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)
|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|
|Note||Masters Thesis Computer Science 2019|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|