This report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical models predicting individual driver crash involvement based on individual driving style, demographic and behavioral history variables, using large sets of naturalistic driving data. The project was designed as a pilot project with the objective of providing the basis for a future more comprehensive research effort. Based on Second Strategic Highway Research Program (SHRP2) data, a subset of behavior and crash data including 2,458 drivers was created for analysis. The data were analyzed to investigate to what extent these drivers were differentially involved in crashes and near crashes, to what extent this was associated with individual characteristics, and if it is possible to predict individual drivers’ crash and near crash involvement based on variables representing individual characteristics. The results clearly demonstrated the presence of differential crash and near crash involvement and showed significant associations between enduring personal factors and crash involvement. Moreover, logistic regression and random forest classifiers were relatively successful in predicting crash and near crash involvement based on individual characteristics, but the ability to specifically predict involvement in crashes was more limited.@en

Freeway merging of heavy goods vehicles (HGV) is a safety–critical manoeuvre. However, at present, it is largely unknown how HGV drivers perceive and execute the merging manoeuvre, and how current advanced driver support and automation systems (ADAS) contribute. We performed semi-structured in-depth interviews with 15 HGV drivers to assess their visual and cognitive processes while merging, interactions with other road users, and attitudes towards ADAS as a basis for future support and automation system design. Results show that the reported execution of merging varies substantially between drivers. Drivers reported reliance on courtesy of other traffic but stated that car drivers are often causing conflicts, whereas other HGV drivers are more cooperative. Current ADAS were perceived as useful in general, with remarks about misuse and abundance of systems. We recommend the introduction of driver support and automation systems which facilitate cooperative behaviour and support effective communication.@en

Merging sections are challenging for drivers of heavy goods vehicles. Visual support for merging was evaluated in a simulator. Experiment 1 tested HMIs that provided participants (n = 5) driving on the on-ramp with a top view or various forms of speed advice for accelerating behind or in front of a truck platoon on the freeway. Experiment 2 tested HMIs that provided drivers (n = 18) on the acceleration lane with a top view complemented with speed and gap advice for finding a gap to merge in. Experiment 1 showed that speed advice yielded less unnecessary braking compared to unsupported driving. In Experiment 2, speed advice yielded low satisfaction ratings. Our results highlight the potential of visual support and stress the importance of not visually overloading the driver.

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This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.

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We provide a brief comment on the work of Martinussen et al. (2017), who studied the relationships between self-reported driving behavior, registered traffic offences, and registered crash involvement. It is argued that if the number of crashes is small, then the correlation with crashes is also small. Our analysis of the SHRP2 naturalistic driving study shows that the violations score of the Driver Behavior Questionnaire and the Sensation Seeking Scale exhibit small correlations with recorded crashes, and small-to-moderate correlations with recorded near-crashes and measures of driving style.

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