Vision Zero postulates that no one should be killed or seriously injured in road traffic; therefore, it is necessary to define evidence-based speed limits to mitigate impact severity. The overall aims to guide the definition of safe speeds limits by establishing relations between impact speed and the risk of at-least-moderate (MAIS2+) and at-least-severe (MAIS3+) injuries for car occupants in frontal and side crashes in Sweden. As Swedish in-depth data are unavailable, the first objective was to assess the applicability of German In-depth Accident Study (GIDAS) data to Sweden. The second was to create unconditional injury risk curves (risk of injury given involvement in any crash), rather than risk curves conditional on the GIDAS sampling criterion of suspected-injury crashes. Thirdly, we compared the unconditional and conditional risk curves to quantify the practical implications of this methodological choice. Finally, we provide an example to demonstrate how injury risk curves facilitate the definition of safe, evidence-based speed limits in Sweden. Characteristics important for the injury outcome were similar between GIDAS and Swedish data; therefore, the injury risk curves using German GIDAS data are applicable to Sweden. The regression models yielded the following results for unconditional injury risk curves: 10 % MAIS2+ at 25 km/h impact speed for frontal head-on crashes, 20 km/h for frontal car-to-object crashes, 55 km/h in far-side crashes, and 45 km/h in near-side crashes. A 10 % MAIS3+ risk was reached between 70 and 75 km/h for all crash types. Conditional injury risk curves gave substantially different results; the 10 % MAIS3+ risk in near-side crashes was 140 km/h, twice the unconditional value. For example, if a 10 % MAIS3+ risk was acceptable, treating remaining uncertainty conservatively, assuming compliance with speed limits and that Automated Emergency Braking takes 20 km/h of the travel speed before impact in longitudinal traffic, the safe speed limit for car occupants on most Swedish roads would be 80 km/h and 60 km/h in intersections.

OBJECTIVE: Automated Driving System (ADS) fleets are currently being deployed in several dense-urban operational design domains within the United States. In these dense-urban areas, pedestrians have historically comprised a significant portion, and sometimes the majority, of injury and fatal collisions. An expanded understanding of the injury risk in collision events involving pedestrians and human-driven vehicles can inform continued ADS development and safety benefits evaluation. There is no current systematic investigation of United States pedestrian collisions, so this study used reconstruction data from the German In-Depth Accident Study (GIDAS) to develop mechanistic injury risk models for pedestrians involved in collisions with vehicles.
METHODS: The study queried the GIDAS database for cases from 1999 to 2021 involving passenger vehicle or heavy vehicle collisions with pedestrians.
METHODS: We describe the injury patterns and frequencies for passenger vehicle-to-pedestrian and heavy vehicle-to-pedestrian collisions, where heavy vehicles included heavy trucks and buses. Injury risk functions were developed at the AIS2+, 3+, 4+ and 5+ levels for pedestrians involved in frontal collisions with passenger vehicles and separately for frontal collisions with heavy vehicles. Model predictors included mechanistic factors of collision speed, pedestrian age, sex, pedestrian height relative to vehicle bumper height, and vehicle acceleration before impact. Children (≤17 y.o.) and elderly (≥65 y.o.) pedestrians were included. We further conducted weighted and imputed analyses to understand the effects of missing data elements and of weighting towards the overall population of German pedestrian crashes.
RESULTS: We identified 3,112 pedestrians involved in collisions with passenger vehicles, where 2,524 of those collisions were frontal vehicle strikes. Furthermore, we determined 154 pedestrians involved in collisions with heavy vehicles, where 87 of those identified collisions were frontal vehicle strikes. Children were found to be at higher risk of injury compared to young adults, and the highest risk of serious injuries (AIS 3+) existed for the oldest pedestrians in the dataset. Collisions with heavy vehicles were more likely to produce serious (AIS 3+) injuries at low speeds than collisions with passenger vehicles. Injury mechanisms differed between collisions with passenger vehicles and with heavy vehicles. The initial engagement caused 36% of pedestrians\' most-severe injuries in passenger vehicle collisions, compared with 23% in heavy vehicles collisions. Conversely, the vehicle underside caused 6% of the most-severe injuries in passenger vehicle collisions and 20% in heavy vehicles collisions.
CONCLUSIONS: U.S. pedestrian fatalities have risen 59% since their recent recorded low in 2009. It is imperative that we understand and describe injury risk so that we can target effective strategies for injury and fatality reduction. This study builds on previous analyses by including the most modern vehicles, including children and elderly pedestrians, incorporating additional mechanistic predictors, broadening the scope of included crashes, and using multiple imputation and weighting to better estimate these effects relative to the entire population of German pedestrian collisions. This study is the first to investigate the risk of injury to pedestrians in collisions with heavy vehicles based on field data.

