Understanding attention selection in driving: From limited capacity to adaptive behaviour

Accident analysis studies have consistently identified attention-related failures as key factors behind road crashes. However, less is known about how such failures lead to accidents. Traditionally, one reason for this knowledge gap has been a lack of sufficiently detailed data from the pre-crash phase, although this situation is currently changing with the advent of naturalistic driving studies. However, a remaining issue is the lack of an adequate conceptual model of attention selection applicable in natural driving situations. Existing attention models applied in the driving domain are generally based on the notion of attention as a resource with limited capacity, subject to overload in demanding conditions. Such models have mainly focused on dual task interference in experimental situations and but have put less emphasis on aspects central to attention selection in everyday driving such as expectancy and anticipatory attention allocation. The general objective of the present thesis was to obtain a better understanding of the relation between attention, performance and crash risk in real driving situations. To this end, a general conceptual framework for understanding attention selection in natural driving was developed, based on the view of attention selection as a form of adaptive behaviour rather than a consequence of limited information processing capacity. This also involved the development of a specific model of attention selection mechanisms and a series of empirical studies to support the model development. The main objective of these studies was to better understand the effects of working memory (or cognitive-) load on driving performance and the key mechanisms behind expectancy and proactive attention scheduling in driving. A key finding was that working memory load appears to selectively affect aspects of driving performance that can be characterised as controlled, while leaving reflexive and habitual, automatic, behaviours largely unaffected, an idea that resolves several inconsistent findings in the existing literature. Based on the proposed model, precise definitions of attention, expectancy, driver inattention and driver distraction were proposed. The thesis also suggests a general conceptualisation of the relation between attention selection and crashes. Finally, practical applications of the present findings in the areas of accident and incident analysis, countermeasure development and evaluation methods are discussed

A model of attention selection in driving

Inattention is one of the most common factors contributing to road crashes. However, the basic mechanisms behind inattention and how it leads to crashes are still relatively poorly understood. The objective of the present thesis was to develop a testable model of attention selection in driving which could serve as the basis for a scientifically grounded taxonomy of drivers’ attention failures.A key starting point for the model development was that inattention needs to be understood within a broader framework of adaptive driver behaviour. A proposal for such a framework was outlined as the basis for the subsequent model development. At the core of the proposed model is an attention selection mechanism based on the biased competition hypothesis, which states that attention selection occurs through local competitive interactions which are biased top-down and/or bottom-up. The model is rooted within the embodied cognitive science tradition and adopts an activation dynamics-, rather than an information processing, metaphor. Still, the main concepts are largely compatible with traditional information processing models of attention, such as Multiple Resource Theory.The model was validated against existing empirical work on driver inattention, in particular the two studies reported in the appended papers, which investigated the effects of visual and cognitive secondary tasks on driving performance, behaviour and state. It was demonstrated how the model offers novel explanations for several observed phenomena not which do not seem to be accounted for by existing models. Moreover, a number of novel predictions were generated which could be tested in future empirical studies. It was also demonstrated how the model can be used as the basis for a taxonomy of attention-related failures and factors in driving

EFFECTS OF VISUAL AND COGNITIVE DISTRACTION ON LANE CHANGE TEST PERFORMANCE

Driver errors related to visual and cognitive distraction were studied in the context of the Lane Change Test (LCT). New performance metrics were developed in order to capture the specific effects of visual and cognitive distraction. In line with previous research, it was found that the two types of distraction impaired driving in different ways. Visual, but not cognitive, distraction led to reduced path control. By contrast, only cognitive distraction affected detection and recognition/response selection. Theoretical and practical implications of these results are discussed

