Task Interruption and Control Recovery Strategies After Take-Over Requests Emphasize Need for Measures of Situation Awareness.

OBJECTIVE: Our objective was to determine whether there is a need to go beyond measures of automation deactivation time to understand the transition to manual driving after take-over requests (TORs) using the example of office tasks as nondriving-related tasks (NDRTs). BACKGROUND: Office tasks are likely NDRTs during automated commutes to/from work. Complex tasks can influence how manual control and visual attention is recovered after TORs. METHOD: N = 51 participants in a driving simulator performed either one of two office tasks or no task (between subjects). We recorded reaction times in a high-urgency and low-urgency scenario (within subjects) and analyzed task interruption strategies. RESULTS: 90% of the participants who performed an NDRT deactivated the automation after 7 to 8 s. However, 90% of the same drivers looked at the side mirror for the first time only after 11 to 14 s. Drivers with office tasks either interrupted the tasks sequentially or in parallel. Strategies were not adapted to the take-over situation or the task but appeared to be due to individual preferences. CONCLUSION: Drivers engaged in NDRTs may neglect lower priority subtasks after a TOR, such as mirror checking. Therefore, there is a need to go beyond measures of automation deactivation time to understand the transition to manual driving. Using analyses of attentional dynamics during take-over situations may enhance the safety of future car-driver handover assistance systems. APPLICATION: If low driver availability is detected, TORs should only be used as a fallback option if sufficient time and adaptive driver support can be provided.

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures.

OBJECTIVE: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. BACKGROUND: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. METHOD: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. RESULTS: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. CONCLUSION: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. APPLICATION: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach.

OBJECTIVE: The paper presents a systematic analysis of drivers' crash avoidance response during crashes and near-crashes and developed a machine learning-based predictive model that can determine driver maneuver using pre-incident driver behavior and driving context. METHODS: We analyzed 286 naturalistic rear-end crashes and near-crashes from the SHRP2 naturalistic driving study. All the events were manually reduced using face video (face and forward) and kinematic responses. In this paper, we developed new reduction variables that enhanced the understanding of drivers' gaze behavior and roadway attention behavior during these events. These features reflected how the event criticality, measured using time to collision, related to drivers' pre-incident behavior (secondary behavior, gaze behavior), and drivers' perception of the event (physical reaction and maneuver). The imperative understanding of such relations was validated using a random forest- (RF) based classifier, which efficiently predicted if a driver was going to brake or change the lane as an avoidance maneuver. RESULTS: The RF presented in this paper effectively explored the nonlinear patterns in the data and was highly accurate (∼96 %) in its prediction. A further analysis of the RF model showed that six features played a pivotal role in the decision logic. These included the drivers' last glance duration before the event, last glance eccentricity, duration of 'eyes on road' immediately before the event, the time instance and criticality when the driver perceives the threat as well as acknowledge the threat, and possibility of an escape path in the adjacent lane. Using partial dependency plots, we also showed how different thresholds of these feature variables determined the drivers' maneuver intention. CONCLUSIONS: In this paper we analyzed driving context, drivers' behavior, event criticality, and drivers' response in a unified structure to predict their avoidance response. To the best of our knowledge, this is the first such effort where large-scale naturalistic data (crashes and near crashes) was analyzed for prediction of drivers' maneuver and determined key behavioral and contextual factors that contribute to this avoidance maneuver.

Asleep at the automated wheel-Sleepiness and fatigue during highly automated driving.

Due to the lack of active involvement in the driving situation and due to monotonous driving environments drivers with automation may be prone to become fatigued faster than manual drivers (e.g. Schömig et al., 2015). However, little is known about the progression of fatigue during automated driving and its effects on the ability to take back manual control after a take-over request. In this driving simulator study with Nö=ö60 drivers we used a three factorial 2ö×ö2ö×ö12 mixed design to analyze the progression (12ö×ö5ömin; within subjects) of driver fatigue in drivers with automation compared to manual drivers (between subjects). Driver fatigue was induced as either mainly sleep related or mainly task related fatigue (between subjects). Additionally, we investigated the drivers' reactions to a take-over request in a critical driving scenario to gain insights into the ability of fatigued drivers to regain manual control and situation awareness after automated driving. Drivers in the automated driving condition exhibited facial indicators of fatigue after 15 to 35ömin of driving. Manual drivers only showed similar indicators of fatigue if they suffered from a lack of sleep and then only after a longer period of driving (approx. 40ömin). Several drivers in the automated condition closed their eyes for extended periods of time. In the driving with automation condition mean automation deactivation times after a take-over request were slower for a certain percentage (about 30%) of the drivers with a lack of sleep (Mö=ö3.2; SDö=ö2.1ös) compared to the reaction times after a long drive (Mö=ö2.4; SDö=ö0.9ös). Drivers with automation also took longer than manual drivers to first glance at the speed display after a take-over request and were more likely to stay behind a braking lead vehicle instead of overtaking it. Drivers are unable to stay alert during extended periods of automated driving without non-driving related tasks. Fatigued drivers could pose a serious hazard in complex take-over situations where situation awareness is required to prepare for threats. Driver fatigue monitoring or controllable distraction through non-driving tasks could be necessary to ensure alertness and availability during highly automated driving.