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Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles

Ghodsi, Zahra and Hari, Siva Kumar Sastry and Frosio, Iuri and Tsai, Timothy and Troccoli, Alejandro and Keckler, Stephen W. and Garg, Siddharth and Anandkumar, Animashree (2021) Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles. . (Unpublished)

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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.

Item Type:Report or Paper (Discussion Paper)
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Record Number:CaltechAUTHORS:20210510-132935112
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109036
Deposited By: Tony Diaz
Deposited On:10 May 2021 20:33
Last Modified:10 May 2021 20:33

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