Trafficking in cognition: applying cognitive psychology to driving

This paper summarises a Presidential Address to the Division of Traffic and Transportation Psychology at the 2002 International Congress of Applied Psychology. It considers whether traffic psychology is a distinct area of psychology, and concludes that the range of psychological approaches that understanding drivers and traffic requires is too pervasive for it to be so. The difficulties and shortcomings of various attempts to apply cognitive psychology to driving and traffic are explored, with respect to perceptual, motor and skilled aspects of the driving task. Examples are given of how ‘understanding driving’ poses theoretical challenges to mainstream cognitive psychology that have yet to be satisfactorily resolved.     

Impacts of a Congestion Assistant on driving behaviour and acceptance using a driving simulator

In-vehicle systems that assist the driver with his driving task are developed and introduced to the market at increasing rate. Drivers may be supported during congested traffic conditions by a so-called Congestion Assistant consisting of a mix of informing, assisting and controlling functions. This paper describes the impacts of the Congestion Assistant on the driver in terms of driving behaviour and acceptance. Thirty-seven participants took part in a driving simulator study. The observed driving behaviour showed promising improvements in traffic safety when approaching the traffic jam. Moreover, positive effects of the system on traffic efficiency can be expected in the jam. The participants stated to appreciate the Congestion Assistant, although not all functions were equally rated. To increase the performance and acceptance of the total system, some refinements were suggested.     

Investigating the effect of contextual factors on driving: An experimental study

Alongside human factors, contextual factors are believed to have an ongoing and complex impact on driving outcomes. However, how and to what extent the components of context influence driving outcomes (e.g. rule violations, crash, stress, fatigue) are far beyond full understanding. The purpose of this study is to explore the effects of weather condition, lighting condition and traffic density on driving outcomes. Thirty-six volunteers were enrolled to participate into a driving simulator-based experiment. Each participant was required to complete twelve trials of simulated driving under different sets of scenarios. Driving outcome was measured by five dependent variables: frequency of speeding, frequency of lane deviations, number of correct sign recognition, completion time and workload. The results showed the frequency of speeding was significantly affected by weather condition, lighting condition and traffic density. Lighting condition had a significant effect on number of correct sign recognition. Weather condition, lighting condition and traffic density had significant effects on task completion time. Weather condition and lighting condition had significant effects on driver’s workload. The implications of the results could help traffic safety professionals better understand the risk factors that may lead to human errors during driving. Practically, countermeasures could be inspired and developed to mitigate the adverse impacts brought by driving context to minimum.     

Instruction-prompted objective behaviors as proxy for subjective measures in a driving simulator

Interactions with other road users and interpretations of traffic situations are important aspects of driving safety. Self-reports are often used to study drivers’ perceptions and attitudes but self-reports can be inaccurate and biased because of socially desirable responding. Driving simulators offer objective measures of driver behaviors but have limited ability to elicit natural behaviors. To address this issue, we tested a driving simulator-based approach that combined realistic driving scenarios including potentially frustrating forward obstacles and delays in travel time with two different types of instructions. Participants' vehicle control behaviors and subjective perception of traffic delays were compared. Results demonstrated that behaviors collected following instructions to drive safely did not have significant associations with participants’ perceptions of the traffic delays while participants following instructions to drive quickly demonstrated behaviors that were predictive of their subjective perceptions of the traffic delays. The findings suggest that vehicle control behaviors can be used as a proxy for subjective perceptions of traffic delays. We conclude that driving simulator methodology combining instructions, realistic traffic scenarios, and adaptive analytical methods is appropriate for studying drivers’ behaviors and interactions with other road users and can minimize the need to rely on subjective self-reports.     

