BARK-Verify

Behavior Simulation for identifying critical scenarios for autonomous vehicle safety

BARK-Verify

Guaranteeing the safety of autonomous systems is a key step towards certification and market entry of such systems. Analyzing and challenging the popular safety layer approach developed by Mobileye/Intel in the benchmarking and testing framework BARK developed at fortiss aims to identify shortcomings.

Project description

The Responsibility Sensitive Safety model (RSS) is a safety layer concept for autonomous vehicles that aims to ensure safety online by establishing contracts between vehicles. Studies analyzing safety and traffic flow shall be conducted in this project.

Research contribution

Identify critical scenarios for an automated vehicle with the RSS acting as a safety layer in respect to usability (scenario goal not reached in time) or safety (yielding collisions), if possible. We also expect to gather insight on how safety envelope models such as RSS shall be best integrated into behavior planning algorithms.

Project duration

01.10.2020 - 31.12.2020

Contact

Dr. Hao Shen

+49 89 3603522 423 shen@fortiss.org

Name	Purpose	Lifetime	Type	Provider
_pk_id	Used to store a few details about the user such as the unique visitor ID.	13 months	HTML	Matomo
_pk_ref	Used to store the attribution information, the referrer initially used to visit the website.	6 months	HTML	Matomo
_pk_ses	Short lived cookie used to temporarily store data for the visit.	30 minutes	HTML	Matomo
_pk_cvar	Short lived cookie used to temporarily store data for the visit.	30 minutes	HTML	Matomo
_pk_hsr	Short lived cookie used to temporarily store data for the visit.	30 minutes	HTML	Matomo