Area-wide real-world test scenarios of poor visibility for safe development of automated vehicles

Introduction Automated vehicles in everyday real-world traffic are predicted to be developed soon (Gasser et al., Rechtsfolgen zunehmender Fahrzeugautomatisierung, Wirtschaftsverlag NW, Berichte der Bundesanstalt für Straßenwesen F83, 2012). New technologies such as advanced object detection and artificial intelligence (AI) that use machine or deep-learning algorithms will support meeting all the maneuvering challenges involved in different degrees of automation (Society of Automotive Engineers - SAE international, Levels of driving automation for on road vehicles, Warrendale, PA., 2014; National Highway Traffic Safety Administration – NHTSA, Preliminary statement of policy concerning automated vehicles, Washington, DC, 2018). For automated series production, these vehicles of course must be safe in real-world traffic under all weather conditions. Therefore, system validation, ethical aspects and testing of automated vehicle functions are fundamental basics for successfully developing, market launching, ethical and social acceptance. Method In order to test and validate critical poor visibility detection challenges of automated vehicles with reasonable expenditure, a first area-wide analysis has been conducted. Because poor visibility restricts human perception similar corresponding to machine perception it was based on a text analysis of 1.28 million area-wide police accident reports – followed by an in-depth case-by-case analysis of 374 identified cases concerning bad weather conditions (see chap. 1.3). For this purpose the first time ever a nationwide analysis included all police reports in the whole area within the state of Saxony from the year 2004 until 2014. Results Within this large database, 374 accidents were found due to perception limitations – caused by “rain”, “fog”, “snow”, “glare”/“blinding” and “visual obstruction” – for the detailed case-by-case investigation. All those challenging traffic scenarios are relevant for automated driving. They will form a key aspect for safe development, validation and testing of machine perception within automated driving functions. Conclusions This first area-wide analysis does not only rely on samples as in previous in-depth analyses. It provides relevant real-world traffic scenarios for testing of automated vehicles. For the first time this analysis is carried out knowing the place, time and context of each accident over the total investigated area of an entire federal state. Thus, the accidents that have been analyzed include all kinds of representative situations that can occur on motorways, highways, main roads, side streets or urban traffic. The scenarios can be extrapolated to include similar road networks worldwide. These results additionally will be taken into account for developing standards regarding early simulations as well as for the subsequent real-life testing. In the future, vehicle operation data and traffic simulations could be included as well. Based on these relevant real-world accidents culled from the federal accident database for Saxony, the authors recommend further development of internationally valid guidelines based on ethical, legal requirements and social acceptance. Document type: Articl

Statistical driver model for accident simulation - Using a statistical driver model for benefit estimation of advanced safety systems with warning interfaces

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

Reconstruction of accidents based on 3D-geodata

Beside numerous information about vehicles injuries and environmental data the GIDAS database contains detailed reconstruction data. This data is calculated by a reconstruction engineer who handles about 1000 accidents per year. The spectrum of one reconstruction ranges from simple crossing accidents to complex run-off accidents with rollover events. Especially for complex accident scenarios there is a large effort to design the environment of the accident scene within PC-Crash ®. To reduce the reconstruction time by maintaining the high quality of reconstruction 3D-geodata can be useful. Geodata is available for nearly every area in Germany and can be used for a fast and detailed creation of complex accident environments. In combination with the accident sketch areal images of the accident scene can be created and the participants are implemented in the new-built 3D-reconstruction environment. As a consequence, the characteristics of the terrain can be considered within the reconstruction which is especially important for run-off accidents

Simulation of Test Drives by Using Police-recorded Accident Data and Combining Macroscopic and Microscopic Elements

