Generating and characterizing scenarios for safety testing of autonomous vehicles

Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio, Timothy Tsai, Alejandro Troccoli, Stephen W. Keckler, Siddharth Garg, Anima Anandkumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient mechanisms to both characterize and generate testing scenarios using a state-of-the-art driving simulator. For any scenario, our method generates a set of possible driving paths and identifies all the possible safe driving trajectories that can be taken starting at different times, to compute metrics that quantify the complexity of the scenario. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project, as well as adversarial scenarios generated in simulation. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident. We demonstrate a strong correlation between the proposed metrics and human intuition.

Original languageEnglish (US)
Title of host publication32nd IEEE Intelligent Vehicles Symposium, IV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728153940
StatePublished - Jul 11 2021
Event32nd IEEE Intelligent Vehicles Symposium, IV 2021 - Nagoya, Japan
Duration: Jul 11 2021Jul 17 2021

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings


Conference32nd IEEE Intelligent Vehicles Symposium, IV 2021

ASJC Scopus subject areas

  • Computer Science Applications
  • Automotive Engineering
  • Modeling and Simulation


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