From partial and high automation to manual driving: Relationship between non-driving related tasks, drowsiness and take-over performance.

BACKGROUND: Until the level of full vehicle automation is reached, users of vehicle automation systems will be required to take over manual control of the vehicle occasionally and stay fallback-ready to some extent during the drive. Both, drowsiness caused by inactivity and the engagement in distracting non-driving related tasks (NDRTs) such as entertainment or office work have been suggested to impair the driver's ability to safely handle these transitions of control. Thus, it is an open question whether engagement in NDRTs will impair or improve take-over performance. METHOD: In a motion-based driving simulator, 64 participants completed an automated drive that lasted either one or two hours using either a partially or highly automated driving system. In the partially automated driving condition, a warning was issued after several seconds when drivers took both hands off the steering wheel, while the highly automated driving system allowed hands-off driving permanently. Drivers were allowed to bring along their smartphones and to use them during the drive. They engaged in a wide variety of NDRTs such as reading or using social media. At the end of the session, drivers had to react to a sudden lead vehicle braking event. In the partial automation condition, there was no take-over request (TOR) to notify the drivers of the braking vehicle, while in the highly automated condition, the situation happened right after the drivers had deactivated the automation in response to a TOR. The lead time of the TOR was set at 8 s. Driver's level of drowsiness, workload (visual, mental and motoric) from carrying out the NDRT and motivational appeal of the NDRT right before the control transition were video-coded and used to predict the outcome of the braking event (i.e., reaction and system deactivation times, minimal Time-to-collision (TTC) and self-reported criticality) with a multiple regression approach. RESULTS: In the partial automation condition, reaction times to the braking vehicle and situation criticality as measured by the minimum TTC could be well predicted. Main predictors for increased reaction time were drowsiness and motivational appeal of the NDRT. However, visual and mental demand associated with NDRTs did decrease reaction time, suggesting that the NDRT helped the drivers to maintain alertness during the partially automated drive. Accordingly, drowsiness and motivational appeal of the NDRT increased situation criticality, while cognitive load due to the NDRT decreased it. In the highly automated condition, however, it was not possible to predict system deactivation time (in reaction to the TOR), brake reaction time to the braking vehicle and situation criticality by observed drowsiness and NDRT engagement. DISCUSSION: The results suggest a relationship between the driver's drowsiness and NDRT engagement in partial automation but not in highly automated driving. Several explanations for this finding are discussed. It could be possible that the lead time of 8 s might have given the drivers enough time to complete the driver state transition process from executing NDRTs to manual driving, putting them in a position to be able to cope with the driving event, while this was not possible in the partial automation condition. Methodological issues that might have led to a non-detection of an effect of drowsiness or NDRT engagement in the highly automated driving condition, such as the sample size and sensitivity of the observer ratings, are also discussed.

Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving.

Currently, development of conditionally automated driving systems which control both lateral and longitudinal vehicle guidance is attracting a great deal of attention. The driver no longer needs to constantly monitor the roadway, but must still be able to resume vehicle control if necessary. The relaxed attention requirement might encourage engagement in non-driving related secondary tasks, and the resulting effect on driver take-over is unclear. The aim of this study was to examine how engagement in three different naturalistic secondary tasks (writing an email, reading a news text, watching a video clip) impacted take-over performance. A driving simulator study was conducted and data from a total of 79 participants (mean age 40 years, 35 females) were used to examine response times and take-over quality. Drivers had to resume vehicle control in four different non-critical scenarios while engaging in secondary tasks. A control group did not perform any secondary tasks. There was no influence of the drivers' engagement in secondary tasks on the time required to return their hands to the steering wheel, and there seemed to be only little if any influence on the time the drivers needed to intervene in vehicle control. Take-over quality, however, deteriorated for distracted drivers, with drivers reading a news text and drivers watching a video deviating on average approximately 8-9cm more from the lane center. These findings seem to indicate that establishing motor readiness may be carried out almost reflexively, but cognitive processing of the situation is impaired by driver distraction. This, in turn, appears to determine take-over quality. The present findings emphasize the importance to consider both response times and take-over quality for a comprehensive understanding of factors that influence driver take-over. Furthermore, a training effect in response times was found to be moderated by the drivers' prior experience with driver assistance systems. This shows that besides driver distraction, driver-related factors influencing take-over performance exist.

What determines the take-over time? An integrated model approach of driver take-over after automated driving.

In recent years the automation level of driver assistance systems has increased continuously. One of the major challenges for highly automated driving is to ensure a safe driver take-over of the vehicle guidance. This must be ensured especially when the driver is engaged in non-driving related secondary tasks. For this purpose it is essential to find indicators of the driver's readiness to take over and to gain more knowledge about the take-over process in general. A simulator study was conducted to explore how drivers' allocation of visual attention during highly automated driving influences a take-over action in response to an emergency situation. Therefore we recorded drivers' gaze behavior during automated driving while simultaneously engaging in a visually demanding secondary task, and measured their reaction times in a take-over situation. According to their gaze behavior the drivers were categorized into "high", "medium" and "low-risk". The gaze parameters were found to be suitable for predicting the readiness to take-over the vehicle, in such a way that high-risk drivers reacted late and more often inappropriately in the take-over situation. However, there was no difference among the driver groups in the time required by the drivers to establish motor readiness to intervene after the take-over request. An integrated model approach of driver behavior in emergency take-over situations during automated driving is presented. It is argued that primarily cognitive and not motor processes determine the take-over time. Given this, insights can be derived for further research and the development of automated systems.