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DeepCrashTest: Translating Dashcam Videos to Virtual Tests forAutomated Driving Systems


Abstract The autonomous vehicle technology has come a long way, but currently, there are no companies that are able to offer fully autonomous ride in any conditions, on any road without any human supervision. These systems should be extensively trained and validated to guarantee safe human transportation. Any small errors in the system functionality may lead to fatal accidents and may endanger human lives. Deep learning methods are widely used for environment perception and prediction of hazardous situations. These techniques require huge amount of training data with both normal and abnormal samples to enable the vehicle to avoid a dangerous situation.



The goal of this thesis is to generate simulations from real-world tricky collision scenarios ... (more)
Created Date 2019
Contributor Bashetty, Sai Krishna (Author) / Fainkeos, Georgios (Advisor) / Amor, Heni Ben (Advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Subject Computer engineering / Autonomous Vehicles / Computer Vision / Deep Learning / Intelligent Transportation Systems / Simulation and Animation / Testing and Evaluation
Type Masters Thesis
Extent 55 pages
Language English
Copyright
Note Masters Thesis Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


  Full Text
43.9 MB application/pdf
Download Count: 472

Description Dissertation/Thesis
  DeepCrashTest_demo.mp4
29.4 MB video/mp4
Download Count: 70

Description Video Demonstration