Situational influencing factors on understanding cooperative actions in automated driving

Cooperative interacting vehicles are a promising approach in the context of automated driving. To ensure understanding and acceptance of such systems, the underlying mechanisms of human cooperation in the context of traffic must be understood. In a driving simulator study, we investigated how situational factors influence cooperative behaviour in a lane change situation on a two-lane German highway during automated driving in the left lane. When another car in the right lane was approaching a slower truck, participants (N = 32) were asked by an automated system to either accelerate, decelerate or maintain speed. The driver’s scope of action, the situation’s criticality for the lane-changing vehicle and the display of intention to change lanes were manipulated. A hierarchical multinomial logistic regression revealed that a wider scope of action, a higher situation’s criticality and signalling the intention to change lanes positively influenced cooperative behaviour by accelerating and decelerating. These results might be applied to design user-centred automated cooperatively interacting vehicles.     

Temporal fluctuations in driving demand: The effect of traffic complexity on subjective measures of workload and driving performance

Traffic density has been shown to be a factor of traffic complexity which influences driver workload. However, little research has systematically varied and examined how traffic density affects workload in dynamic traffic conditions. In this driving simulator study, the effects of two dynamically changing traffic complexity factors (Traffic Flow and Lane Change Presence) on workload were examined. These fluctuations in driving demand were then captured using a continuous subjective rating method and driving performance measures. The results indicate a linear upward trend in driver workload with increasing traffic flow, up to moderate traffic flow levels. The analysis also showed that driver workload increased when a lane change occurred in the drivers’ forward field of view, with further increases in workload when that lane change occurred in close proximity. Both of these main effects were captured via subjective assessment and with driving performance parameters such as speed variation, mean time headway and variation in lateral position. Understanding how these traffic behaviours dynamically influence driver workload is beneficial in estimating and managing driver workload. The present study suggests possible ways of defining the level of workload associated with surrounding traffic complexity, which could help contribute to the design of an adaptive workload estimator.     

Intraindividual variability in driving simulator parameters of healthy drivers of different ages

Intraindividual variability is a fundamental behavioural characteristic of aging but has been examined to a very limited extent in driving. This study investigated intraindividual variability in driving simulator measures in healthy drivers of different ages using the coefficient of variation (COV) as a variability measure. Participants were healthy volunteers who were regular drivers, who were divided into a “young” group, a “middle-aged” group, and an “old” group. They drove in two environments (rural, 72 drivers; urban, 60 drivers), under conditions of moderate and high traffic load, without and with distraction (conversation). Significant differences in COV were observed in the rural condition for headway distance and lateral position as a function of traffic load, with high traffic (without and with distraction) resulting in increased COV of headway and decreased COV of lateral position. Significant differences in COV were observed in the urban condition for headway distance only, with high traffic (without and with distraction) resulting in increased COV of headway. No age effects were found for any of the driving conditions. The results indicate that traffic load affected headway distance and lateral position in opposite directions in all three age groups: high traffic resulted in increased variability of headway in both rural and urban conditions but in decreased variability of lateral position in the rural conditions compared to moderate traffic irrespective of distraction. The study indicates that driving conditions affect the intraindividual variability of driving measures in selective ways, which may be linked to the extent of automatization of the driving variables and to adaptive changes to traffic condition challenges.     

Approaching intersections: Gaze behavior of drivers depending on traffic,intersection type,driving maneuver,and secondary task involvement

