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The changed focus in vehicle safety technology from secondary to primary safety systems need to evolve new methods to investigate accidents, high critical, critical and normal driving situations. Current Naturalistic Driving Studies mostly use vehicles that are highly equipped with additional measuring devices, video cameras, recording technology, and sensors. These equipped fleets are very expensive regarding the setup and administration of the study. Due to the great rarity of crashes it is additionally necessary to have a high distribution and a homogeneous distribution of subject groups. At the end all these facts are leading to a very expensive study with a manageable number of data. Smartphones are becoming more and more popular not only for younger people. Contrary to traditional mobile phones they are mostly equipped with sensors for acceleration and yaw rates, GPS modules as well as cameras in high definition resolution. Additionally they have high-performance processors that enable the execution of CPU-intensive tools directly on the phone. The wide distribution of these smartphones enables researchers to get high numbers of users for such studies. The paper shows and demonstrates a software app for smartphones that is able to record different driving situations up to crashes. Therefore all relevant parameter from the sensors, camera and GPS device are saved for a given duration if the event was triggered. The complete configuration is independently adjustable to the relevant driver and all events were sent automatically to the research institute for a further process. Direct after the event, interviews with the driver can be done and important data regarding the event itself are documented. The presentation shows the methodology and gives a demonstration of the working progress as well as first results and examples of the current study. In the discussion the advantages of this method will be discussed and compared with the disadvantages. The paper shows an alternative method to investigate real accident and incident data. This method is thereby highly cost efficient and comparable with existing methods for benefit estimation.
The main focus of the benefit estimation of advanced safety systems with a warning interface by simulation is on the driver. The driver is the only link between the algorithm of the safety system and the vehicle, which makes the setup of a driver model for such simulations very important. This paper describes an approach for the use of a statistical driver model in simulation. It also gives an outlook on further work on this topic. The build-up process of the model suffices with a distribution of reaction times and a distribution of reaction intensities. Both were combined in different scenarios for every driver. Each scenario has then a specific probability to occur. To use the statistical driver model, every accident scene has to be simulated with each driver scenario (combinations of reaction times and intensities). The results of the simulations are then combined regarding the probabilities to occur, which leads to an overall estimated benefit of the specific system. The model works with one or more equipped participants and delivers a range for the benefit of advanced safety systems with warning interfaces.
A reduction of around 48% of all road fatalities was achieved in Europe in the past years including a reduced number of fatalities with an older age. However, among all road fatalities, the proportion of elderly is steadily increasing. In an ageing society, the European (Horizon2020) project SENIORS aims to improve the safe mobility of older road users, who have different transportation habits compared to other age groups. To increase their level of safe mobility by determining appropriate requirements for vehicle safety systems, the characteristics of current road traffic collisions involving the elderly and the injuries that they sustain need to be understood in detail. Hereby, the paper focuses on their traffic participation as pedestrian, cyclist or passenger car occupant. Following a literature review, several national and international crash databases and hospital statistics have been analysed to determine the body regions most frequently and severely injured, specific injuries sustained and types of crashes involved, always comparing older road users (65 years and more) with mid-aged road users (25-64 years). The most important crash scenarios were highlighted. The data sources included European statistics from CARE, data on national level from Germany, Sweden, Italy, United Kingdom and Spain as well as in-depth crash information from GIDAS (Germany), RAIDS (UK), CIREN and NASS-CDS (US). In addition, familiar hospital data from Germany (TraumaRegister DGU-®), Italy (Italian Register of Acute Traumas) and UK hospital statistics (TARN) were included in the study to gain further insight into specific injury patterns. Comprehensive data analyses were performed showing injury patterns of older road users in crashes. When comparing with mid-aged road users, all databases showed that the thorax body region is of particularly high importance for the older car occupant with injury severities of AIS 2 or AIS 3+, whereas the body regions lower extremities, head and thorax need to be considered for the older pedestrians and cyclists. Besides these comparisons, the most frequent and severe top 5 injuries were highlighted per road user group. Further, the most important crash configurations were identified and injury risk functions are provided per age group and road user group. Although several databases have been analysed, the picture on the road safety situation of older road users in Europe was not complete, as only Western European data was available. The linkage between crash data and hospital data could only be made on a general level as their inclusion criteria were quite different.
The levels of continuous vehicle automation have become common knowledge. They facilitate overall understanding of the issue. Yet, continuous vehicle automation described therein does not cover "automated driving" as a whole: Functions intervening temporarily in accident-prone situations can obviously not be classified by means of continuous levels. Continuous automation describes the shift in workload from purely human driven vehicles to full automation. Duties of the driver are assigned to the machine as automation levels rise. Emergency braking, e.g., is obviously discontinuous and intensive automation. It cannot be classified under this regime. The resulting absence of visibility of these important functions cannot satisfy " especially in the light of effect they take on traffic safety. Therefore, in order to reach a full picture of vehicle automation, a comprehensive approach is proposed that can map out different characteristics as "Principle of Operation" at top level. On this basis informing and warning functions as well as functions intervening only temporarily in near-accident situations can be described. To reach a complete picture, levels for the discontinuous, temporarily intervening functions are proposed " meant to be the counterpart of the continuous levels already in place. This results in a detailed and independent classification for accident-prone situations. This finally provides for the visibility these important functions deserve.
While cyclists and pedestrians are known to be at significant risk for severe injuries when exposed to road traffic accidents (RTAs) involving trucks, little is known about RTA injury risk for truck drivers. The objective of this study is to analyze the injury severity in truck drivers following RTAs. Between 1999 and 2008 the Hannover Medical School Accident Research Unit prospectively documented 43,000 RTAs involving 582 trucks. Injury severity including the abbreviated injury scale (AIS) and the maximum abbreviated injury scale (MAIS) were analyzed. Technical parameters (e.g. delta-v, direction of impact), the location of accident, and its dependency on the road type were also taken into consideration. The results show that the safety of truck drivers is assured by their vehicles, the consequence being that the risk of becoming injured is likely to be low. However, the legs especially are at high risk for severe injuries during RTAs. This probability increases in the instance of a collision with another truck. Nevertheless, in RTAs involving trucks and regular passenger vehicles, the other party is in higher risk of injury.
Driver distraction
(2017)
This report for the Institute of Advanced Motorists (IAM) summarises recent research and knowledge from scientific studies about distracted driving. The report defines what it means to be "distracted" when driving, discusses the impact of distraction on driver behaviour and safety, and what can be done to reduce distracted driving. The focus of distraction discussed here relates to how drivers engage with technology when driving. The report begins with a background to driver distraction, followed by discussion about what is actually meant by driver distraction. It is then considered why humans cannot successfully do two things at the same time, particularly within the context of driving. The subsequent section summarises the scientific research findings to date with regard to driver distraction and technology, and how this affects different types of road user. Recommendations for how driver distraction can be mitigated in the real world and a summary conclude the report. Responses to common questions raised by drivers are presented in Appendix A.