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

Definition of run-off-road crash clusters - for safety benefit estimation and driver assistance development

Single-vehicle run-off-road crashes are a major traffic safety concern, as they are associated with a high proportion of fatal outcomes. In addressing run-off-road crashes, the development and evaluation of advanced driver assistance systems requires crash test scenarios that are representative of the variability found in real-world crashes. Current approaches subdivide crash data into predefined conflict situations. However, this approach does not take into account inherent patterns in the crash data variables, and may miss common mechanisms or factors. We apply hierarchical agglomerative cluster analysis of crash data variables to define similarities in a set of test scenarios that are representative of run-off-road crashes in the German In-Depth Accident Study (GIDAS) database. Out of 13 clusters, nine test scenarios are derived, corresponding to crashes characterised by: drivers drifting off the road in daytime and night-time, high speed departures, high-angle departures on narrow roads, highways, snowy roads, loss-of-control on wet roadways, sharp curves, and high speeds on roads with severe road surface conditions. In addition, each cluster was analysed with respect to crash variables related to the crash cause and reason for the unintended lane departure. The study shows that cluster analysis of representative data provides a statistically based method to identify relevant properties for run-off-road test scenarios. This was done to support development of vehicle-based run-off-road countermeasures and driver behaviour models used in virtual testing

Seat Belt Fit and Comfort for Older Adult Front Seat Passengers in Cars

An explorative user study was performed to study seat belt fit, perceived comfort and safety awareness of older adults in the front passenger seat of a large, stationary passenger car. The study included 55 participants between 65 and 80 years (32 males, 23 females). The participants buckled up in two scenarios, in a pre-defined seat position and in a self-adjusted preferred seat position. Anthropometric measures, photographs, and measurements of seat and seat belt positions were taken. Interviews were conducted regarding comfort perception and previous awareness of seat belt usage and discomfort. The results showed a change in seat belt fit due to older adults’ body compositions and increased BMI, and a limited safety awareness of non-optimal shoulder and lap belt fit. Some usually experienced discomfort in regular driving and used add-on accessories to increase sitting height and decrease sitting discomfort. These findings are important when designing restraint systems in future vehicles to ensure further improved safety for older adults

Integrated Safety: Establishing Links for a comprehensive virtual tool chain

As technologies for injury prevention and crash avoidance both contribute to injury reduction in car crashes, tools predicting the combined effect of all safety features are needed. This study aims at establishing a computer simulation methodology including two important elements for assessing this combined effect. The first element describes the states of the involved vehicles or objects at crash initiation regarding positions, orientations and velocities as parameters used for crash evaluation. The second element focuses on the car occupant, enabling computationally efficient prediction of occupant position transfer during pre-crash maneuvers. An extended aim is to demonstrate how data flows between these elements in an example case study.Real-world data from the Volvo Cars traffic accident database (VCTAD) was used as the basis for pre-crash simulations involving two cars, with and without a conceptual autonomous emergency braking (AEB) function. For cases in which the crash was not avoided by the AEB function, the crash configuration was identified. A simplified occupant kinematics model (SOCKIMO) was developed and applied to these remaining crashes, supporting the selection of crash situations to be analyzed in detail. The SAFER human body model (HBM) was used for simulation of the occupant response, providing information on pre-crash kinematics as well as the occupant crash response.As a result, a novel crash configuration definition for estimating the consequences of car crashes based on preceding events was established. The Volvo parametric crash configuration (VPARCC) definition can be used as a link between pre-crash and crash simulation tools as well as for illustrating sets of real-world accident data and how these change based on maneuvers preceding a crash. SOCKIMO results demonstrated occupant kinematics similar to those of volunteers, and the subsequent simulations using the SAFER HBM showed considerable changes in occupant crash response based on pre-crash vehicle kinematics.The VPARCC definition can also be applied to collision objects such as trucks or vulnerable road users. The developed SOCKIMO can be used to filter out cases from large crash data sets to be further analyzed with detailed models such as finite element active HBMs. By applying the more detailed HBM, the effects of avoidance maneuvers on occupant kinematics relevant for injury prediction can be evaluated. This approach would not be possible using simplified occupant models only (due to the lack of details) or by using detailed models only (due to the large simulation effort).The presented methodology for estimating combined safety performance can be used for transferring output from pre-crash simulations to input for crash simulations. The feasibility of combining the individual elements of this methodology was demonstrated in an example case where autonomous emergency braking led to a large change in the crash configuration and was predicted to introduce substantial occupant pre-crash excursion. In this example case, it was shown that the present A-HBM tool is able to cover the complete sequence from pre-crash maneuvers to crash in one single simulation