With the development of autonomous driving functions, the evaluation of their functional safety is becoming increasingly important. Current vehicles are tested with separate simulations or test drives. In order to validate future autonomous vehicles by means of test drives, a substantial number of test kilometers are necessary. In addition, these test drives must be repeated for every new release of the system, which increases the expenses for validation. For this reason, programs that can simulate test drives have a high significance. Previous programs do not include the indispensable combination of routing simulation and accident simulation needed to represent a simulated test drive. Therefore, an approach to combining a macroscopic simulation (routing simulation) with a microscopic simulation (accident simulation) is used in this paper. When the start location and the destination are given, the macroscopic simulation can compute the test route by means of the OSRM (Open Source Routing Machine) routing application. While driving along the test route, the simulated vehicles pass various locations of real accidents. The relevant data is taken from the accident database compiled by the police of Saxony, Germany. A selection procedure ensures that only relevant accident situations along the test route are later simulated microscopically. Only if the accident situation is similar to the current situation of the simulated vehicle can the accident situation be simulated microscopically. Therefore, various boundary conditions are used to determine whether there are similarities regarding weather, traffic, light conditions and trajectories of the accident vehicles. To study different variations of the selection procedure, three different concepts are developed and evaluated. The first concept is based on a given test route between start location and destination and a realistic calculation of the travel time. The second concept is also based on a given test route but combines this with a time window for the entire route. The third concept combines an unknown test route, which is calculated between relevant accident locations during the simulation, with a realistic calculation of the travel time. After the evaluation of all three concepts, only the third concept is implemented in the simulation. Within the microscopic simulation by means of PC-Crash, a relevant accident situation is simulated twice, once without and once with the tested driver assistance system in action. With the help of a collision detection system, a conclusion about the efficiency of the driver assistance system is made. The result is a program that combines completed test kilometers with avoided accident situations to simulate a test drive. The current program can only be used in Saxony, Germany. For an expansion to all of Europe, comprehensive accident data is necessary. In addition, the selection procedure could be improved by means of georeferenced weather and traffic data. Because of the basic simulation tools, the actual simulation is not designed for quality but rather for quantity. However, high-quality simulation tools can be implemented with little effort. The simulation of test drives is an important challenge, and with the program developed here, an opportunity to solve it is introduced

Area-wide real-world test scenarios of poor visibility for safe development of automated vehicles

Introduction: Automated vehicles in everyday real-world traffic are predicted to be developed soon (Gasser et al., Rechtsfolgen zunehmender Fahrzeugautomatisierung, Wirtschaftsverlag NW, Berichte der Bundesanstalt für Straßenwesen F83, 2012). New technologies such as advanced object detection and artificial intelligence (AI) that use machine or deep-learning algorithms will support meeting all the maneuvering challenges involved in different degrees of automation (Society of Automotive Engineers - SAE international, Levels of driving automation for on road vehicles, Warrendale, PA., 2014; National Highway Traffic Safety Administration – NHTSA, Preliminary statement of policy concerning automated vehicles, Washington, DC, 2018). For automated series production, these vehicles of course must be safe in real-world traffic under all weather conditions. Therefore, system validation, ethical aspects and testing of automated vehicle functions are fundamental basics for successfully developing, market launching, ethical and social acceptance. Method: In order to test and validate critical poor visibility detection challenges of automated vehicles with reasonable expenditure, a first area-wide analysis has been conducted. Because poor visibility restricts human perception similar corresponding to machine perception it was based on a text analysis of 1.28 million area-wide police accident reports – followed by an in-depth case-by-case analysis of 374 identified cases concerning bad weather conditions (see chap. 1.3). For this purpose the first time ever a nationwide analysis included all police reports in the whole area within the state of Saxony from the year 2004 until 2014. Results: Within this large database, 374 accidents were found due to perception limitations – caused by “rain”, “fog”, “snow”, “glare”/“blinding” and “visual obstruction” – for the detailed case-by-case investigation. All those challenging traffic scenarios are relevant for automated driving. They will form a key aspect for safe development, validation and testing of machine perception within automated driving functions. Conclusions: This first area-wide analysis does not only rely on samples as in previous in-depth analyses. It provides relevant real-world traffic scenarios for testing of automated vehicles. For the first time this analysis is carried out knowing the place, time and context of each accident over the total investigated area of an entire federal state. Thus, the accidents that have been analyzed include all kinds of representative situations that can occur on motorways, highways, main roads, side streets or urban traffic. The scenarios can be extrapolated to include similar road networks worldwide. These results additionally will be taken into account for developing standards regarding early simulations as well as for the subsequent real-life testing. In the future, vehicle operation data and traffic simulations could be included as well. Based on these relevant real-world accidents culled from the federal accident database for Saxony, the authors recommend further development of internationally valid guidelines based on ethical, legal requirements and social acceptance

How to Link Accident Data and Road Traffic Measurements to Enable ADAS/AS Simulation?

