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The data situation for quantifying the proportion of accidents avoided by the introduction of active safety systems is incomplete, since there is generally no data available on the accidents avoided by the technology in question. In this paper, a split-register approach is suggested and compared with the classical case-control approach known from epidemiologic applications. Provided a set of assumptions hold, which can reasonably be made in such data situations, the split register approach allows inferences on the population accident risk. For both approaches the benefits of basing the analysis on the results of a logistic regression to adjust for confounding factors are outlined. The biasing effects of violating key assumptions are discussed and the split-register approach is demonstrated using the example of the active safety system ESP with data from the German in-depth accident study GIDAS.
Active safety systems are aimed at accident prevention, hence the knowledge required for their development is different from that required for passive safety systems aimed at injury prevention. Particularly, knowledge about accident causation is required. When looking at existing accident causation data, it is argued it fails to explain in sufficient detail how and why the accidents occur. Therefore, there is a need for detailed micro-level descriptions of accident causation mechanisms, and also of methodologies suitable for creating such descriptions. One study addressing these needs is the Swedish project FICA (Factors Influencing the Causation of Accidents and Incidents), where an accident investigation methodology suitable for active safety is developed, and in-depth accident investigations following this methodology are carried out on-scene in the area of Gothenburg by a multidisciplinary team. A preliminary aggregated analysis of different cases shows that the methodology developed is adequate for pointing out common contributing factors and devising principal countermeasures.
There is a need for detecting characteristics of pedestrian movement before car-pedestrian collisions to trigger a fully reversible pedestrian protection system. For this purpose, a pedestrian sensor system has been developed. In order to evaluate the effectiveness of the sensor system, the in-depth knowledge of car-pedestrian impact scenarios is needed. This study aims at the evaluation of the sensor system. The accident data are selected from the STRADA database. The accident scenarios available in this database were evaluated and the knowledge of the most common scenarios was developed in terms of the pedestrian trajectory, the pedestrian speed, the car trajectory, the car velocity, etc. A mathematical model was then established to evaluate the sensor system with different detective angles. It was found that in order to detect all the pedestrians in the most common scenarios on time the sensor detective angle must be kept larger than 60 degrees.
Die EU hat für die Verkehrssicherheit in Europa ein anspruchsvolles Ziel vorgegeben: Bis 2010 soll die Anzahl der im Straßenverkehr Getöteten gegenüber 2000 halbiert werden. Für Deutschland kann eine erfolgreiche Zwischenbilanz gezogen werden: In den letzten 5 Jahren nahm trotz Vergrößerung des Kraftfahrzeugbestandes um 6% die Anzahl der Verkehrstoten um 29% ab, in den vergangenen 10 Jahren ist ein Rückgang um 43% zu verzeichnen. Diese im internationalen Vergleich überdurchschnittlichen Erfolge sind nicht zuletzt auch auf Fortschritte in der Fahrzeugtechnik zurückzuführen, wobei die zunehmende Verbreitung von Systemen der Aktiven Sicherheit wie ABS, BAS, ESP einen entscheidenden Anteil hat. Nach der deutlichen Reduzierung von Fahrunfällen durch ESP-® stehen nun die Auffahrunfälle im Fokus der Sicherheitsentwicklung von Mercedes-Benz. Das Paket aus verbessertem rückwärtigen Signalbild (Adaptives Bremslicht) und Brems-Assistent (BAS) wurde kürzlich durch radarbasierte Bremsassistenz ergänzt (BAS PLUS und PRE-SAFE-®-Bremse). Der Beitrag geht auf Funktion und Wirksamkeit der einzelnen Systeme ein und gibt einen Ausblick in die nähere Zukunft.
The objective of this study was to identify aspects of the individual experience and behaviour of drivers in intersection accidents. A total of 40 accident drivers sketched their ideas and expectations relating to intersection assistance using the method of Structure Formation Technique. Using this method prepared content cards and relation cards for a subject matter are formed together in a structure through the application of an explicit set of rules. The structures generated in this process were compared with the structures of 20 control persons who have not recently experienced an accident at intersections. The basis for this comparison was a case-control design with matched samples regarding the variables age, sex, education, occupation, driving experience and annual mileage. The results of the accident reports indicate that additional assistance is instrumental in the perception of other road users. Generally the interviewed drivers were open-minded towards the use of intersection assistance systems. Drivers who have recently experienced an accident at intersections significantly more often approved of warning assistance in their vehicle than drivers who have not recently experienced an accident. Further accident experienced drivers favoured warning and information via audio warning more frequently. The ideas of the drivers were strongly shaped by the experiences with already available advanced driver assistance systems. Hence acoustic and visual warnings were generally preferred to tactile warnings. The findings also indicate a relationship between the variable age and the acceptance of automatic vehicle intervention, and the suggestion of a head up display as a configuration of a visual warning system.
Methods for analyzing the efficiency of primary safety measures based on real life accident data
(2009)
Primary safety measures are designed to help to avoid accidents or, if this is not possible, to stabilize respectively reduce the dynamics of the vehicle to such an extent that the secondary safety measures are able to act as good as possible. The efficiency of a primary safety measure is a criterion for the effectiveness, with which a system of primary safety succeeds in avoiding or mitigation the severity of accidents within its range of operation and in interactionwith driver and vehicle. Based on Daimler-´s philosophy of the "Real Life Safety" the reflection of the real world accidents in the systems range of operation is both starting point as well as benchmark for its optimization. This paper deals with the methodology to perform assessments of statistical representative efficiency of primary safety measures. To be able to carry out an investigation concerning the efficiency of a primary safety measure in a transparent and comparable way basic definitions and systematics were introduced. Based on these definitions different systematic methods for estimating efficiency were discussed and related to each other. The paper is completed by presenting an example for estimating the efficiency of actual "single" and "multi" connected primary safety systems.