Older people are or remain increasingly mobile for longer and participate in road traffic as car drivers or passengers, cyclists, and pedestrians. Regardless of their role in causing accidents, they are more likely to be seriously injured due to their higher vulnerability. If they are involved in an accident they suffer increasingly more from severe injuries, which consequently leads to longer hospitalization times. These aspects are even more applicable for persons aged 75 years or more than for persons aged 65-74 years. From a German in-depth accident study (GIDAS) analysis of the individual injuries of different types of road users, the most frequently severely injured body regions as well as the leading injuries can be derived. Primarily head and thorax injuries are of importance and secondarily also injuries to the lower extremities (especially for cyclists and pedestrians). The majority of the presented results confirm findings from comparable studies; however, this study was conducted for the first time on the basis of the abbreviated injury scale (AIS) 2015 and some individual injuries (especially commotio cerebri, which dominates in almost all age and road user groups) were upgraded from AIS1 to AIS2 in the latest AIS revision. As a result, the current results partly show significant increases in injury severity, especially for the head, compared to earlier studies based on the AIS 2008.
UNASSIGNED: Ältere Menschen sind oder bleiben zunehmend länger mobil und nehmen als PKW-Fahrer/Insasse, Radfahrer und Fußgänger am Straßenverkehr teil. Unabhängig davon, welche Rolle sie bei der Verursachung von Unfällen spielen, besteht aufgrund ihrer höheren Vulnerabilität eine höhere Wahrscheinlichkeit für schwere und schwerste Verletzungen. Im Fall einer Unfallbeteiligung erleiden ältere Menschen mehr und schwerere Verletzungen, was in der Folge zu längeren Krankenhausaufenthalten führt. Diese Aspekte gelten insbesondere für die Altersgruppe der über 75-Jährigen, mehr noch als für die 65- bis 74-Jährigen. Aus einer GIDAS-Analyse der individuellen Verletzungen verschiedener Verkehrsteilnehmerarten lassen sich die am häufigsten schwer und schwerstverletzten Körperregionen sowie die führenden Verletzungen ableiten. In erster Linie sind Kopf- und Thoraxverletzungen von Bedeutung, in zweiter Linie auch Verletzungen an den unteren Extremitäten (insbesondere bei Radfahrern und Fußgängern). Die vorgestellten Ergebnisse bestätigen größtenteils Erkenntnisse aus vergleichbaren Studien. Allerdings wurde diese Studie erstmals auf der Basis des AIS 2015 durchgeführt, und einige Einzelverletzungen (insbesondere die Commotio cerebri, die in fast allen Alters- und Verkehrsteilnehmergruppen dominiert) wurden in der letzten AIS-Revision von AIS1 auf AIS2 hochgestuft. Infolgedessen zeigen die aktuellen Ergebnisse im Vergleich zu früheren Studien auf Basis des AIS 2008 z. T. deutliche Steigerungen der Verletzungsschwere, insbesondere im Kopfbereich.

