The driver response process in assisted and automated driving

Background: Safe assisted and automated driving can be achieved through a detailed understanding of the driver response process (the timing and quality of driver actions and visual behavior) triggered by an event such as a take-over request or a safety-relevant event. Importantly, most current evidence on driver response process in vehicle automation, and on automation effects (unsafe response process) is based on driving-simulator studies, whose results may not generalize to the real world. Objectives: To improve our understanding of the driver response process 1) in automated driving, which takes full responsibility for the driving task but assumes the driver is available to resume manual control upon request and 2) assisted driving, which supports the driver with longitudinal and lateral control but assumes the driver is responsible for safe driving at all times. Method: Data was collected in four experiments on a test track and public roads using the Wizard-of-Oz approach to simulate vehicle automation (assisted or automated). Results: The safety of the driver responses was found to depend on the type of vehicle automation. While a notable number of drivers crashed with a conflict object after experiencing highly reliable assisted driving, an automated driving function that issued a take-over request prior to the same event reduced the crash rate to zero. All participants who experienced automated driving were able to respond to the take-over requests and to potential safety-relevant events that occurred after automation deactivation. The responses to the take-over requests consisted of actions such as looking toward the instrument cluster, placing the hands on the steering wheel, deactivating automation, and moving the feet to the pedals. The order and timing of these actions varied among participants. Importantly, it was observed that the driver response process after receiving a take-over request included several off-path glances; in fact, drivers showed reduced visual attention to the forward road (compared to manual driving) for up to 15 s after the take-over request. Discussion: Overall, the work in this thesis could not confirm the presence of severe automation effects in terms of delayed response and a degraded intervention performance in safety-relevant events previously observed in driving simulators after automated driving. These differing findings likely stem from a combination of differences in the test environments and in the assumptions about the capabilities of the automated driving system. Conclusions: Assisted driving and automated driving should be designed separately: what is unsafe for assisted driving is not necessarily unsafe for automated driving and vice versa. While supervising drivers may crash in safety-relevant events without prior notification during highly reliable assisted driving, a clear and timely take-over request in automated driving ensures that drivers understand their responsibilities of acting in events when back in manual driving. In addition, when take-over requests are issued prior to the event onset, drivers generally perform similar manual driving and intervention performance as in a baseline. However, both before and just after the take-over requests, several drivers directed their gaze mainly off-road. Therefore, it is essential to consider the effect of take-over request designs not only on the time needed to deactivate automation, but also on drivers’ visual behavior. Overall, by reporting the results of tests of a future automated driving system (which is in line with future vehicle regulations and insurance company definitions) in realistic environments, this thesis provides novel findings that enhance the picture of automation effects that, before this thesis, seemed more severe

The automation effect: Investigating factors that influence the driver response process in a safety-relevant event during assisted driving and after unsupervised automation

Introduction: Safe vehicle automation can be achieved through a detailed understanding of drivers’ ability to respond to a safety-relevant event after a period of automated driving. For instance, there is a need to understand in which scenarios automation effects are present (e.g. delayed response, degraded driving performance, crashing). Further, there is a need to identify specific factors (e.g. test environment, system-prompts, hands-on-wheel requirement, automation duration) that contribute to or prevent these automation effects. Objectives: The aim of this thesis is to investigate factors that influence: (a) automation effects in a non-prompted (i.e. absence of warning/notification) safety-relevant event during assisted driving and (b) automation aftereffects (i.e. automation effects specifically occurring after automation has been deactivated) in a prompted safety-relevant event during unsupervised automation. Method: Two Wizard-of-Oz test-track experiments were performed in order to investigate the driver response process in safety-relevant events. In experiment 1, the drivers were required to supervise (with or without a hands-on-wheel requirement) an assisted driving system, and then respond to a safety-relevant event that was not prompted by the system. In experiment 2, the drivers drove manually (baseline) and with an unsupervised automation system (a short and a long duration) before encountering a safety-relevant event. The automation system prompted (issued a take-over request) the driver to resume manual driving shortly before the safety-relevant event became visible. Results: In experiment 1, one third of the drivers responded late, or did not act at all, and crashed in the non-prompted safety-relevant event. In fact, the drivers crashed to the same extent and responded similarly independent of if they supervised the assisted driving system with or without hands on the wheel. In experiment 2, all drivers resumed manual control and did not collide in the safety-relevant event, both after a short and a long automation duration. All drivers showed a similar response and driving performance in the safety-relevant event for both long and short automation duration as well as in the manual baseline. Discussion: A hands-on-wheel requirement was not found to prevent late response or crashing in a non-prompted safety-relevant event encountered during assisted driving. More work is needed to understand the potential safety-benefits of a hands-on-wheel requirement in other types of conflicts and for driver distractions. The finding of minor automation aftereffects in experiment 2 contrasts to previous driving simulator studies. The reason may be the different test environments but is more likely due to different timings for prompting the drivers to resume manual control in relation to when the safety-relevant event became visible. Conclusions: Safe vehicle automation, including both assisted and unsupervised automation, can be achieved in a realistic environment (test track) for most drivers. However, assisted driving in combination with a non-prompted safety-relevant event, can be detrimental for safety, since some drivers may not understand the need to respond to avoid a crash. In fact, a hands-on-wheel requirement did not result in earlier steering responses nor did it prevent drivers from crashing. Thus, more work is needed to understand how to make sure drivers understand the need to respond in non-prompted safety-relevant events during assisted driving. In fact, it seems that when automation has matured to a level when it can prompt the drivers (i.e. unsupervised automation that can issue a take-over request) prior to a safety-relevant event becomes visible, drivers are able to safely resume manual control and perform similar as after an extended period of manual driving. Such safe driving performance seems to be independent of automation durations below 15 minutes