The progress of safety technologies, based on the continuous advances in vehicle crash worthiness, restraint systems and active safety functions made traffic safer than ever before. Latest developments heading from assisted Advanced Driver Assistance System (ADAS) to Automated Driving (AD), lead to more and more complex real-world situations to be handled, going from standard driving tasks up to critical situations, which may cause a collision. Therefore, throughout the development process of such systems, it becomes common to use simulation technologies in order to assess these systems in advance. To gain results out of the simulation, input data are required; typically, from various sources, so the requirements can be covered. Thus, the challenge of scoping with different input sources arises. To come along with that problem, two main kinds of input data will be needed in general: (1) the descriptive parameter covering all border conditions, so called parameter room; (2) the system specifications for estimation. The quality of the results correlates strongly with the quality of inputs given. In case of ADAS systems and AD functions, the second kind of input data is very well known. Major challenges relate to the first kind of input data. Thus, the paper will describe a way to create input data that cover all descriptive parameters needed from normal driving up to the collision by the combination of accident analysis and real-world road traffic observations. The method aims at being applicable to different data sources and to different countries

Animal street crossing behavior: An in-depth field study for the identification of animal street crossing behaviour using the AIMATS-methodology

Based on the police recorded accident data in the German federal state of Saxony (2007-2014), 9.3 % (approx. 85,000) of all accidents involve animals. In 2015, 2,580 accidents involving animals caused injuries in Germany. In order to design ADAS (Advanced Driver Assistance System) in a way that helps to avoid such accidents, it is necessary to understand the animals’ behavior. Current methods to observe animal behavior are using vehicle mounted NDS (Naturalistic Driving Study) data. This kind of NDS is expensive considering the number of relevant data sets recorded. This paper delivers the results of a one-year field study that used a new methodology based on in-situ recording units integrated in the infrastructure at critical sites. This way, vast data sets of animal street crossing scenarios can be generated in a quality similar to the one of NDS methods - yet at a relatively low cost. The definition of the scenarios is based on an in-depth investigation method which was presented at the ESAR conference (Hannover, Germany) in 2016 and is called “AIMATS”. An accident data analysis of approx. 85,000 police recorded accidents with wild animal involvement in Germany made it possible to identify locations with a high possibility of accidents involving animals. These locations were observed by means of an infrared camera with a 50Hz frame rate. The recorded camera data allowed a detailed analysis of the movement of all road users. An automated analysis of the recorded results delivers typical and realistic models of the behavior of animals that have encounters with other road users. For this study, we assumed that the animal behavior at near miss scenarios is the same as their behavior in accident scenarios. This has been confirmed. This paper describes the results of a large-scale infrastructure-based traffic observation using the AIMATS methods. This method can be used for all traffic scenarios at a relatively low cost rate per scenario

FAPS - Fraunhofer-IVI-Accident Prevention-School: A new method to increase the overall traffic safety by using real accident data and expert evidence

Menschen beschäftigen sich im alltäglichen Leben relativ selten mit den Gefahren im Straßenverkehr. Dies führt zu fehlenden Grundverständnis zum frühen Erkennen kritischer Situationen. Die Unwissenheit über unfallspezifische Zusammenhänge kann zu falsch eingeschätzten Situationen führen, welche das Risiko an einem Verkehrsunfall beteiligt zu sein stark erhöhen. Die Fraunhofer IVI Accident Prevention School (FAPS) bietet Schülern die Möglichkeit sich direkt mit realen Unfällen zu identifizieren, indem Unfälle im direkten Umfeld der Schule Basis einer eigenen Projektarbeit werden. Unter Nutzung der flächendeckenden polizeilichen Unfalldaten erreicht FAPS eine große Bevölkerungsschicht und soll somit die gesamte Verkehrssicherheit maßgeblich erhöhen. Hierbei können Gutachten realer Verkehrsunfälle die Anschaulichkeit stark erhöhen

Technological optimization of left turns from the subsidiary road network on highways to avoid wrong-way driving