Urban intersections are hotspots for crashes because they provide a location for several traffic streams and types of road users to cross. A main cause of crashes is the misinformation of drivers as they fail to sense relevant visual information. We aimed to analyze the gaze behavior of car drivers in a variety of intersection scenarios, bringing together the partial findings of previous research, and examine the interdependencies of the contributing factors to provide a database for driver modeling. In a driving simulator study with 59 participants, we varied intersection scenarios regarding drivers’ right of way (yield sign, green traffic light), intersection type (T junction, X intersection), surrounding traffic (none, irrelevant, relevant), and intended driving maneuver (left turn, right turn, going straight). A total of 25 intersection scenarios were presented in a within-subjects design to a control group and a group with a cognitive load task (counting back in numbers of two). Fixations were coded regarding defined areas of interest in the field of view and separated according to three segments of the intersection approach: 75–50 m, 50–25 m, and 25–0 m before entering the intersection. The results show that the effect of surrounding traffic, secondary task engagement, and the intended driving maneuver changed dramatically depending on the right of way of the driver. Surrounding traffic primarily affected gaze behavior in scenarios of ceding the right of way close to the intersection entry. The cognitive load task increased fixations on the road center especially in situations where the driver had the right of way, but less in situations of ceding the right of way. Interactions with the type of intersection were only apparent for different driving maneuvers. This study provides a detailed and comprehensive picture of drivers’ attentional processes when approaching intersections which is relevant for understanding and modeling of driver behavior in urban traffic.     

The effects of music tempo on simulated driving performance and vehicular control

The automobile is currently the most popular and frequently reported location for listening to music. Yet, not much is known about the effects of music on driving performance, and only a handful of studies report that music-evoked arousal generated by loudness decreases automotive performance. Nevertheless, music tempo increases driving risks by competing for attentional space; the greater number of temporal events which must be processed, and the frequency of temporal changes which require larger memory storage, distract operations and optimal driving capacities. The current study explored the effects of music tempo on PC-controlled simulated driving. It was hypothesized that simulated driving while listening to fast-paced music would increase heart rate (HR), decrease simulated lap time, and increase virtual traffic violations. The study found that music tempo consistently affected both simulated driving speed and perceived speed estimates: as the tempo of background music increased, so too did simulated driving speed and speed estimate. Further, the tempo of background music consistently affected the frequency of virtual traffic violations: disregarded red traffic-lights (RLs), lane crossings (LNs), and collisions (ACs) were most frequent with fast-paced music. The number of music-related automobile accidents and fatalities is not a known statistic. Police investigators, drivers, and traffic researchers themselves are not mindful of the risks associated with listening to music while driving. Implications of the study point to a need for drivers' education courses to raise public awareness about the effects of music during driving.     

Effect of verbal messages with reminders to communicate driving situations to alter driver behavior in conditional driving automation

In conditionally automated driving, drivers are relieved of steering (hands-off), accelerating, and braking actions as well as of continuous monitoring of driving situations and the system operation status (eyes off). This enables continuously engagement in non-driving-related activities. Managing the allocation of a driver’s attention to the surrounding environment and automation status presents a major challenge in human–machine system design. In this study, we propose a verbal message with a reminder (monitoring request) to divert the driver’s attention from non-driving-related activities to peripheral monitoring under conditionally automated driving. When the system encounters events related to weather, traffic, and road geometry, it provides a verbal message pertaining to the road surroundings (e.g., “It is foggy outside”) to the driver. After three seconds, the system provides a reminder message (i.e., “Did you confirm it?”) to the driver. We explore two questions: (1) how does the message with the reminder affect the driver’s attention allocation, and (2) how does the message with the reminder affect the driver behavior in response to a request to intervene (RTI). With a driving simulator, we designed a repeated measures mixed design with a between-participant factor of “Driving condition” and within-participant factors of “Event type” and “Measurement time”. Three driving conditions were established as follows: no messages, messages without reminders, and messages with reminders. Twenty-seven drivers participated as participants in the driving simulator experiment. Results showed that the reminder message was effective in allocating the participants’ attention to the surrounding environment, and the participants took over the driving task after spending more time understanding the take-over situation in the condition of messages with reminders compared to those in the condition of no messages. We conclude that the proposed reminder message can direct drivers’ attention to the road surroundings during conditionally automated driving. In the future, we plan to design adaptive verbal monitoring requests to adjust the reminder message according to the situation.     

