Lehrstuhl Software Engineering: Dependability

M.Sc. Anil Ranjitbhai Patel


Wissenschaftliche Mitarbeiter der Arbeitsgruppe Software Engineering: Dependability



M.Sc. Anil Ranjitbhai Patel
Technische Universität Kaiserslautern
Gebäude 32, Raum 435
Postfach 3049
67653 Kaiserslautern

Tel: +49 (631) 205-3334

Fax: +49 (631) 205-3331

E-mail: patel@informatik.uni-kl.de

Research Focus

Dynamic Risk/Safety Management for Autonomous Vehicles

(Safety is defined as freedom from risk and risk is the possibility of suffering harm or loss.)

Autonomous vehicles (AVs) are complex safety-critical systems that operate in an uncertain and dynamic environment. During runtime, the environmental uncertainties and random component failures might result in hazardous events, sometimes even to an accident, if left undetected. Moreover, in the event of random errors, highly integrated automotive systems might suffer from the butterfly effect, which could produce an unannounced unsafe behavior of the system. While AV operates in a dynamic environment, traditional safety assurance mechanisms like Fault Tree Analysis (FTA), Failure Mode Effect and Criticality Analysis (FMECA), etc. are primarily beneficial, but not sufficient to ensure safety as they are based on static worst-case assumptions. It is, therefore, necessary to move from static safety management methods to dynamic safety management approaches. Hazard Analysis and Risk Assessment (HARA) of ISO 26262 assure functional safety of systems by considering: Hazardous events, their associated severity, exposure, and controllability ratings to evaluate an Automotive Safety Integrity Level (ASIL). The entire process, however, is based on the very premise that a human driver is forever available to take control and is thus responsible for the safety of the vehicle. On the contrary, autonomous vehicles (AVs) function without any human intervention. Therefore, Dynamic Risk Assessment (DRA) at runtime is the need of the hour to be a step forward to analyze the current risk of the actual situation at runtime, instead of relying on static worst-case assumptions.



Master Arbeit

  • Kaivlya Patel - Parameterized Tool for Tradespace Analysis for Fact-based Concept Decisions in Systems Engineering
  • Clement John Shaji - Diversity for Safety and Security of Autonomous Vehicles against Accidental and Deliberate Faults
  • Ali Faraz - Dynamic Risk Assessment for Adaptable Autonomous Systems using Machine-Learning technique
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