Generating test scenarios for assessing automated driving systems using scenario fusion
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Beschreibung
This thesis presents the find-unify-synthesize-evaluate for representativity (FUSE4Rep) process model, a novel approach to the safety evaluation of automated driving systems (ADS). Designed to make road traffic safer by preventing accidents, ADS must demonstrate a higher level of safety than human drivers. FUSE4Rep addresses the challenge of unifying divergent information from sources such as police accident data and video-based traffic observations to ensure a comprehensive scenario representation. Through scenario fusion, the process synthesises diverse traffic data into a representative scenario catalogue, enabling a thorough assessment of ADS over a wide scenario space. Using statistical matching, it derives and varies logical scenarios to cover potential real-world conditions in stochastic simulations. A case study shows how German police accident data and video-based observations are used to create a fused scenario catalogue, demonstrating the practical application of FUSE4Rep. As part of the comprehensive “Dresden Method” for ADS evaluation, this approach provides a reliable framework for the development of safer ADS and contributes to improved road safety.
Autor*in
Maximilan Bäumler
Themen in »Generating test scenarios for assessing automated driving systems using scenario fusion«
Statistisches Matching, Stochastische Verkehrssimulation Stochastic driving simulation, Szenarien-basiertes Testen Scenario-based testing, Drone, Maschinelles lernen Machine learning, automatisierte Fahrsysteme automated driving systems (ADSs) Verkehrssimulationen, traffic simulations, Verkehrsunfälle traffic accidents, Straßenverkehrsdatenquellen, Autonomes Notbremssystem autonomous emergency braking system, Dresdner Methode Dresden method, Ereignisdatenrekorder, event data recorder Naturalistische Fahrstudie, naturalistic driving study Surrogat-Sicherheitsmaßnahme, surrogate safety measure driving, Videobasierte Verkehrsbeobachtung, video-based traffic observation Stau-Pilot, Verkehrsdatenquelle, traffic data, Straßennetz road network, traffic guidance, Verkehrsführung menschliches Fahrerverhalten, human driver behavior, accident