Support for distracted driving laws: An analysis of adolescent drivers from the Traffic Safety Culture Index from 2011 to 2017

IntroductionAdolescent drivers are often the focus of traffic safety legislation as they are at increased risk for crash-related injury and death. However, the degree to which adolescents support distracted driving laws and factors contributing to their support are relatively unknown. Using a large, nationally weighted sample of adolescent drivers in the United States, we assessed if perceived threat from other road users’ engagement in distracted driving, personal engagement in distracted driving behaviors, and the presence of state distracted driving laws was associated with support for distracted driving laws.MethodsThe sample included 3565 adolescents (aged 16–18) who participated in the Traffic Safety Culture Index survey from 2011 to 2017. A modified Poisson regression model with robust errors was fit to the weighted data to examine support for distracted driving laws. Models included age, gender, year, state distracted driving laws, personal engagement in distracted driving behavior, and perceived threat from other road users’ engaging in distracted driving.ResultsApproximately 87% of adolescents supported a law against texting and emailing compared to 66% who supported a universal handheld cellphone law. Support for distracted driving legislation was associated with greater perceived threat of other road users engaging in distracted driving while accounting for personal engagement in distracted driving, state distracted driving laws, and developmental covariates.DiscussionGreater understanding of the factors behind legislative support is needed. Public health interventions focused on effectively translating the risks of cellphone use while driving and effective policy will further improve the traffic safety culture.     

A validation study of driving errors using a driving simulator

BackgroundDriving simulators have become an important research tool in road safety. They provide a safer environment to test driving performance and have the capacity to manipulate and control situations that are not possible on-road.AimTo validate a laboratory-based driving simulator in measuring on-road driving performance by type and mean driving errors.MethodsParticipants were instructed to drive a selected route on-road. The same route was programmed in the driving simulator using the UC/Win-road software. All participants completed a background questionnaire. On-road driving behaviours of participants and driving behaviours in the simulator were assessed by an occupational therapist and two trained researchers using an assessment form. Interclass correlations were calculated to assess the inter-rater agreement between the researchers on driving behaviours. Paired t-tests were used to assess differences in driving performance between the simulator and on-road assessments.ResultsA convenience sample of 47 drivers aged 18–69 years who held a current Western Australian class C licence (passenger vehicle) were recruited into the study. The mean age was 34.80 years (SD: 13.21) with twenty-six males (55.32%) and 21 females (44.68%) completing the study. There was no statistical difference between the on-road assessment and the driving simulator for mirror checking, left, right and forward observations, speed at intersections, maintaining speed, obeying traffic lights and stop signs.ConclusionThe preliminary results provide early support for the relative validity of the driving simulator which may be used for a variety of road safety outcomes with reduced risk of harm to participants.     

Evaluating perception in driving simulation experiments

The use of driving simulation for vehicle design and driver perception studies is expanding rapidly. This is largely because simulation saves engineering time and costs, and can be used for studies of road and traffic safety. How applicable driving simulation is to the real world is unclear however, because analyses of perceptual criteria carried out in driving simulation experiments are controversial. On the one hand, recent data suggest that, in driving simulators with a large field of view, longitudinal speed can be estimated correctly from visual information. On the other hand, recent psychophysical studies have revealed an unexpectedly important contribution of vestibular cues in distance perception and steering, prompting a re-evaluation of the role of visuo-vestibular interaction in driving simulation studies.     

Car following with an inertia-oriented driving technique: A driving simulator experiment

