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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.
Various kinds of demerit point systems have been developed and implemented in European countries, aimed at tackling repeat offences in road transport by acting as a deterrent and providing sanctioning. The impact of a demerit point system on the number of crashes is often reported to be significant, but temporary. The objective of the EU BestPoint project was to establish a set of recommended practices that would result in a more effective and sustainable contribution of demerit point systems to road safety. A high actual chance of losing the licence and a high perceived chance of losing the licence are basic prerequisites for the effective operation of demerit point systems. For measures applied within the context of a demerit point system, a four-step-approach is recommended: warning letter, driver improvement course, licence withdrawal, rehabilitation course. Further recommendations concern issues like points and offences, e.g. which offences should lead to points, target groups, and the administration of demerit point systems. The final result of the EU BestPoint project is a handbook (van Schagen & Machata, 2012) which provides a concise overview of all recommended practices. The presentation/paper outlines how sustainable safety improvements can be achieved if national demerit point systems are implemented and maintained according to the recommended practices. In addition, potential further steps towards an EU-wide demerit point system (cross-border exchange on points and/or offences) are presented.
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.
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 effect of fatigue on driving has been compared to the effect of alcohol impairment in both driver performance and crash studies. However are crash characteristics and causation mechanisms similar in crashes involving fatigue to those involving alcohol when studied in the real world? This has been explored by examining data held in the EC project SafetyNet Accident Causation Database. Causation data was recorded using the SafetyNet Accident Causation System (SNACS). The focus was on Cars/MPV crashes and drivers assigned the SNACS code Alcohol or Fatigue. The Alcohol group included 44 drivers and the Fatigue group included 47. "Incorrect direction" was a frequently occurring critical event in both the Alcohol and Fatigue groups. The Alcohol group had more contributory factors related to decision making and the Fatigue group had more contributory factors relating to incorrect observations. This analysis does not allow for generalised statements about the significance of the similarities and differences between crashes involving alcohol and fatigue, however the observed differences do suggest that attempts to quantify the effect of fatigue by using levels of alcohol impairment as a benchmark should be done with care.
With an ever rising human life expectancy the share of elderly people in society is constantly rising. This leads to the fact that at the same rate the share of people with age related diseases such as dementia and poor eyesight taking part in traffic will rise and therefore traffic accidents caused by this group of people due to the disease will play an ever greater role. This Situation will be among the future challenges of road safety work. At present this study displays specific characteristics of accidents caused by elderly car drivers (aged 65 or higher) based on the analysis of the German In-Depth Accident Study GIDAS. Herein almost 1000 elderly car drivers were identified as accident participants in the years 2008 to 2011. The focus of this study lies on identifying special types of accidents which are caused by elderly drivers and on characterizing these types with the information gathered on scene and by interviewing the participants. The main evidence analyzed is the knowledge about the accident locality, the trajectories of the participants as well as the reasons for the occurrence of the accidents. Furthermore personal information such as the personal condition before the accident and driving purposes is used to identify patterns of contributing circumstances for accidents caused by elderly traffic participants.
Nowadays human-created systems are increasing in complexity due to the interaction of humans and technology. Especially road traffic systems are composed of multitudinous resources (e.g. personnel, vehicles, organizations, etc.), which make it even harder to anticipate the positive and negative effects on safety. One key in achieving a significant reduction of fatalities is seen in driver assistant systems counterbalancing the lack of drivers' capabilities. But the actual outcome of implementing these sophisticated technologies especially on influencing driver's capabilities are yet unknown. Latest research exemplifies an increase of reaction times of drivers in case of dysfunctional driver assistant systems. This research paper applies STAMP/STPA (STAMP = systems-theoretic accident model and processes; STPA = systems-theoretic process analysis) to the German automobile traffic system focusing on the effects of driver assistant systems on drivers. By doing so, the potential hazards caused by technology can be identified.
Introduction: Spine injuries pose a considerable risk to life and quality of life. The total number of road deaths in developed countries has markedly decreased, e.g. in Germany from over 20000 in 1970 to less than 4000 in 2010, but little is known how this is reflected in the burden of spine fractures of motor vehicle users. In this study, we aimed to show the actual incidence of spine injuries among drivers and front passengers and elucidate possible dependencies between crash mechanisms and types of injuries.
From literature well-known analyzes on risks, hazards and causes of accidents of older drivers are amended by the present study in which a comparison of the specific features of accident causes of older car drivers (older than 60 years) and of younger car drivers (under 25 years) is conducted. Mainly the question is pursued if specific errors, mistakes and lapses are predominant in the two different age groups. The analysis system ACAS (Accident Causation Analysis System) used hereby consists of a sequential system of accident causation factors from the human, the technical and the infrastructural field, whereupon for this study the influence of the human features on the accident development in two different age groups is of interest. ACAS is both an accident model and an analysis and classification system, which describes the human participation factors of an accident and their causes in the temporal sequence (from the perceptibility to concrete action errors) taking into consideration the logical sequence of individual basic functions. In five steps (categories) of a logical and temporal sequence the hierarchical system makes human functions and processes as determinants of accident causes identifiable. The methodology specifically focuses on the use in so-called "In-Depth" and "On-Scene" investigation studies. With the help of the system for each accident participant one or more of five hypotheses of human cause factors are formed and then specified by appropriate verification criteria. These hypotheses in turn are further specified by indicators in such manner that the coding of the causation factors by a code system meets the needs of database processing and are accessible to a quantitative data analysis. The first results of the descriptive comparison of the two age groups concern mainly differences in the functional levels "information admission/perception" (where the elderly drivers have more difficulties than the young ones) and "information processing/evaluation" (where the younger drivers show more problems). Concerning the cognitive function of "planning" the group of younger drivers seems to be more often involved in an accident because of excessive speed.