Systematic Review of Driver Distraction in the Context of Advanced Driver Assistance Systems (ADAS) & Automated Driving Systems (ADS)

Advanced Vehicle Systems promise improved safety and comfort for drivers. Steady advancements in technology are resulting in increasing levels of vehicle automation capabilities, furthering safety benefits. In fact, some of these vehicle automation systems are already deployed and available, but with promised benefits, such systems can potentially change driving behaviors. There is evidence that drivers have increased secondary task engagements while driving with automated vehicle systems, but there is a need for a clearer scientific understanding of any potential correlations between the use of automated vehicle systems and potentially negative driver behaviors. Therefore, this thesis aims to understand the state of knowledge on automated vehicle systems and their possible impact on drivers’ distraction behaviors. I have conducted two systematic literature reviews to examine this question. This thesis reports these reviews and examines the effects of secondary task engagement on driving behaviors such as take-over times, visual attention, trust, and workload, and discusses the implications on driver safety

Exploring the benefits of conversing with a digital voice assistant during automated driving: A parametric duration model of takeover time

Vehicle automation allows drivers to disengage from driving causing a potential decline in their alertness. One of the major challenges of highly automated vehicles is to ensure a timely (with respect to safety and situation awareness) takeover in such conditions. For this purpose, the current study investigated the role of an in-vehicle digital voice-assistant (VA) in conditionally automated vehicles, offering spoken discourse relating specifically to contextual factors, such as the traffic situation and road environment. The study involved twenty-four participants, each taking two drives (counterbalanced): with VA and without VA, in a driving simulator. Participants were required to takeover vehicle control following the issuance of a takeover request (TOR) near the end of each drive. A parametric duration model was adopted to find the key factors determining takeover time (TOT). Paired comparisons showed higher alertness and higher active workload (mean NASA-TLX rating) during automation when accompanied by the VA. Paired t-test comparison of gaze behavior prior to takeover showed significantly higher instances of checking traffic signal, roadside objects, and the roadway during the drive with VA, indicating higher situation awareness. The parametric model indicated that the VA increased the likelihood of making a timely takeover by 39%. There was also some evidence suggesting that male drivers are likely to resume control 1.21 times earlier than female drivers. The study findings highlight the benefits of adopting a digital voice assistant to keep the drivers alert and aware about the recent traffic environment in partially automated vehicles

Effects of cognitive tasks on car drivers’ behaviors and physiological responses

The effects of drivers’ engagement in cognitive tasks (i.e., non-visual, cognitively loading activities unrelated to the task of driving) are debated and unclear. Numerous experiments show impaired driver behaviors, yet naturalistic studies typically do not find an increased crash risk. In the future, autonomous driving (AD) is expected to improve traffic safety while allowing safe engagement in cognitive (and other) tasks. Having the opportunity to perform non-driving related tasks while traveling may then motivate drivers to use AD, provided they can actually engage in the tasks. Unfortunately, research on drivers’ engagement in cognitive tasks suffers severe methodological limitations since reliable and unintrusive measures of cognitive load are lacking.The aim of this thesis is therefore to advance the understanding of task-induced cognitive load in the context of traffic safety. This aim is split into two objectives: A) to better understand how drivers’ involvement in cognitive tasks can affect safety-relevant driver behaviors and decisions and B) to provide methodological guidance about assessing cognitive load in drivers using physiological measures.To accomplish Objective A, effects of cognitive tasks on driver behaviors were studied during routine driving and in a safety-critical event in a driving simulator. Also, drivers’ ability to engage in a non-driving related task while using AD in real traffic was explored. In line with the cognitive control hypothesis (Engstr\uf6m et al., 2017), it was found that cognitive tasks negatively affected driver behaviors in situations where cognitive control was needed, for example in intersections—but not in a lead vehicle braking scenario where responses were triggered automatically by visual looming. It was also found that although the number of off-path glances decreased during cognitive load, the timing of the remaining glances was unaffected. Clearly, cognitive load has different effects on different mechanisms. When using AD, drivers were indeed capable of engaging in a non-driving related task—suggesting that AD will be able to fulfill drivers’ desire to perform such tasks while traveling, which may motivate AD usage and thus improve traffic safety (given that AD is truly safer than manual driving). Finally, a simulator study addressing Objective B showed that the measurability of cognitive load was greatly improved by recognizing that multiple coexisting mental responses give rise to different physiological responses. This approach can provide less context-dependent measurements and allows for a better, more detailed understanding of the effects of cognitive tasks.These findings can help improve traffic safety—both by being used in system development, and as part of the systems themselves

Ensuring the Take-Over Readiness of the Driver Based on the Gaze Behavior in Conditionally Automated Driving Scenarios