Developers of in-vehicle safety systems need to have data allowing them to identify traffic safety issues and to estimate the benefit of the systems in the region where it is to be used, before they are deployed on-road. Developers typically want in-depth crash data. However, such data are often not available. There is a need to identify and validate complementary data sources that can complement in-depth crash data, such as Naturalistic Driving Data (NDD). However, few crashes are found in such data. This paper investigates how rear-end crashes that are artificially generated from two different sources of non-crash NDD (highD and SHRP2) compare to rear-end in-depth crash data (GIDAS).
Crash characteristics and the performance of two conceptual automated emergency braking (AEB) systems were obtained through virtual simulations - simulating the time-series crash data from each data source.
Results show substantial differences in the estimated impact speeds between the artificially generated crashes based on both sources of NDD, and the in-depth crash data; both with and without AEB systems. Scenario types also differed substantially, where the NDD have many fewer scenarios where the following-vehicle is not following the lead vehicle, but instead catches-up at high speed. However, crashes based on NDD near-crashes show similar pre-crash criticality (time-to-collision) to in-depth crash data.
If crashes based on near-crashes are to be used in the design and assessment of preventive safety systems, it has to be done with great care, and crashes created purely from small amounts of everyday driving NDD are not of much use in such assessment.
Researchers and developers of in-vehicle safety systems can use the results from this study: (a) when deciding which data to use for virtual safety assessment of such systems, and (b) to understand the limitations of NDD.

Heavy goods vehicles (HGVs) are involved in 4.5% of police-reported road crashes in Europe and 14.2% of fatal road crashes. Active and passive safety systems can help to prevent crashes or mitigate the consequences but need detailed scenarios based on analysis of region-specific data to be designed effectively; however, a sufficiently detailed overview focusing on long-haul trucks is not available for Europe. The aim of this paper is to give a comprehensive and up-to-date analysis of crashes in the European Union that involve HGVs weighing 16 tons or more (16 t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis, as follows. Crash statistics based on data from the Community Database on Accidents on the Roads in Europe (CARE) provide a general overview of crashes involving HGVs. These results are complemented by a more detailed characterization of crashes involving 16 t+ trucks based on national road crash data from Italy, Spain, and Sweden. This analysis is further refined by a detailed study of crashes involving 16 t+ trucks in the German In-Depth Accident Study (GIDAS), including a crash causation analysis. The results show that most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on nonhighway roads. Three main scenarios for 16 t+ trucks are characterized in-depth: rear-end crashes in which the truck is the striking partner, conflicts during right turn maneuvers of the truck with a cyclist riding alongside, and pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g., distraction) were the main crash causation factor in 72% of cases in the rear-end striking scenario while information access problems (e.g., blind spots) were present for 72% of cases in the cyclist scenario and 75% of cases in the pedestrian scenario. The three levels of data analysis used in this paper give a deeper understanding of European HGV crashes, in terms of the most common crash characteristics on EU level and very detailed descriptions of both kinematic parameters and crash causation factors for the above scenarios. The results thereby provide both a global overview and sufficient depth of analysis of the most relevant cases and aid safety system development.

It is widely believed that with higher levels of vehicle automation and especially with the advent of fully automatic vehicles, the currently typical forward-facing, upright position will give way to a more relaxed and reclined seating posture. Therefore, the current study investigates the influence of a reclined sitting position on crash injury severity by analyzing real-world crash data from the German in-depth accident study (GIDAS). We compared reclined to upright occupants and focused on effect sizes regarding odds ratios at different injury severity levels. We used the abbreviated injury scale (AIS 2015) for injury scaling and the maximum AIS (MAIS) at the levels 2+, 3+, and 4+ to convert injury severity into a dichotomous metric. Two different analyses were conducted, one looking at the occupant MAIS and one focusing on selected body regions. The body regions investigated are head/face/neck (HFN), thorax, abdomen, pelvis/hip/lower extremities (PHL), and upper extremities. We computed odds ratios greater than one indicating a higher odds of injury at a given injury severity level in the reclined group compared to the upright group. The odds ratios for belted, reclined occupants compared to belted, upright sitting occupants are 2.07, 3.09, and 3.66 for the injury severity levels MAIS2+, MAIS3+, and MAIS4+, respectively. When looking at the body regions, the spread of the odds ratios is wider: At the MAIS2+ level, the odds ratios range between 1.6 and 7.1; at the MAIS3+ level, the odds ratios span from 1.5 to 8.7, with the latter value representing the PHL region. No odds ratio could be computed for the upper extremity injuries at this level. At the MAIS4+ injury severity level, only the HFN odds ratio was statistically significant with a value of 5.6. This study is among the first to show an association between body posture and injury severity at MAIS3+ and MAIS4+ injury level in real-world crashes for reclined seating postures.

