Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: Autonomous Vehicles
- Creators: Turaga, Pavan
- Creators: Yu, Hongbin
Description
The focus of this study was to address the problem of prohibitively expensive LiDARs currently being used in autonomous vehicles by analyzing the capabilities and shortcomings of affordable LiDARs as replacements. This involved the characterization of affordable LiDARs that are currently available on the market. The characterization of the LiDARs involved testing refresh rates, field of view, distance the sensors could detect, reflectivity, and power of the emitters. The four LiDARs examined in this study were the Scanse, RPLIDAR A2, LeddarTech Vu8, and LeddarTech M16. Of these low cost LiDAR options we find the two best options for use in affordable autonomous vehicle sensors to be the RPLIDAR A2 and the LeddarTech M16.
ContributorsMurphy, Thomas Joseph (Co-author) / Gamal, Eltohamy (Co-author) / Yu, Hongbin (Thesis director) / Houghton, Todd (Committee member) / Electrical Engineering Program (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
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 for training and testing autonomous vehicles. Dashcam crash videos from the internet can now be utilized to extract valuable collision data and recreate the crash scenarios in a simulator. The problem of extracting 3D vehicle trajectories from videos recorded by an unknown monocular camera source is solved using a modular approach. The framework is divided into two stages: (a) extracting meaningful adversarial trajectories from short crash videos, and (b) developing methods to automatically process and simulate the vehicle trajectories on a vehicle simulator.
The goal of this thesis is to generate simulations from real-world tricky collision scenarios for training and testing autonomous vehicles. Dashcam crash videos from the internet can now be utilized to extract valuable collision data and recreate the crash scenarios in a simulator. The problem of extracting 3D vehicle trajectories from videos recorded by an unknown monocular camera source is solved using a modular approach. The framework is divided into two stages: (a) extracting meaningful adversarial trajectories from short crash videos, and (b) developing methods to automatically process and simulate the vehicle trajectories on a vehicle simulator.
ContributorsBashetty, Sai Krishna (Author) / Fainkeos, Georgios (Thesis advisor) / Amor, Heni Ben (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2019