BackgroundFor many decades, car-following (CF) and congestion models have assumed a basic invariance: drivers’ default driving strategy is to keep the safety distance. The present study questions that Driving to keep Distance (DD) is a traffic invariance and, therefore, that the difference between the time required to accelerate versus decelerate must necessarily determine the observed patterns of traffic oscillations. Previous studies have shown that drivers can adopt alternative CF strategies like Driving to keep Inertia (DI) by following basic instructions. The present work aims to test the effectiveness of a DI course that integrates 4 tutorials and 4 practice sessions in a standard PC computer designed to learn more adaptive driving behaviors in dense traffic. Methods. Sixty-eight drivers were invited to follow a leading car that varied its speed on a driving simulator, then they took a DI course on a PC computer, and finally they followed a fluctuating leader again on the driving simulator. The study adopted a pretest-intervention-posttest design with a control group. The experimental group took the full DI course (tutorials and then simulator practice). The control group had access to the DI simulator but not to the tutorials. Results. All participating drivers adopted DD as the default CF mode on the pre-test, yielding very similar results. But after taking the full DI course, the experimental group showed significantly less accelerations, decelerations, and speed variability than the control group, and required greater CF distance, that was dynamically adjusted, spending less fuel in the post-test. A group of 8 virtual cars adopting DD required less space on the road to follow the drivers that took the DI course.     

Attitudes,norms and driving behaviour: A comparison of young drivers in South Africa and Sweden

Culture is increasingly recognised among traffic psychologists to be a factor influencing driving behaviour. This study examines whether a cultural background characterised by rapid social change and high levels of violence and aggression, as in the South African context, has any discernible influences on driving standards or the behaviour of individual drivers. The experiences and attitudes of young drivers in South Africa are compared with a group of young drivers from Sweden, a country whose society has exhibited high levels of stability and where road user behaviour is renowned for its restraint and compliance with regulations.The two cohorts provide information about their exposure to traffic injuries, their attitudes to other drivers and to a range of traffic offences, and to the types of behaviour they personally engage in. Among the South African respondents the notion of a declining standard of driving emerges very clearly, and specific new norms of driving are identified. Such norms are explained to be a consequence of new social values or challenges inherent within contemporary South African society.     

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.     

Psychometric adaptation of the driving cost and benefit scale in Chinese drivers

The present study aimed to adapt the Driving Cost and Benefit Scale (DCBS, Taubman-Ben- Ari, 2008) to Chinese drivers and examine its relationships with driving style and traffic violations. Nine hundred drivers aged 18 to 60 years were asked to complete the DCBS and the Multidimensional Driving Style Inventory. The results of exploratory factor analysis (n = 429) and confirmatory factor analysis (n = 429) yielded a 36-item scale with satisfactory reliability. The Chinese version of the DCBS contains seven factors, including three driving cost factors (damage to self-esteem, life endangerment and distress) and four driving benefit factors (impression management, sense of control, thrill and pleasure). Significant associations between the DCBS-C factors and driving styles and traffic violations show that the discriminant validity of the scale is acceptable. Moreover, the driving cost factor of damage to self-esteem and the driving benefit factor of thrill both contributed to drivers’ traffic violations and crashes. The findings show that the reliability and validity of the Chinese version of the DCBS are acceptable, and it can be used as a tool to measure driving motivation in China.     

A fuzzy-based driver assistance system using human cognitive parameters and driving style information

Reducing the number of traffic accidents due to human errors is an urgent need in several countries around the world. In this scenario, the use of human-robot interaction (HRI) strategies has recently shown to be a feasible solution to compensate human limitations while driving. In this work we propose a HRI system which uses the driver’s cognitive factors and driving style information to improve safety. To achieve this, deep neural networks based approaches are used to detect human cognitive parameters such as sleepiness, driver’s age and head posture. Additionally, driving style information is also obtained through speed analysis and external traffic information. Finally, a fuzzy-based decision-making stage is proposed to manage both human cognitive information and driving style, and then limit the maximum allowed speed of a vehicle. The results showed that we were able to detect human cognitive parameters such as sleepiness –63% to 88% accuracy–, driver’s age –80% accuracy– and head posture –90.42% to 97.86% accuracy– as well as driving style –87.8% average accuracy. Based on such results, the fuzzy-based architecture was able to limit the maximum allowed speed for different scenarios, reducing it from 50 km/h to 17 km/h. Moreover, the fuzzy-based method showed to be more sensitive with respect to inputs changes than a previous published weighted-based inference method.