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The misuse of CRS (child restraint system) is one of the most urgent problems in connection of child safety in cars. Numerous field studies show that more than two thirds of all CRS are used in a wrong way. This misuse could lead to serious injuries for the children. Surprisingly the quality of CRS use is coded much better in accident data (e.g. GIDAS) than the results of observatory field studies show. It is expected that misuse of CRS was not detected by the accident teams in a large number of the cases. An essential part in improving child seats and their usability is the knowledge of the relation between misuse and resulting injuries. For that the analysis and experimental reconstruction of accidents is an important part. For allowing an exact experimental accident reconstruction, it is necessary to have detailed information about the securing situation of the child and about the installation of the CRS in the car.
This study updates previous IIHS studies comparing estimated delta Vs for crash tested vehicles to the distribution of estimated delta Vs in the National Automotive Sampling System (NASS) Crashworthiness Data System (CDS). The delta V estimates for 232 frontal crash tests at 64.4km/h into a deformable barrier with 40 percent overlap are compared with estimates from frontal offset crashes in the 1997-2004 NASS database. All delta V estimates were based on SMASH, the delta V estimating program used by NASS since 1997. Results indicated that for all vehicles tested by IIHS, SMASH delta Vs were, on average, 32 percent lower than impact speeds and about 28 percent lower than the expected delta V. Almost 80 percent of all real-world frontal crashes resulting in AIS 3+ injuries and just over 60 percent of all fatal crashes occur at or below the average estimated delta V calculated for crash tested vehicles.
The increasing economics in India has an enormous growth of its road traffic. As observed from official Indian accident statistics the number of road fatalities are one of the highest worldwide. In contrast to most industrialized nations they have an rapidly increasing trend. To come along with this trend it becomes more than essential to understand the traffic accident situation. The official Indian accident statistics gives a glimpse of only basic information. Therefore more detailed data is needed. By using In-depth accident data and officially representative statistics the current accident situation can be evaluated in India, if a suitable weighting methodology is considered. Hence in 2009/2010 a pilot study with the collaboration partner JP-Research India pvt. Ldt. was gathered in Tamil Nadu in south of India. In-depth accident investigations were done around the Coimbatore area on four highways. At first, the collected data is evaluated. Due to consequent and continuous further development based on the first approach a methodology similar to NASS/CDS/GES in the US and GIDAS in Germany was developed. Of course all relevant accident related parameters including pictures and severity information were collected. As a matter of fact based on scaled sketches and reconstruction benefit analyses can be done in order to analyze the accident scenery in India. As a first outcome influence from infrastructure, missing education and vehicle safety were identified as key parameters in order to reduce the number of accidents and casualties. To compare the accident situation against international standards an accident classification for left hand traffic was developed based on the German Insurance classification system. Looking into detail additional accident types were identified and added to create an Indian accident type catalogue. The positive results encouraged several OEMs to participate in this investigation and together with BOSCH a consortium was established in 2010/11. Within one year from beginning in May 2011 about 200 highway accidents were collected, reported and reconstructed using the new standard. Hence a first good overview of the accident situation is available for the Coimbatore Tamil Nadu area. The major target for establishing accident investigations is the extension towards other states of India and urban areas to achieve a better overview of the accident scenery. Therefore local and national authorities have to be embedded in order to strengthen the awareness against traffic safety.
Road condition acquisition and assessment are the key to guarantee their permanent availability. In order to maintain a country's whole road network, millions of high-resolution images have to be analyzed annually. Currently, this requires cost and time excessive manual labor. We aim to automate this process to a high degree by applying deep neural networks. Such networks need a lot of data to be trained successfully, which are not publicly available at the moment. In this paper, we present the GAPs dataset, which is the first freely available pavement distress dataset of a size, large enough to train high-performing deep neural networks. It provides high quality images, recorded by a standardized process fulfilling German federal regulations, and detailed distress annotations. For the first time, this enables a fair comparison of research in this field. Furthermore, we present a first evaluation of the state of the art in pavement distress detection and an analysis of the effectiveness of state of the art regularization techniques on this dataset.
The market introduction and penetration of electric vehicles can be seen as a milestone in order to reduce the environmental burden imposed by the transport sector. The wide-spread use of electric vehicles powered by electricity from renewable sources promises a substantial reduction of local emissions in urban areas as well as greenhouse gas emissions. To be a successful mobility alternative several obstacles and challenges have to be overcome first. Especially the customers' purchase decision determines finally whether an innovation like electromobility will be successful. Therefore, this paper concentrates on demand-related obstacles and barriers for a broad market deployment of electric cars. Within the Electromobility+ project eMAP these issues are investigated via a consumer survey. It was designed to identify the awareness of potential consumers of electric cars as well as give an estimate of the attitude towards this new technology. In addition to the picture of potential demand-related obstacles the consumers were asked to evaluate the suitability of various promotion measures.
Immediate user self-evacuation is crucial in case of fire in road tunnels. This study investigated the effects of information with or without additional virtual reality (VR) behavioural training on self-evacuation during a simulated emergency situation in a road tunnel. Forty-three participants were randomly assigned to three groups with accumulating preventive training: The control group only filled in questionnaires, the informed group additionally read an information brochure on tunnel safety, and the VR training group received an additional behavioural training in a VR tunnel scenario. One week later, during the test session, all participants conducted a drive through a real road tunnel in which they were confronted with a collision of two vehicles and intense smoke. The informed and the behaviourally trained participants evacuated themselves more reliably from the tunnel than participants of the control group. Trained participants showed better and faster behavioural responses than informed only participants. Interestingly, the few participants in the control group who reacted adequately to the scenario were all female. A 1 year follow-up online questionnaire showed a decrease of safety knowledge, but still the trained group had somewhat more safety relevant knowledge than the two other groups. Information and especially VR behavioural training both seem promising to foster adequate self-evacuation during crisis situations in tunnels, although long term beneficial behavioural effects have to be demonstrated. Measures aiming to improve users/ behaviour should take individual difference such as gender into account.