Real-Time Distraction Countermeasures

In general terms, distraction can be defi ned as misallocated attention.1 In the context of driving, distraction could be induced by a range of activities such as looking after children, looking for road signs, applying makeup, and using in-vehicle information systems. Distraction may also be purely “internally” triggered, for example when daydreaming. A large body of empirical research links driver distraction to degraded driving performance, for example, reduced lateral control, reduced event and object detection performance, and impaired decision making (see, e.g., Young et al.2 for a review). Distraction has also repeatedly been identifi ed as a major contributing factor in crashes, 3-5 although direct causal links between distraction-induced performance degradation and actual crash risk have been diffi cult to establish empirically. This is due mainly to the methodological diffi culties associated with collecting suffi ciently detailed precrash data. However, recent results from naturalistic fi eld studies, such as the 100-car study, 6, 7 have contributed to bridging this gap (see Chapters 16 and 17), by demonstrating signifi cant increases in (relative) risk resulting from driver engagement in a variety of distracting activities

Evaluation Framework for Commercial Vehicle Safety Systems and Services (EFrame). Final Report

The main project aim was to develop a structured framework for traffic safety evaluation in an industrial (commercial vehicle manufacturer) context. The resulting framework facilitates more efficient development of crash/injury countermeasures by (1) identifying and focusing on the most important safety problems, (2) estimating the potential and actual safety benefits of safety systems and services and (3) identifying the data sources needed to perform these analyses.The project started with identification of the general types of safety evaluation analyses needed from an industrial development perspective (the Evaluation Use Cases, EUCs). The EUCs helped to keep the project focused, in spite of its broad general scope, and constituted the basis for all remaining work in the project (WP1). Next, an initial sketch of the framework, in terms of the data sources and analysis needed to address the EUCs were developed (WP1). This was followed by a comprehensive state-of-the-art review of existing data sources and road safety analysis methodologies that could potentially be used as components in the framework (WP2). Based on this, existing methods were adapted, or novel methods developed, to address the Evaluation Use Cases (WP3). Finally, the methods adapted/developed in WP3 were applied to a set of concrete evaluation test cases in order to demonstrate the framework and identify needs for further improvement (WP4). Based on this, the final framework was defined (WP4). Thus, the project objectives have generally been met, although further development and testing is needed on other concrete test cases beyond than those addressed in WP4.The framework has the potential to reduce the number of killed and injured in traffic by focusing industrial development and academic research on the most effective safety systems and services and increases AB Volvo’s international competitiveness by further strengthening its safety system/services offering. The project has also, thanks to its broad scope, fostered increased collaboration between different sub-fields of traffic safety analysis (e.g., passive safety, active safety and road user behavior analysis) and thus contributed to the development of a critical mass of competence at SAFER/Chalmers/Volvo in this area

Final framework specification for Evaluation Framework for Commercial Vehicle Safety Systems and Services (EFrame)

The objective of the EFrame FFI project was to develop a structured framework for traffic safety evaluation in an industrial (commercial vehicle manufacturer) context. The resulting framework facilitates more efficient development of crash/injury countermeasures by identifying and focusing on the most important safety (crash) problems, providing a toolset for analyzing crashes and estimating the potential and actual effectiveness of safety systems and services and, finally, identifying the data sources needed to perform these analyses. A general overview of the project and its results can be found in the Final Report (Engstr\uf6m and Wege, 2016)The project started with identification of the general types of safety evaluation needed from an industrial development perspective (the Evaluation Use Cases, EUCs). The EUCs helped to keep the project focused, in spite of its broad general scope, and constituted the basis for all remaining work in the project

Adaptive behavior in the simulator: Implications for active safety system evaluation

