Acknowledgement: This work is partly funded by SAFER Vehicle and Traffic Safety Centre at 12Chalmers and Chalmers Area of Advanced Transport. The work was carried out within the 13 SAFER collaborative environment. We thank the reviewers for their constructive feedback. 14 15 Objective: To outline a conceptual framework for understanding driving style and, based on 19 this, review the state-of-the-art research on driving styles in relation to road safety. 20Background: Previous research has indicated a relationship between the driving styles 21

In Europe, the number of road crashes is steadily decreasing every year. However, the incidence of bicycle crashes is not declining as fast as that of car crashes. In Sweden, cyclists are the most frequently injured road users. Collisions between bicycles and motorized vehicles are of particular concern because the high speed and large mass of motorized vehicles create a high risk of serious injury to cyclists. In Sweden's urban areas, bicycle lanes keep bicycles separated from motorized vehicles, but on rural roads bicycle lanes are often absent, requiring drivers to interact with cyclists-usually by overtaking them. During this maneuver, drivers regulate speed and lateral position, negotiating with potential oncoming traffic to stay within their comfort zones while approaching and passing cyclists. In this study an instrumented bicycle recorded 145 overtaking maneuvers performed by car and truck drivers on public rural roads in Sweden. The bicycle was equipped with a LIDAR and two cameras to assess how drivers approached and circumvented the bicycle. The collected data allowed us to identify four overtaking phases and quantify the corresponding driver comfort zones. The presence of an oncoming vehicle was the factor that most influenced the maneuver, whereas neither vehicle speed, lane width, shoulder width nor posted speed limit significantly affected the driver comfort zone or the overtaking dynamics.

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Predictive processing has been proposed as a unifying framework for understanding brain function, suggesting that cognition and behaviour can be fundamentally understood based on the single principle of prediction error minimization. According to predictive processing, the brain is a statistical organ that continuously attempts get a grip on states in the world by predicting how these states cause sensory input and minimizing the deviations between the predicted and actual input. While these ideas have had a strong influence in neuroscience and cognitive science, they have so far not been adopted in applied human factors research. The present paper represents a first attempt to do so, exploring how predictive processing concepts can be used to understand automobile driving. It is shown how a framework based on predictive processing may provide a novel perspective on a range of driving phenomena and offer a unifying framework for traditionally disparate human factors models.

The involvement of cyclists in road crashes has not been decreasing with the same magnitude as the involvement of other road users. In particular, the interactions between cyclists and motorized traffic can lead to high-severity crashes. To improve the safety of these interactions, a thorough understanding of road user behaviour is first needed. In this study, we focused on drivers overtaking cyclists on rural roads. The two main objectives of this study were to develop models that predicted: (a) drivers' decisions to perform either a flying or an accelerative overtaking manoeuvre in the presence of oncoming traffic, and (b) the lateral comfort distance that drivers maintain from cyclists during the overtaking.A driving simulator study was designed to assess driver decision-making during the overtaking. The 37 drivers who participated in the study each performed seven overtaking manoeuvres with oncoming traffic. Out of the 259 overtaking manoeuvres, 168 were flying and 91 were accelerative. Binary logisticregression models with mixed effects predicted the type of overtaking strategy (flying or accelerative).Driving speeds were found to significantly affect the strategy. The overall performance of the models predicting the strategy was 85-90%. Models were also developed for predicting the lateral comfort distance. The results show that the lateral comfort distance is mostly affected by the longitudinal distance between the subject vehicle and the oncoming vehicle, the longitudinal distance between the subject vehicle and the cyclist, and the presence of an oncoming vehicle-as well as by the drivers' characteristics (sensation seeking in flying overtaking manoeuvres and ordinary violations in accelerative manoeuvres). The root mean square error, which was used to assess the performance of the models, ranged from 0.56-0.62.In conclusion, the models predicting the overtaking strategy performed reasonably well, while the models predicting lateral distance did not provide accurate predictions. The models predicting overtaking strategy may support 1) the development and evaluation of active safety systems, 2) the design of automated driving, and 3) policy making.

Objective: This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.

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