Crashworthiness assessments in the United States (U.S.) and the European Union (EU) include a large number of safety regulations and consumer testing programs. However, safety standards and testing procedures differ between the two regions. Not much research has been done in relation to this topic, because it has always been assumed that the accident environments in the U.S. and EU are not comparable. The objective of this study is to compare how vehicle occupants are severely injured in motor vehicle collisions in the U.S. and the EU by applying unsupervised learning to accident data.
A new methodology to identify clusters of seriously injured occupants in NASS-CDS was proposed by the authors in previous research. The current study goes one step further and uses the clusters to compare the injury patterns at the Maximum Abbreviated Injury Scale (MAIS) 3+ level of passenger vehicle occupants in the U.S. and German accident environments. The clustering model developed with NASS-CDS data is applied in this study to German In-Depth Accident Study (GIDAS) data. A machine learning algorithm automatically assigned each GIDAS case to its most similar NASS-CDS cluster controlling for nine different parameters. Those included the injury severity at the body region level, biomechanical characteristics of the occupants, and technical severity of the crash.
Differences and analogies between GIDAS and NASS-CDS data within clusters of seriously injured occupants are highlighted. One of the clusters groups the collisions with the greatest mass incompatibility in NASS-CDS and GIDAS data. The injury patterns in the clusters that include elderly people match significantly between the U.S. and German data sets. The lack of younger population and elevated body mass index (BMI) values in the GIDAS sample make the injury patterns within these population groups less comparable than in the other clusters.
Remarkably similar injury patterns at the MAIS 3+ level have been found in U.S. and German accident data sets after controlling for nine different parameters. This research provides evidence to indicate that how belted vehicle occupants are severely injured in the U.S. and in the EU is not necessarily different.

The Vision Zero approach advocates for a road transport system designed with human injury tolerance and human fallibility as its basis. While biomechanical limits and the relationship between speed and injury outcome has been extensively investigated for car occupants and pedestrians, research analyzing this relationship for motorcyclists remains limited. The aim of this study was to address this issue by developing multivariate injury risk models for motorcyclists that estimate the relationship between speed and injury severity. For that purpose, motorcycle injury crashes from the German In-Depth Accident Study (GIDAS) database for the period 1999-2017 (n = 1037) were extracted. Different models were tested using logistic regression and backwards elimination of non-significant variables. The best fitting model in the current study included relative speed, type of crash opponent, impact location on the motorcycle and impact mechanism of the rider during the crash. A strong and significant relationship between relative speed and injury severity in motorcycle crashes was demonstrated. At 70 km/h, the risk for at least serious injuries in collisions with wide objects, crash barriers and narrow objects was 20%, 51%, and 64%, respectively. Further, it was found that head-on collisions between motorcycles and passenger cars, with both vehicles traveling at 60 km/h (a relative speed at 120 km/h), present 55% risk of at least serious injury to the motorcycle rider. More research is needed to fully understand the boundary conditions needed to design a safe road transport system for motorcyclists. However, this study provides important insights into the relationship between speed and injury severity for riders in various crash situations. The results may be useful in the discussion of appropriate speed limits and in determining the benefits of countermeasures which aim to reduce crash speed.