The Problem. Driving is, most of the time, a self-paced task where drivers proactively control the driving situation, based on their expectations of how things will develop in the near future. Crashes are typically associated with unexpected events where this type of proactive adaptation failed in one way or another. These types of scenarios are the main targets for active safety systems. In evaluation studies, drivers’ responses to expected events may be qualitatively different from responses to similar, but unexpected, events. Hence, creating artificial active safety evaluation scenarios that truly represent the targeted real-world scenarios is a difficult challenge. Role of Driving Simulators. Driving simulators offer great possibilities to test active safety systems with real drivers in specific target scenarios under tight experimental control. However, in simulator studies, experimental control generally has to be traded against realism. The objective of this chapter is to address some key problems related to driver expectancy and associated adaptive behavior in the context of simulator-based active safety system evaluation. Key Results of Driving Simulator Studies. The chapter briefly reviews common types of adaptive driver strategies found in the literature and proposes a general conceptual framework for describing adaptive driver behavior. Based on this framework, some key challenges in dealing with these types of issues in simulator studies are identified and potential solutions discussed. Scenarios and Dependent Variables. Key variables representing adaptive driver behavior include the selection of speed, headway, and lane position as well as the allocation of attention and effort. It will never be possible to create artificial simulator scenarios for active safety evaluation that perfectly match their real-world counterparts, but there are several means that could be used to reduce the discrepancy. Problems with expectancy and resulting adaptive behavior may at least be partly overcome by various means to “trick” drivers into critical situations, several of which are addressed in the chapter. Platform Specificity and Equipment Limitations. The issues discussed in this chapter should apply across all types of driving simulator platforms. However, some of the proposed methods for tricking drivers into critical situations may require specific simulator features, such as a motion base

Effects of forward collision warning and repeated event exposure on emergency braking

Many experimental studies use repeated lead vehicle braking events to study the effects of forward collision warning (FCW) systems. It can, however, be argued that the use of repeated events induce expectancies and anticipatory behaviour that may undermine validity in terms of generalisability to real-world, naturalistic, emergency braking events. The main objective of the present study was to examine to what extent the effect of FCW on response performance is moderated by repeated exposure to a critical lead vehicle braking event. A further objective was to examine if these effects depended on event criticality, here defined as the available time headway when the lead vehicle starts to brake. A critical lead vehicle braking event was implemented in a moving-base simulator. The effects of FCW, repeated event exposure and initial time headway on driver response times and safety margins were examined. The results showed that the effect of FCW depended strongly on both repeated exposure and initial time headway. In particular, no effects of FCW were found for the first exposure, while strong effects occurred when the scenario was repeated. This was interpreted in terms of a switch from closed-loop responses triggered reactively by the situation, towards an open-loop strategy where subjects with FCW responded proactively directly to the warning. It was also found that initial time headway strongly determined response times in closed-loop conditions but not in open-loop conditions. These results raise a number of methodological issues pertaining to the design of experimental studies with the aim of evaluating the effects of active safety systems. In particular, the implementation of scenario exposure and criticality must be carefully considered

