A method for identifying aggressive driving by using naturalistic driving data

Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. By understanding the behavior of the drivers and finding quantitative ways to categorize the behavior associated with higher crash risk, programs for modifying driver behavior towards safer driving can be designed. The objective of this study is to identify aggressive drivers by metrics calculated from naturalistic driving data. The drivers are separated by the aggressive behavior of following too closely to a front vehicle, i.e. tailgating. Furthermore, two jerk metrics are calculated to identify aggressive drivers: a) number of large positive jerks when pressing the gas pedal and b) number of large negative jerks when pressing the brake pedal. Moreover, drivers’ gender, Arnett Inventory of Sensation Seeking (AISS) score, Driver Behavior Questionnaires (DBQ) and country effects on the metrics are analyzed.The results show that the aggressive drivers, defined for car following situations using tailgating metric, were associated with significantly higher frequency of using large negative jerk. The results could be potentially applied in programs for driver training and education, advanced driver coaching, and in the context of usage-based insurance

Detailed description of bicycle and passenger car collisions based on insurance claims

Today, cyclists constitute the highest percentage of severely injured road users in Sweden (Trafikverket, 2014). In particular, collisions between bicycles and motor vehicles often have the most serious outcomes. However, bicycle accidents are underreported in official databases (Elvik and Mysen, 1999) and information regarding accident details is very limited. The aims of this study were to investigate the frequency and severity of different accident scenarios and identify relevant factors and circumstances of these collisions. The detailed information about bicycle-car collisions at all levels of crash severity was obtained from insurance claims. A dataset of 882 collisions between bicycles and passenger cars in Sweden (2005-2012) was used for analysis (Isaksson-Hellman, 2012). Results showed that in over 78% of all bicycle-car collisions, the bicycle and car crossed each other's paths. In over 53% of these collisions, the cyclist crossed the roadway while following a bicycle path. In about half of these collisions the drivers reported that they did not see the cyclists beforehand. Collisions in which the bicycle and car were traveling in the same/opposite direction, the next most frequent type of collision, were less frequent (11%), but the injury severity was on average higher. These novel data, which cannot be found in other data sources, will enable new analyses, contributing to a better understanding of bicycle-car collisions in real road traffic situations. (C) 2016 Elsevier Ltd. All rights reserved

An evaluation of the real-world safety effect of a lane change driver support system and characteristics of lane change crashes based on insurance claims data

<p><b>Objective</b>: Lane changes, which frequently occur when vehicles travel on major roads, may contribute to critical situations that significantly affect the traffic flow and traffic safety. Thus, knowledge of lane change situations is important for infrastructure improvements as well as for driver support systems and automated driving development projects. The objectives of this study were to evaluate the crash avoidance performance of a lane change driver support system, the Blind Spot Information System (BLIS) in Volvo car models, and to describe the characteristics of lane change crashes by analyzing detailed information from insurance claim reports.</p> <p><b>Methods</b>: An overall evaluation of the safety effect of BLIS was performed by analyzing crash rate differences in lane change situations for cars with and without the optionally mounted BLIS system based on a population of 380,000 insured vehicle years. Further, crashes in which the repair cost of the host vehicle exceeded approximately US$1,250 were selected and compared. Finally, the study examined different precrash factors and crash configurations, using in-depth insurance claims data from representative lane change crash cases including all severity levels in a population of more than 200,000 insured vehicle years.</p> <p><b>Results</b>: The technology did not significantly reduce the overall number of crashes when all types of lane change crashes and severity levels were considered, though a significant crash-reducing effect of 31$1,250 were analysed. Cars with the BLIS technology also have a 30% lower claim cost on average for reported lane change crashes, indicating reduced crash severity. When stratifying the data into specific situations, by collecting precrash information in a case-by-case study, the influence of BLIS was indicated to differ for the evaluated situations, although no significant results were found. For example, during general lane change maneuvers (i.e., not while exiting or entering highways or during weaving/merging situations) the crash rate was reduced by 14%, whereas in weaving/merging situations the crash rate increased.</p> <p><b>Conclusions</b>: The insurance data analyzed provided useful information about real-world lane change crash characteristics by covering collisions in all crash severities and thus revealed information beyond what is available in, for example, data sets of police-reported crashes. This will guide further development of driver support systems. For crashes with repair cost exceeding US$1,250, a significant crash reduction was found, although the technology did not significantly reduce the total number of lane change crashes. An average lower insurance claim cost for cars equipped with the BLIS technology also indicated that the technology contributes to reduced crash severity even if crashes were not totally avoided. Stratifying the data into different lane change crash situations gave indications of the condition-specific performance of the system, even if the results were not statistically significant at the 95% level.</p

Identification of aggressive driving from naturalistic data in car-following situations

Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology

Pre-crash factors influencing drivers of older ages in intersection collisions

The objective of this study is to understand the driver needs from a preventive and protective perspective focusing on cognitive pre-crash factors influencing the older driver in intersection collisions. The study combines information from a statistical dataset and 33 in-depth cases. The statistical data confirms results from prior studies indicating that the 55+ drivers are relatively more involved in collisions occurring in intersections having an overall higher injury risk compared to the comparative group of drivers aged 25-35. The in-depth data indicates that missed observations were one major cause in the development of the collision scenarios studied. The major possible causes together with the contributing causes are analyzed and discussed