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

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

Identification of Three Novel Radiotracers for Imaging Aggregated Tau in Alzheimer’s Disease with Positron Emission Tomography

Aggregates of tau and beta amyloid (Aβ) plaques constitute the histopathological hallmarks of Alzheimer’s disease and are prominent targets for novel therapeutics as well as for biomarkers for diagnostic in vivo imaging. In recent years much attention has been devoted to the discovery and development of new PET tracers to image tau aggregates in the living human brain. Access to a selective PET tracer to image and quantify tau aggregates represents a unique tool to support the development of any novel therapeutic agent targeting pathological forms of tau. The objective of the study described herein was to identify such a novel radiotracer. As a result of this work, we discovered three novel PET tracers (2-(4-[¹¹C]­methoxyphenyl)­imidazo­[1,2-<i>a</i>]­pyridin-7-amine <b>7</b> ([¹¹C]­RO6924963), <i>N</i>-[¹¹C]­methyl-2-(3-methylphenyl)­imidazo­[1,2-<i>a</i>]­pyrimidin-7-amine <b>8</b> ([¹¹C]­RO6931643), and [¹⁸F]­2-(6-fluoropyridin-3-yl)­pyrrolo­[2,3-<i>b</i>:4,5-<i>c</i>′]­dipyridine <b>9</b> ([¹⁸F]­RO6958948)) with high affinity for tau neurofibrillary tangles, excellent selectivity against Aβ plaques, and appropriate pharmacokinetic and metabolic properties in mice and non-human primates