ANNEXT - Slutrapport

Huvudm\ue5let med ANNEXT har varit att utv\ue4rdera och illustrera styrkan i att anv\ue4nda redan insamlade externa naturalistiska k\uf6rdata fr\ue5n DriveCams datainsamling (www.drivecam.com), f\uf6r att \uf6ka f\uf6rst\ue5elsen kring vad som orsakar trafikolyckor. Fram till nyligen har det inte funnits data som medger detaljerade studier av vad som sker i de viktiga sekunderna precis innan en olycka (eller n\ue4stan-olycka) i verklig trafik. Det vill s\ue4ga, det har inte varit m\uf6jligt att studera t.ex. f\uf6rares aktiviteter (t.ex. distraktioner s\ue5som mobilanv\ue4ndande) eller hur interaktionen mellan fordonen och dess f\uf6rare faktiskt g\ue5tt till under sekunderna f\uf6re krock. Vi har anv\ue4nt oss av data from h\ue4ndelseinitierade inspelningsenheter i kommersiella fordon, d\ue4r data inte samlats in som en del i n\ue5got forskningsprojekt, utan som en del i en aff\ue4rsverksamhet d\ue4r DriveCam s\ue5lt en tj\ue4nst till f\uf6retag som har flottor med fordon. Det vill s\ue4ga, dessa h\ue4ndelseinspelare installeras inte i fordon f\uf6r att specifikt forska p\ue5 orsaker till olyckor, utan som en del i en tj\ue4nst f\uf6r att f\uf6rb\ue4ttra trafiks\ue4kerheten f\uf6r just dessa f\uf6retag. Ett tydligt m\ue5l \ue4r att f\ue5 ner stillest\ue5ndskostnader f\uf6r f\uf6retagens fordon vid olyckor, men det har ocks\ue5 effekten att antalet olyckor (och d\ue4rmed skadade och d\uf6dade) minskar p\ue5 v\ue4garna d\ue4r de k\uf6rs. Fordonen k\uf6rs som en del i den vanliga verksamheten hos f\uf6retagen. N\ue4r en olycka eller n\ue4stan-olycka identifieras med hj\ue4lp av kinematiska triggers (t.ex. tr\uf6skelv\ue4rden p\ue5 acceleration), sparas data 8 sekunder f\uf6ra och 4 sekunder efter triggern i inspelningsenheten. Data innefattar bl.a. GPS, video p\ue5 f\uf6raren och fram\ue5t samt accelerometerdata. Denna skickas sedan tillbaks till DriveCam d\ue4r en genomg\ue5ng av alla h\ue4ndelser g\uf6rs och varje event klassas f\uf6r trafiks\ue4kerhetsrelevans. I ANNEXT har vi f\ue5tt tillg\ue5ng till 100 p\ue5k\uf6randeh\ue4ndelse (70 olyckor och 30 n\ue4stan-olyckor) samt 93 korsningsh\ue4ndelser (63 olyckor och 30 n\ue4stan-olyckor). Alla h\ue4ndelser har kodats av DriveCam-personal och vi p\ue5 SAFER har sedan analyserat data. F\uf6ljande beskriver processen genom projektet: F\uf6rsta steget var att ta fram en prelimin\ue4r analysplan, baserad p\ue5 projektans\uf6kan och ytterligare identifierade behov. Steg tv\ue5 var att kontakta DriveCam och f\ue5 till ett kontrakt inom vilkets ramar projektet kunde genomf\uf6ras. Scenarios identifierades och kriterier f\uf6r hur h\ue4ndelser skulle v\ue4ljas ut utvecklades och itererades mot DriveCam. Detaljerna \ue4r beskrivna i en publikation (Engstr\uf6m, Werneke, et al., 2013) fr\ue5n projektet (se separat avsnitt). Analysplanen har f\uf6rfinats allteftersom i projektet. Som en del i analysplanen utvecklades en annoteringsbeskrivning, eller kodbok. Denna beskriver de variabler som vi identifierat som n\uf6dv\ue4ndiga f\uf6r analysen och som kr\ue4ver manuell annotering av DriveCam-video. I ANNEXT utf\uf6rde en dedikerad annoterare p\ue5 DriveCam all prim\ue4r videoannotering. F\uf6r att f\uf6rfina kodboken och verifiera annotering \ue5kte SAFER-deltagare till DriveCam vid tv\ue5 tillf\ue4llen. N\ue4r den huvudsakliga annotering var avslutad genomf\uf6rde SAFER-partners 1) utveckling av metoder f\uf6r extraktion av optiska parameterar baserat p\ue5 annoterad video (B\ue4rgman et al., 2013), och processade dessa data f\uf6r att kvalitetss\ue4kra inf\uf6r analys, 2) iterativ vidareutveckling av kodningsschemat som kan kallas Kodbok f\uf6r bidragandefaktorer (se resultat) och applicerade denna p\ue5 tillg\ue4nglig data. Under v\ue5ren och sommaren 2013 presenterades tv\ue5 vetenskapliga artiklar p\ue5 konferenser. Ytterligare publikationer \ue4r under utveckling (se separat avsnitt

Attention selection and multitasking in everyday driving: A conceptual model

This chapter outlines a conceptual model of attention selection and multitasking in everyday driving. While existing theoretical and empirical work on attention in driving has mainly focused on dual-task interference in experimental settings, the present model aims to account for attention selection in natural driving situations. The model starts from the view of attention as a form of adaptive behaviour and emphasises the key role of expectancy, the dynamic interplay between top-down and bottom-up selection, the often habitual nature of attention selection in real driving and how attention selection is driven by perceived and expected value. However, the model also offers a novel characterisation of dual-task interference mechanisms and more precise definitions of key concepts such as driver inattention and driver distraction. Based on the model, a general conceptualisation of the relation between attention selection and crash causation is proposed and implications for the design of driver support systems and automotive human-machine interfaces are discussed