Over the last decades the number of traffic accident fatalities on German roads decreased by 77% down to 4968 in the year 2007. This positive development is due to optimisations of vehicle safety, roads and infrastructure and medical rescue issues. Up to now mostly the optimisations of secondary safety measures lead to this effect on vehicle safety. Since some years more and more driver assistance systems are available and lead to a further reduction of all accidents. These new systems are often comfort systems and have not primarily been developed to increase vehicle safety. In contrast to secondary safety systems primary safety systems are able to mitigate and avoid accidents. So in the future it is important to estimate the benefit of these systems in reducing accident numbers as well. Current benefit estimation methods mostly focus on a single system only and not on the combination of systems. In this paper a new method for a multivariate benefit estimation based on real accident data is developed. The paper describes the basic method to estimate the benefit of primary and secondary safety systems in combination. With the presented method the benefit will not be overestimated as it would be by a simple addition of the benefits of single systems. The model will be validated by a multivariate prospective benefit estimation of different vehicle safety systems in comparison to single benefit estimations of the same systems. For this the German In-Depth Accident Database is used. The results show the importance to implement the interactions of safety systems in the estimation process and rate the overestimation by a simple addition of the single system benefits. The validation includes primary and secondary safety systems in combination. The validation is done using more than 3500 real accidents which were initiated by cars. This sample out of the GIDAS database is representative for the current accident situation in Germany. The paper shows the necessity of a multivariate estimation of the benefit for existing and future safety systems.
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.
Im Jahr 2004 fand an der Medizinischen Hochschule Hannover die erste ESAR-Konferenz (Expert Symposium on Accident Research) statt. Die Idee einer internationalen Konferenz war aus der Notwendigkeit entstanden, diejenigen Experten zusammen zu bringen, die weltweit tätig sind und Verkehrsunfälle wissenschaftlich analysieren, um ihre Ergebnisse gemeinsam zu diskutieren und einem Zielpublikum von Behördenvertretern, Entwicklungsingenieuren der Automobilindustrie und anderen Wissenschaftlern darzubringen. Die durch Professor Otte initiierte und nun zum vierten Male organisierte Konferenz fand eine breite Akzeptanz und ist mittlerweile Bestandteil einer Konferenzlandschaft mit Zielvorträgen von der Fahrzeugsicherheit bis hin zur Verletzungsanalyse und den Unfallursachen. ESAR kann als wissenschaftliches Kolloquium und Plattform für einen Informationsaustausch der Unfallforscher angesehen werden, die sich speziell mit Methoden der Unfalluntersuchung, mit Verletzungsmechanismen und der Bewertung von Verletzungen, Unfallursachen und anderen Bereichen der statistischen Unfalldatenanalyse befassen. Experten aus den Bereichen der Medizin, der Verkehrspsychologie und der Technik sowie Vertreter zuständiger Behörden kommen hier zusammen, um die Erfahrungen in der Unfallprävention und der Unfallrekonstruktion zu diskutieren und um der Forschung neue Felder zu eröffnen. Neben den Belangen der Europäischen Gemeinschaft werden auch die weltweit zu registrierenden hohen Verletztenzahlen berücksichtigt. Wissenschaftliche Vorträge aus aller Welt tragen dazu bei, geeignete Maßnahmen und Methoden zur Analyse und drastischen Verringerung der Zahl der bei Verkehrsunfällen Getöteten zu entwickeln. Die Zusammensetzung des Teilnehmerkreises dieser wie früherer ESAR-Konferenzen hat längst eine über Europa hinausgreifende Internationalitaet erreicht und bietet daher einen aufschlussreichen Überblick über die verschiedenen Standards bestehender Verkehrssicherheit und unterschiedlichen Unfallszenarien und über die Anforderungen an die Unfallanalysen. Die Ergebnisse langjähriger Forschungsarbeiten in Europa, USA, Australien und asiatischen Ländern beinhalten unterschiedliche infrastrukturelle Zusammenhänge und geben Erkenntnisse über Population, Fahrzeugbestand und Fahrereigenschaften. Derartige Informationen bilden eine exzellente Basis für abzuleitende Empfehlungen und Maßnahmen für die Erhöhung der Verkehrssicherheit international.
Accident data shows that the vast majority of pedestrian accidents involve a passenger car. A refined method for estimating the potential effectiveness of a technology designed to support the car driver in mitigating or avoiding pedestrian accidents is presented. The basis of the benefit prediction method consists of accident scenario information for pedestrian-passenger car accidents from GIDAS, including vehicle and pedestrian velocities. These real world pedestrian accidents were first reconstructed and the system effectiveness was determined by comparing injury outcome with and without the functionality enabled for each accident. The predictions from Volvo Cars" general Benefit Estimation Model are refined by including the actual system algorithm and sensing models for a relevant car in the simulation environment. The feasibility of the method is proven by a case study on a authentic technology; the Auto Brake functionality in Collision Warning with Full Auto Brake and Pedestrian Detection (CWAB-PD). Assuming the system is adopted by all vehicles, the Case Study indicates a 24% reduction in pedestrian fatalities for crashes where the pedestrians were struck by the front of a passenger car.