Unfälle aufgrund von Falschfahrten sind sehr seltene Ereignisse, welche aber in der Regel eine besonders hohe Unfallschwere aufweisen. Aktuelle Studien aus Deutschland zeigen, dass häufig falsches Linksabbiegen an Anschlussstellen den Ausgangspunkt von Falschfahrten bildet. Im vorliegenden Forschungsvorhaben wurde durch empirische Erhebungen und Fahrten im Fahrsimulator an umgestalteten und nicht umgestalteten Anschlussstellen die Maßnahmenwirkung unterschiedlicher Markierungsvarianten evaluiert. Aus den Ergebnissen wurden Empfehlungen für die optimierte Knotenpunktmarkierung abgeleitet. Im Untersuchungskollektiv waren sowohl signalisierte als auch nicht-signalisierte Anschlussstellen vertreten. An allen empirisch untersuchten Anschlussstellen wurden videogestützte Verkehrserhebungen durchgeführt. Aus den erhobenen fahrer- und umfeldspezifischen Merkmalen konnte das Orientierungs- und Abbiegeverhalten der Linksabbieger analysiert werden. Im Fahrsimulator wurde zusätzlich überprüft, inwieweit gruppenbezogene Ausprägungen bzw. Unterschiede bzgl. des objektiven Fahrverhaltens und der subjektiven Fahrempfindungen auftreten. Im Ergebnis der empirischen Untersuchungen und der Probandenversuche im Fahrsimulator wurde für nicht signalisierte Anschlussstellen eine Markierungsvariante favorisiert, bei der die Wartelinie weiter innen im Knotenpunkt liegt als bisher. Zusätzliche Richtungspfeile und eine innere Abbiegeleitlinie in Verbindung mit weiteren Anpassungen (Sonderform des Zeichens 296 StVO mit Breitstrich, Zeichen 222 StVO eingedreht und durch Leitplatte Zeichen 626 StVO ergänzt) unterstützen den Verkehrsteilnehmer, sich beim Abbiegeprozess vom nachgeordneten Straßennetz auf die Autobahn intuitiv richtig zu verhalten. An signalisierten Anschlussstellen wird ebenfalls der Versatz der Haltlinie in Richtung Knotenpunktmitte, in Verbindung mit den bereits für nicht-signalisierte Anschlussstellen genannten Anpassungen favorisiert. Die Standorte der Signalgeber müssen aber in jedem Fall im Hinblick auf die Bestimmungen der RiLSA (2010) mit der zuständigen Straßenverkehrsbehörde abgestimmt werden. Das Fahrverhalten an den untersuchten Anschlussstellen hat gezeigt, dass die empfohlenen Markierungsvarianten ein intuitiv richtiges Verhalten beim Abbiegen unterstützen und dadurch Falschfahrten vermieden werden.Accidents caused by wrong-way driving are very rare events, but which generally have a particularly high accident severity. Recent studies from Germany show that often wrong way driving begins by the false left turn from the subsidiary road onto the motorway. In this research project the driving behaviour at junctions with common and various redesigned pavement markings were evaluated at on-site junctions as well as within a driving simulator. As a result, optimized designs of pavement markings could be recommended. The research project focussed on signalized and on unsignalized junctions as well. The traffic surveys of the on-site junctions were video-based. The raised driver and environment-specific data is used to describe and analyse the behaviour during the phase of orientation and the turning manoeuvre. The results of the driving behaviour within the driving simulator were tested on systematic effects within particular groups. As a result of the behaviour studies on-site and within the driving-simulator a pavement marking for unsignalized junctions could be recommended which contains a shifted holding line of the left turn lane further into the section area. An additional left-turn arrow beyond the holding line and an inner turn line in combination with further adjustments (holding line of the intersecting motorway exit with an additional barrier line, shifted "pass-by-on-right" sign with added obstruction marker) support the road users to ensure a correct left-turn-manoeuver. At signalized junctions a shifted stop line further into the section is recommended as well and added by the mentioned adjusted features at unsignalized junctions. With regard to the recommendations within the guidelines for traffic signals (RiLSA, 2010) the location of the traffic signals have to be coordinated with the road traffic authority in any case. The driving behaviour of the investigated junctions has shown that the recommended pavement markings support an intuitively correct left-turn-manoeuver and therefore avoid wrong-way driving