Conditional automation is the next step towards the fully automated vehicle. Under prespecified conditions an automated driving function can take-over the driving task and the responsibility for the vehicle, thus enabling the driver to perform secondary tasks. However, performing secondary tasks and the resulting reduced attention towards the road may lead to critical situations in take-over situations. In such situations, the automated driving function reaches its limits, forcing the driver to take-over responsibility and the control of the vehicle again. Thus, the driver represents the fallback level for the conditionally automated system. At this point the question arises as to how it can be ensured that the driver can take-over adequately and timely without restricting the automated driving system or the new freedom of the driver. To answer this question, this work proposes a novel prototype for an advanced driver assistance system which is able to automatically classify the driver’s take-over readiness for keeping the driver ”in-the-loop”. The results show the feasibility of such a classification of the take-over readiness even in the highly dynamic vehicle environment using a machine learning approach. It was verified that far more than half of the drivers performing a low-quality take-over would have been warned shortly before the actual take-over, whereas nearly 90% of the drivers performing a high-quality take-over would not have been interrupted by the driver assistance system during a driving simulator study. The classification of the take-over readiness of the driver is performed by means of machine learning algorithms. The underlying features for this classification are mainly based on the head and eye movement behavior of the driver. It is shown how the secondary tasks currently being performed as well as the glances on the road can be derived from these measured signals. Therefore, novel, online-capable approaches for driver-activity recognition and Eyes-on-Road detection are introduced, evaluated, and compared to each other based on both data of a simulator and real-driving study. These novel approaches are able to deal with multiple challenges of current state-of-the-art methods such as: i) only a coarse separation of driver activities possible, ii) necessity for costly and time-consuming calibrations, and iii) no adaption to conditionally automated driving scenarios.Das hochautomatisierte Fahren bildet den nächsten Schritt in der Evolution der Fahrerassistenzsysteme hin zu vollautomatisierten Fahrzeugen. Unter definierten Bedingungen kann dabei der Fahrer die Fahraufgabe inklusive der Verantwortung über das Fahrzeug einer automatisierten Fahrfunktion übergeben und erhält die Möglichkeit sich anderen Tätigkeiten zu widmen. Um dennoch sicherzustellen, dass der Fahrer bei Bedarf schnellstmöglich die Kontrolle über das Fahrzeug wieder übernehmen kann, stellt sich die Frage, wie die fehlende Aufmerksamkeit gegenüber dem Straßenverkehr kompensiert werden kann ohne dabei die hochautomatisierte Fahrfunktion oder die neu gewonnenen Freiheiten des Fahrers zu beschränken. Um diese Frage zu beantworten wird in der vorliegenden Arbeit ein erstes prototypisches Fahrerassistenzsystem vorgestellt, welches es ermöglicht, die Übernahmebereitschaft des Fahrers automatisiert zu klassifizieren und abhängig davon den Fahrer "in-the-loop" zu halten. Die Ergebnisse zeigen, dass eine automatisierte Klassifikation über maschinelle Lernverfahren selbst in der hochdynamischen Fahrzeugumgebung hervorragende Erkennungsraten ermöglicht. In einer der durchgeführten Fahrsimulatorstudien konnte nachgewiesen werden, dass weit mehr als die Hälfte der Probanden mit einer geringen Übernahmequalität kurz vor der eigentlichen Übernahmesituation gewarnt und nahezu 90% der Probanden mit einer hohen Übernahmequalität in ihrer Nebentätigkeit nicht gestört worden wären. Diese automatisierte Klassifizierung beruht auf Merkmalen, die über Fahrerbeobachtung mittels Innenraumkamera gewonnen werden. Für die Extraktion dieser Merkmale werden Verfahren zur Fahreraktivitätserkennung und zur Detektion von Blicken auf die Straße benötigt, welche aktuell noch mit gewissen Schwachstellen zu kämpfen haben wie: i) Nur eine grobe Unterscheidung von Tätigkeiten möglich, ii) Notwendigkeit von kosten- und zeitintensiven Kalibrationsschritten, iii) fehlende Anpassung an hochautomatisierte Fahrszenarien. Aus diesen Gründen wurden neue Verfahren zur Fahreraktivitätserkennung und zur Detektion von Blicken auf die Straße in dieser Arbeit entwickelt, implementiert und evaluiert. Dabei bildet die Anwendbarkeit der Verfahren unter realistischen Bedingungen im Fahrzeug einen zentralen Aspekt. Zur Evaluation der einzelnen Teilsysteme und des übergeordneten Fahrerassistenzsystems wurden umfangreiche Versuche in einem Fahrsimulator sowie in realen Messfahrzeugen mit Referenz- sowie seriennaher Messtechnik durchgeführt

Varieties of interaction: from User Experience to Neuroergonomics:On the occasion of the Human Factors and Ergonomics Society Europe Chapter Annual Meeting in Rome, Italy 2017

Proceedings of the HFES European Chapter conferenc