Development and Validation of a Generic Finite Element Ribcage to be used for Strain-based Fracture Prediction

Finite element human body models, comprising detailed anatomical descriptions, can complementanthropomorphic test devices (ATDs) in the development of new restraint systems. Human body models (HBMs)can evaluate injury on tissue level, i.e. rib strain can be used to evaluate the risk of rib fracture, although theHBM must accurately predict the rib strain distribution to be effective. Current HBMs are not validated for ribstrain, and it remains unknown if any represent an average-shaped ribcage. Thus, a new generic ribcage wascreated, representing an average male, based on a combination of averaged geometrical and material data fromin-vivo and in-vitro datasets. The ribcage was incorporated into the THUMS AM50 Version 3, resulting in theSAFER HBM Version 9. Validation of ribcage kinetic, kinematics and strain distribution was carried out at threelevels of complexity: anterior-posterior rib bending tests; rigid impactor table-top test; and a 40 km/h frontalsled test. The rib strains in the single rib load case were predicted within \ub1 one standard deviation for 91% of themeasuring points. The biofidelity for the rib strains in the table-top and sled test load cases was deemed ‘fair’using CORA analysis. This study is an important step in the development and validation process of strain-basedrib fracture criteria for HBMs

PreS2-TML peptide or guanidinium modified Gd-DOTA exhibits efficient cellular uptake

The majority of magnetic resonance contrast agents are restricted to the extracellular domains. For the development of novel, intracellular magnetic resonance contrast agents, we have designed Gd-DOTA derivatives comprising PreS2-TML peptide or ethylguanidinium as carrier moiety. Initial in vitro cell uptake studies with Jurkat cells revealed efficient contrast agent uptake for imaging purposes, in the range of 0.04 fmol/cell (PreS2-TML peptide) to 0.2 fmol/cell (ethylguanidinium) following 2 h incubations at 100 µM

Generic finite element models of human ribs, developed and validated for stiffness and strain prediction - To be used in rib fracture risk evaluation for the human population in vehicle crashes

To enable analysis of the risk of occupants sustaining rib fractures in a crash, generic finite element models of human ribs, one through twelve, were developed. The generic ribs representing an average sized male, were created based on data from several sources and publications. The generic ribs were validated for stiffness and strain predictions in anterior-posterior bending. Essentially, both predicted rib stiffness and rib strain, measured at six locations, were within one standard deviation of the average result in the physical tests. These generic finite elements ribs are suitable for strain-based rib fracture risk predictions, when loaded in anterior-posterior bending. To ensure that human variability is accounted for in future studies, a rib parametric study was conducted. This study shows that the rib cross-sectional height, i.e., the smallest of the cross-sectional dimensions, accounted for most of the strain variance during anterior-posterior loading of the ribs. Therefore, for future rib fracture risk predictions with morphed models of the human thorax, it is important to accurately address rib cross-sectional height

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

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