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.
India is one of the leading countries reporting highest road accidents & related injuries. TMARG (Tata Motors Accident Research Group) has been recording crashes in association with M/s. Lokamanya Medical Foundation since 2011 with M/s, Amandeep Hospitals since Aug 2013. This study has highlighted some accident types not discussed extensively in literature. Trucks to Truck impacts " Cabin interaction with overhanging loadbody structures and Offset underside impacts for passenger vehicles are seen in significant numbers. The paper discusses these in more detail including severity.
Das Merkblatt zur Verbesserung der Verkehrssicherheit auf Motorradstrecken (MVMot (FGSV, 2007)) gibt bereits Hilfen zur Bestimmung unfallauffälliger Bereiche von Motorrädern auf Landstraßen. Bisher existiert aber kein Merkblatt, welches den Bereich innerhalb geschlossener Ortschaften abdeckt, obwohl etwa 2/3 der Unfälle mit Personenschaden unter Beteiligung motorisierter Zweiradfahrer (MZR) innerorts stattfinden (DESTATIS, 2012). Mit Hilfe theoretischer Ansätze werden geeignete Grenzwerte entwickelt, um unfallauffällige Bereiche (UAB) für MZR im Innerortsbereich zu erkennen und abzugrenzen. Neben der Bestimmung eines Grenzwerts über die absolute Anzahl an Unfällen unter Beteiligung von MZR wurde auch der Ansatz über den Relativanteil an allen Unfällen in einer Unfallhäufungsstelle (UHS nach M-Uko (FGSV, 2012)) gewählt. Die theoretischen Überlegungen zur Grenzwertbestimmung basieren auf einer Optimierung des Aufwands an zu bearbeitenden UAB im Hinblick auf den Nutzen, bestehend aus den dadurch bearbeiteten Unfällen. Aus typischen Unfallkonstellationen und Defiziten in den bestimmten UAB wurden typische Unfallkonstellationen abgeleitet. Spezielle Defizite einer Unfallkonstellation ergaben sich aus der Analyse der Örtlichkeit und der Unfalltexte. Den Defiziten wurden geeignete Maßnahmen gegenübergestellt und in einem Maßnahmenkatalog zusammengefasst. Unter Verwendung multivariater statistischer Modelle wurden Wirkungen einzelner (stetiger) Größen auf das Unfallgeschehen bzw. die Beschreibung systematischer Auswirkungen einzelner (kategorialer) Merkmale beschrieben. Es wurden sowohl Streckenmodelle als auch Knotenpunktmodelle des Hauptstraßennetzes entwickelt, die sich in ihrer Vorfahrtsregelung und Knotenpunktform unterscheiden. Es wurden nur für Elemente des Straßennetzes mit Angaben zur Verkehrsstärke Modelle erstellt. Modelle für Knotenpunkte, an denen die Verkehrsbelastung bekannt ist, konnten zwischen 60-70 % der systematischen Varianz der Unfallhäufigkeit beschreiben. Der Großteil ist allein auf den Einfluss der Verkehrsstärken auf der Haupt- und Nebenrichtung zurückzuführen. Weitere Unterschiede in der Unfallhäufigkeit werden durch den systematischen Einfluss von Straßenbahngleisen, Fußgängerquerungen in Geschäftsstraßen (Anzahl der Dienstleistungen im Umfeld als Stellvertretervariable) und den Zustand der Fahrbahnoberfläche (Anzahl der Haltstellen im Umfeld als Stellvertretergröße für Spurrillen) erklärt. An Strecken ergeben sich die Unterschiede der Unfallhäufigkeit zusätzlich aus variierenden Streckenlängen. Die Untersuchung der UAB zeigte folgende häufig auftretende Defizite: - spitzwinklige Befahrung von Straßenbahngleisen, - fehlende Sicht, - Spurrillen, - Kreisverkehrszufahrten am Ortseingang nach Innerortsstandard, - schlecht erkennbare Wartepflicht, - Griffigkeitswechsel (Bitumenvergüsse, großflächige Bodenmarkierungen), - hohe Geschwindigkeiten, - plötzliche Bremsvorgänge. Für häufig vorkommende Unfallkonstellationen können anhand des Maßnahmenkatalogs auf typische sicherheitsrelevante Defizite innerorts hingewiesen werden und situationsgerechte und wirkungsvolle Maßnahmen vorgeschlagen werden.
Cyclists are more likely to be injured in fatal crashes than motorised vehicles. To gain detailed and precise behavioural data of road users, i.e. trajectories, a measuring campaign was conducted. Therefore, a black-spot for accidents with cyclists in Berlin, Germany was selected. The traffic has been detected by a fully automated traffic video analysis system continuously for twelve hours. The video surveillance system is capable of automatically extracting trajectories, classifying road user types and precise determining and positioning of conflicts and accidents. Additionally, pre-conflict and pre-accident situations could be analysed to provide further in-depth understanding of accident causation. The evaluation of the measuring campaign comprised the investigation of traffic parameters, e.g. traffic flow, as well as traffic-safety related parameters based on Surrogate Safety Measures (SSM). Furthermore, the spatial and temporal distributions of conflicts involving cyclists were determined. As a result, three possible conflict clusters could be identified, of which one cluster could be confirmed by detailed video analysis, showing conflicts caused by right turning vehicles.