Automatic incident detection and classification at intersections

Collisions at intersections are common and their consequences are often severe. This paper addresses the need for information on accident causation; a knowledge that can be used to obtain more effective countermeasures. A novel method that can be applied to data recorded in a groundbased observation system or similar is proposedfor classifying vehicle interactions into a set ofpredefined traffic scenarios. The classification isbased on possible combinations of trajectories of two interacting vehicles that have passed through an intersection. Additionally, the authors present an incident detection algorithm that uses the classified vehicle interactions. This algorithm constitutes the core of a video-based automatic incident detection at intersections (AIDI) system. The performance of the AIDI system was successfullyverified both in a driving simulator and in real traffic conditions

Automatic incident detection and classification at intersections

Collisions at intersections are common and their consequences are often severe. This paper addresses the need for information on accident causation; a knowledge that can be used to obtain more effective countermeasures. A novel method that can be applied to data recorded in a groundbased observation system or similar is proposedfor classifying vehicle interactions into a set ofpredefined traffic scenarios. The classification isbased on possible combinations of trajectories of two interacting vehicles that have passed through an intersection. Additionally, the authors present an incident detection algorithm that uses the classified vehicle interactions. This algorithm constitutes the core of a video-based automatic incident detection at intersections (AIDI) system. The performance of the AIDI system was successfullyverified both in a driving simulator and in real traffic conditions

CAR-TO-CYCLIST CRASHES IN EUROPE AND DERIVATION OF USE CASES AS BASIS FOR TEST SCENARIOS OF NEXT GENERATION ADVANCED DRIVER ASSISTANCE SYSTEMS – RESULTS FROM PROSPECT

Systems available on the market address also conflicts with vulnerable road users (VRUs) such as pedestrians and cyclists. Within the European project PROSPECT (Horizon2020, funded by the EC) improved VRU ADAS systems are developed and tested. However, before determining systems’ properties and starting testing, an up-to-date analysis of VRU crashes was needed in order to derive the most important Use Cases (detailed crash descriptions) the systems should address. Besides the identified Accident Scenarios (basic crash descriptions), this paper describes in short the method of deriving the Use Cases for car-to-cyclist crashes. Method Crashes involving one passenger car and one cyclist were investigated in several European crash databases looking for all injury severity levels (slight, severe and fatal). These data sources included European statistics from CARE, data on national level from Germany, Sweden and Hungary as well as detailed accident information from these three countries using GIDAS, the Volvo Cars Cyclist Accident database and Hungarian in-depth accident data, respectively. The most frequent accident scenarios were studied and Use Cases were derived considering the key aspects of these crash situations (e.g., view orientation of the cyclist and the car driver’s manoeuvre intention) and thus, form an appropriate basis for the development of Test Scenarios. Results Latest information on car-to-cyclist crashes in Europe was compiled including details on the related crash configurations, driving directions, outcome in terms of injury severity, accident location, other environmental aspects and driver responsibilities. The majority of car-to-cyclist crashes occurred during daylight and in clear weather conditions. Car-to-cyclist crashes in which the vehicle was traveling straight and the cyclist is moving in line with the traffic were found to result in the greatest number of fatalities. Considering also slightly and seriously injured cyclists led to a different order of crash patterns according to the three considered European countries. Finally the paper introduced the Use Cases derived from the crash data analysis. A total of 29 Use Cases were derived considering the group of seriously or fatally injured cyclists and 35 Use Cases were derived considering the group of slightly, seriously or fatally injured cyclists. The highest ranked Use Case describes the collision between a car turning to the nearside and a cyclist riding on a bicycle lane against the usual driving direction. Discussion A unified European dataset on car-to-cyclist crash scenarios is not available as the data available in CARE is limited, hence national datasets had to be used for the study and further work will be required to extrapolate the results to a European level. Due to the large number of Use Cases, the paper shows only highest ranked ones