Problem Definition According to the World Health Organization, traffic injuries have become the eighth cause of death and the leading cause among children and young adults. Human error, and in particular perceptual error, is among the most frequently reported causes of road fatalities. The desire to reduce traffic fatalities has led to the development of automated driving, which promises revolutionary advances in driver safety, traffic capacity and driver convenience. Since true autonomy in mixed traffic has not yet been achieved, today's automated vehicles require the driver to continuously supervise the automation and to capably intervene when necessary. However, simulator studies and experiences from disciplines such as aviation and factories have demonstrated that humans are generally ill-equipped to monitor automation for longer periods. This raises the concern that partial automation may harm rather than help traffic safety if not designed to adequately support the drivers in their supervisory tasks. Research objectives To address this concern, further insights are needed in how drivers monitor automation in complex real-world traffic, and how their behaviour and performance change with long-term automated driving experience. This dissertation sets out to investigate how real-world automation changes the availability of attentional resources, to establish where and how drivers use automation in naturalistic conditions, and evaluate how these change with experience. While these objectives investigate periods of automated driving, vehicles with automated driving functionalities will often be driven manually, when outside the operational design domain or at the driver’s preference. In these conditions, the available automation may still outperform the driver on particular tasks, such as detecting and tracking surrounding road users without bias or distraction. This dissertation therefore also contributes to the search for ways in which automation can provide meaningful support to the traffic monitoring task in manual and supervised driving. To evaluate if and when supervised automated driving negatively affects the driver’s ability to monitor, mental workload is evaluated in a Tesla model S on public roads (Chapter 2). Voluntary automation use and attention are examined in a naturalistic driving study on public roads (Chapter 3). To evaluate the effect of experience with automated driving, Chapter 2 compares drivers with and without prior automation use, whereas Chapter 3 examines how behaviour changes over a two-month period, compared to one month of manual driving. Two studies are performed to examine how driving automation can support the driver with the monitoring task, for which an instrumented vehicle was extended with cameras which track the driver’s gaze and associate it to surrounding road users as detected by the vehicle perception. The first study (Chapter 4) investigates how well gaze behaviour can indicate driver awareness toward individual road users, and proposes a recognition task to obtain a ground truth for awareness of multiple other road-users. The second study (Chapter 5) evaluates if driver gaze and head pose can provide earlier predictions for emergency alerting and intervention systems. A crossing pedestrian collision risk prediction system is used as a case study where gaze and contextual cues are evaluated in their contribution to path and risk prediction using a dynamic Bayesian network. Findings & recommendations Chapter 2 found that workload differed between roads with high and low traffic complexity, both for manual and automated driving, which indicates that drivers remain sensitive to changes in task demand while supervising automated driving. Drivers with prior experience in automated driving perceived a lower workload while supervising automation compared to manual driving. No workload difference was perceived for first-time users. In contrast, attentional demand as measured by a detection-response task was higher during automation use compared to manual driving regardless of experience. This indicates that monitoring automation (SAE2) requires more mental capacity compared to manual driving, which suggests that in contrast to a wide range of studies, SAE2 can increase workload. Supervising automation may therefore be beneficial for driver attention, but perception of workload during supervision may be too low for this to occur naturally. Future work should consider calibrating workload perception and system limitation understanding rather than actual task demand to encourage attentive supervision. Chapter 3 shows that automation is mostly used on road types generally considered suitable for automated driving with only incidental use on urban roads. This suggests that users are adhering to the operational design domain of these vehicles. On highways, automation is used at all speeds, but less during short periods of slow driving. No time-in-drive, time-of-day or experience effects were found for automation use. On the highway, head pose deviation was smaller during automation use compared to manual driving but tended to increase over the first six weeks of use, which may indicate a change in monitoring strategy. Further research is needed to assess if this difference indicates better or worse monitoring behaviour. Chapter 4 found that drivers performed better on the recognition task when road users were relevant for the driven manoeuvre and when drivers had directed their gaze within 10 degrees of these road users. However, at least 18% of road users were recognised while only observed peripherally, suggesting that peripheral vision should not be neglected in attention monitoring. Recognition performance was not predicted by gaze metrics and requires further development to reduce forget rates. Further analysis is needed to compare the recognition task to established situation awareness measures after these improvements are obtained. Chapter 5 demonstrates that driver and pedestrian attention monitoring can provide a benefit to pedestrian crossing collision risk prediction when predicting further than 0.75 seconds ahead. The higher workload during supervised automation and the general adherence to the operational design domain in naturalistic driving indicate that supervising driving automation can be beneficial to driver attention and traffic safety, but literature and recent accidents demonstrate that challenges remain in encouraging such attentive behaviour. Strategies to encourage attentive supervision should therefore be further developed, as well as ways to maintain these strategies while automation technology improves in pursuit of the opposite objective to reduce engagement in the driving task. The joint analysis of driver gaze and road scene may improve driver support during manual driving and supervised automation, and benefit the development of automated driving. But care should be taken that systems which use driver attention or rely on other contextual cues do not become susceptible to the same mistakes as drivers tend to make. While careful design approaches can reduce the risk of mimicking human error, validation will ultimately require a reliable way to distinguish between awareness and inattentional blindness. The instrumentation and conducted studies with on-road automation demonstrate that on-road research is becoming more practical and accessible than ever before, thanks to recent developments in automation. The observation that during on-road automation, inexperienced drivers perceive higher workload compared to in simulators testifies for the importance of on-road driving research. Challenges encountered during the naturalistic study and attention study demonstrate that the instrumentation and processing have to be designed and tested carefully for on-road research to be effective.
|Award date||25 Feb 2021|
|Publication status||Published - 2021|
- situation awareness
- naturalistic driving
- driver support