Sonstige
It has been pointed that most of the accidents on the roads are caused by driver faults, inattention and low performance. Therefore, future active safety systems are required to be aware of the driver status to be able to have preventative features. This probe study gives a system structure depending on multi-channel signal processing for three modules: Driver Identification, Route Recognition and Distraction Detection. The novelty lies in personalizing the route recognition and distraction detection systems according to particular driver with the help of driver identification system. The driver ID system also uses multiple modalities to verify the identity of the driver; therefore it can be applied to future smart cars working as car-keys. All the modules are tested using a separate data batch from the training sets using eight drivers" multi-channel driving signals, video and audio. The system was able to identify the driver with 100% accuracy using speech signals of length 30 sec or more and a frontal face image. After identifying the driver, the maneuver/ route recognition was achieved with 100% accuracy and the distraction detection had 72% accuracy in worst case. In overall, system is able to identify the driver, recognize the maneuver being performed at a particular time and able to detect driver distraction with reasonable accuracy.
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.
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.
This paper set out to examine the possibilities for injury avoidance implications for older drivers in crashes, based on crash and injury patterns among older drivers and current trends in ageing in most western societies. A number of safety technologies were identified and discussed which have potential for improving vehicle older driver crash avoidance and crashworthiness. While there were some promising estimates available of the likely benefits of this technology for improving safety, it is evident that they need to be confirmed for older drivers, given their age-related disabilities and sensory limitations. Further research is urgently required to ensure that these technologies yield safety benefits without any disbenefits for older drivers.rn
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.
The Swedish National Road Administration (SNRA), the Japanese Automobile Research Institute (JARI) and the Federal Highway Research Institute (BASt) are co-operating in the International Harmonized Research Activities on Intelligent Transportation Systems (IHRA-ITS). Under this umbrella a joint study was conducted. The overall objective of this study was to contribute to the definition and validation of a "battery of tools" which enables a prediction and an assessment of changes in driver workload due to the use of in-vehicle information systems (IVIS) while driving. In this sense \"validation\" means to produce empirical evidence from which it can be concluded that these methods reliably discriminate between IVIS which differ in terms of relevant features of the HMI-design. Additionally these methods should also be sensitive to the task demands imposed on the driver by the traffic situation and their interactions with HMI-design. To achieve these goals experimental validation studies (on-road and in the simulator) were performed in Sweden, Germany and Japan. As a common element these studies focused on the secondary task methodology as an approach to the study of driver workload. In a joint German-Swedish on-road study the Peripheral Detection Task (PDT) was assessed with respect to its sensitivity to the complexity of traffic situations and effects of different types of navigation systems. Results show that the PDT performance of both the German and the Swedish subjects reflects the task demands of the traffic situations better than those of the IVIS. However, alternative explanations are possible which will be examined by further analyses. Results of this study are supplemented by the Japanese study where informational demands induced by various traffic situations were analysed by using a simple arithmetic task as a secondary task. Results of this study show that relatively large task demands can be expected even from simple traffic situations.
Description of road traffic related knee injuries in published investigations is very heterogeneous. The purpose of this study was to estimate the risk of knee injuries in real world car impacts in Germany focusing vulnerable road users (pedestrians, bicyclists and motorcyclists) and restrained car drivers. The accident research unit analyses technical and medical data collected shortly after the accident at scene. Two different periods (years 1985-1993 and 1995-2003) were compared focusing on knee injuries (Abbreviated Injury Scale (AISKnee) 2/3). In order to determine the influences type of collision, direction and speed as well as the injury pattern and different injury scores (AIS, MAIS, ISS) were examined. 1.794 pedestrians, 742 motorcyclists, 2.728 bicyclists and 1.116 car drivers were extracted. 2% had serious ligamentous or bony injuries in relation to all injured. The risk of injury is higher for twowheelers than for pedestrians, but knee injury severity is higher for the latter group. Overall the current knee injury risk is low and significant reduced comparing both time periods (27%, p<0,0001). Severe injuries (AISKnee 2/3) were below 1%). Improved aerodynamic design of car fronts reduced the risk for severe knee injuries significantly (p=0,0015). Highest risk of injury is for motorcycle followed by pedestrians, respectively. Knee protectors could prevent injuries by reducing local forces. The classically described dashboard injury was rarely identified. The overall injury risk for knee injuries in road traffic is lower than estimated and reduced comparing both periods. The aerodynamic shape of current cars compared to older types reduced the incidence and severity of knee injuries. Further modification and optimization of the interior and exterior design could be a proper measurement. Classic described injury mechanisms were rarely identified. It seems that the AIS is still underestimating extremity injuries and their long term results.
Novice drivers are at high risk for crash involvement. We performed an analysis of causations, injury patterns and distributions of novice drivers in cars and on motorcycles in road traffic as a basis for proper measurements. Method Data of accident and hospital records of novice drivers (licence < 2 years) were analysed focusing the following parameters: injury type, localisation and mechanism, Abbreviated Injury Scale (AIS), maximum AIS (MAIS), delta-v, collision speed and other technical parameters and have been compared to those of experienced drivers. In 18352 accidents in the area of Hannover (years1985"2004), 2602 novice drivers and 18214 experienced drivers were recorded having an accident. Novice car drivers were more often and severe injured than experienced and on motorcycles the experienced riders were at higher risk. Novice drivers of both groups sustained more often extremity injuries. 4.5 % novice car drivers were not restraint compared to 3.7 % of the experienced drivers and 6.1 % novice motorcycle drivers did not wear a proper helmet (versus 6.5 %). Severe injuries sustained at a rate of 20 % at collision speeds below 30 km/h and in 80% at collision speeds above 50 km/h. Novice car drivers drove significant older cars. The risk profile of novice drivers is similar to those of drivers older than 65 years. Structural protection and special lectures like skidding courses could be proper remedial action next to harder punishment of violations.
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.
Empirical vehicle crashworthiness studies are usually based on national or in-depth traffic accident surveys: Data on accident-involved cars/drivers are analysed in order to quantify the chance of driver injury and to assess certain risk factors like car make and model. As the cars/drivers involved in the same accident form a "cluster", where the size of the cluster equals the number of accident-involved parties, traffic accident survey data are typical multi-level data with accidents as first-level or primary and cars/drivers as secondlevel or secondary units (car occupants in general are to be considered as third level units). Consequently, appropriate statistical multi-level models are to be used for driver injury risk estimation purposes as these models properly account for the cluster structure of traffic accident survey data. In recent years various types of regression models for clustered data have been developed in the statistical sciences. This paper presents multi-level statistical models, which are generally applicable for vehicle crashworthiness assessment in the sense that data on single and multiple car crashes can be analysed simultaneously. As a special case of multi-level modelling driver injury risk estimation based on paired-by-collision car/driver data is considered. It is demonstrated that assessment results may be seriously biased, if the cluster structure inherent in traffic accident survey data is erroneously ignored in the data analysis stage.