data from 8519 survey recipients and mail survey data for the respondents in Paper I. Document case studies were performed for 158 accidents in Paper II by combining accident mail survey questionnaires and insurance documents. In Paper III, an incident causation analysis was performed based on video-recordings of 90 car-to-pedestrian incidents in a naturalistic driving study. The findings imply that self-reported and observation data collection procedures are both required as complementary sources of information for car safety development. Mail surveys can be used as a cost efficient method to collect general information from a large number of accidents as well as information on some driver safety issues. Valuable, additional information about accidents can be obtained by analysing written descriptions from mail survey and insurance documents. This can provide insights into how the driver experienced the accident, facilitate the interpretation of mail survey responses, and provide information that is not captured by the mail survey variables. Video-recordings from naturalistic driving studies can provide detailed information on many driver safety issues. This is especially valuable for aspects of driver behaviour that is difficult to capture with self-report methods. There is ample opportunity to improve the understanding of driver safety issues in accidents and incidents. By combining data from self-reported and recorded events, future studies can improve estimates of the occurrence of different driver safety issues and provide a wider picture of accident and incident causation. A combination of different types of data sources can also be used to further address the validity of accident mail surveys

Real World Data on Driver Behaviour in Accidents and Incidents: Evaluating data collection and analysis methods for car safety development

Real world data is important for safety development within the road transportation system. For car safety development in particular, methods to collect and analyse real world data on driver behaviour from normal driving, incidents and accidents are needed to address safety in driving. This thesis investigates what different analysis methods applied to self-report and observation data can provide about driver safety issues (e.g., drowsiness, distraction) in accidents and incidents. Nonresponse analysis and adjustment in an accident mail survey was performed by using insurance data from 8519 survey recipients and mail survey data for the respondents in Paper I. Document case studies were performed for 158 accidents in Paper II by combining accident mail survey questionnaires and insurance documents. In Paper III, an incident causation analysis was performed based on video-recordings of 90 car-to-pedestrian incidents in a naturalistic driving study. The findings imply that self-reported and observation data collection procedures are both required as complementary sources of information for car safety development. Mail surveys can be used as a cost efficient method to collect general information from a large number of accidents as well as information on some driver safety issues. Valuable, additional information about accidents can be obtained by analysing written descriptions from mail survey and insurance documents. This can provide insights into how the driver experienced the accident, facilitate the interpretation of mail survey responses, and provide information that is not captured by the mail survey variables. Video-recordings from naturalistic driving studies can provide detailed information on many driver safety issues. This is especially valuable for aspects of driver behaviour that is difficult to capture with self-report methods. There is ample opportunity to improve the understanding of driver safety issues in accidents and incidents. By combining data from self-reported and recorded events, future studies can improve estimates of the occurrence of different driver safety issues and provide a wider picture of accident and incident causation. A combination of different types of data sources can also be used to further address the validity of accident mail surveys

Understanding and prioritizing crash contributing factors

Real world data on driver behavior in normal driving and critical situations are essential for car safety development. Data collection and analysis methods that provide insight into the prevalence of crash contributing factors (e.g., drowsiness, distraction) and causation mechanisms are valuable when making priorities and selecting countermeasure principles.This thesis investigates different analysis methods applied to real world data from three sources: a crash mail survey, insurance claims, and naturalistic driving. Several analysis methods were investigated, including: adjusting for nonresponse in a crash mail survey, analyzing narratives provided by the involved road users in a crash, and investigating causation mechanisms based on video recordings of critical situations. Naturalistic driving data from whole trips were analyzed to investigate the influence of driving context (e.g., turning, other vehicles, speed) on drivers’ eye glance behavior and their exposure to visual-manual phone tasks.Insurance data proved useful for compensating for survey nonresponse bias related to crash types and driver demographics, while several crash contributing factors are likely to be underestimated in mail surveys due to issues regarding memory and social desirability. Narratives provided detailed additional information explaining why some of the crashes occurred. Video recordings of critical situations consistently revealed contributing factors related to drivers\u27 visual behavior, the road environment, and the behavior of other road users, although drivers’ own thoughts and low vigilance were not identified. Naturalistic driving data collected continuously from whole trips were found to be an excellent source of information for studying normal driving behavior. Driving context influenced drivers’ eye glance behavior, task timing and overall propensity to engage in visual-manual phone tasks.In conclusion, no single source of real world data is sufficient on its own to prioritize crash types and contributing factors, and to select countermeasure principles. Future development should emphasize the analysis of large datasets from different sources, in order to provide insights into a wide range of crash contributing factors in different types of critical situations, including severe crashes

Dialling, texting, and reading in real world driving: When do drivers choose to use mobile phones?

Mobile phone use is the most debated and studied form of driver distraction. Naturalistic driving studies have shown that the risk of being involved in a near-crash or crash increases during manual and visual interaction with a mobile phone (e.g., when texting or dialling), while just talking on a mobile phone seems neutral or may even have a protective effect. Previous studies involving focus groups and questionnaires present conflicting results about the strategies that drivers use to decide when to engage in mobile phone use. The aim of this study is to analyse naturalistic driving data to determine when drivers decide to engage or disengage in dialling, texting or reading text messages. Video- , map-, and vehicle-data from approximately 300 passenger car trips, in average 15 minutes long, were searched for sequences involving mobile phone use. All sequences, as well as, driving prior to each initiation of mobile phone use, were coded and analysed. Results show that drivers adapt mobile phone use both to the road characteristics and to the presence of other road users. This adaptation includes both proactive behaviour, such as overtaking prior to dialling a number, and reactive behaviour, such as delaying reading a text message until the vehicle exits a curve and enters a straight road segment