Scenarios for Safety Validation and Development of Automated Driving Systems

Our datasets contain not just naturalistic trajectory data in high accuracy, but are also from challenging places, where relevant scenarios happen. Our team has experience from the German PEGASUS project for safety validation.

What we offer:

Scenario Datasets

  • “Replay 2 Sim” Scenario Datasets
  • Logical and Concrete Scenario Datasets
  • Scenario Datasets according to UNECE R157 ALKS
  • We support the ASAM OpenX-Standards OpenSCENARIO® and OpenDRIVE®
  • Scenarios covering the Layers 1-5 of the 6-Layer Model

Scenario-based safety validation on system level

  • Processes, Methods, Tools and Consulting along the scenario-based validation chain from the creators of the PEGASUS database
  • Scenario extraction algorithms according to internationally accepted concepts
  • Generation of scenario concepts, e.g. specific to your ODD
  • Identification, extraction and parametrization of relevant scenarios
  • From cloned real-world scenarios to logical/concrete scenarios in standard formats such as OpenScenario

Our Research Activities:

We are working in several public funded projects within the field of system-level safety validation. Here are some examples of projects for the Federal Highway Research Institute:

Evidence-Based Derivation of Basic Scenarios for Controlled-Access Highways

The efficient and effective safety assessment of automated driving functions is currently one of the most relevant research topics. For this purpose, widespread concepts from industry and research use scenario-based approaches in which the relevant characteristics of a traffic situation are filtered in order to store them in databases using a suitable scheme. The data can come from different sources (field tests, accident databases, simulation, UAV, etc.). A uniform database and standardized system are currently being researched and developed. Based on fka’s work in the PEGASUS project, fka and its scientific cooperation partner, the Institute for Automotive Engineering (ika) at RWTH Aachen University, have been working for a year now on a research project (82.0729/219) commissioned by the Federal Highway Research Institute.

The goal of the two-year project is to derive a universally applicable concept for describing traffic situations within the driving domain controlled-access highway. Thereby, driving on the highway is to be divided into disjunctive elements, so-called basic scenarios, which depend on various factors that describe the situational traffic context. In this way, it should be possible to construct both simple and complex scenarios. The central result of the project is a codebook that describes the generally applicable scheme for the classification of scenarios in analogy to the accident type classification.

Concept for Statistically Representative Traffic Observations

Data sources employed by fka include accident data, field operational tests, driving simulator studies and in particular traffic observation using infrastructure sensors and drones. As all sources constitute just singular measurements, a complete acquisition of all traffic is not viable, though.

The goal of the BASt project (FE 82.0735/2019) “Concept for Statistically Representative Traffic Observations” is to develop a concept for the representative gathering of scenarios in traffic areas like federal highways as well as urban and rural areas. A sample taken on the basis of this concept can be used to derive statistically valid conclusions about the frequency of individual scenarios in the context of the traffic in total or a specific traffic domain. The result of this project constitutes a first approach for a proof of completeness and therefore is a central building block for an efficient and effective approval process of automated driving systems.

Contact Our Experts:

The data sent will be processed only for the purpose of processing your request. Further information can be found in our privacy policy.

Thank you for your message. It has been sent.
There was an error trying to send your message. Please try again later.