Browsing by Author "Nayak, Abhishek"

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    Reference Machine Vision for ADAS Functions
    Nayak, Abhishek; Rathinam, Sivakumar; Pike, Adam (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-05)
    Studies have shown that fatalities due to unintentional roadway departures can be significantly reduced if Lane Departure Warning and Lane Keep Assist systems are used effectively. However, these systems have not been widely adopted due, in part, to the lack of suitable standards for pavement markings that enable reliable functionality of sensor systems. The objective of this project is to develop a reference lane detection system that will provide a benchmark for evaluating different lane markings and perception algorithms. The project will also validate the effectiveness of lane markings’ material characteristics as well as the vision algorithms through a systematic testing of lane detection algorithms in a robust test/vehicle environment.
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    Response of Autonomous Vehicles to Emergency Response Vehicles (RAVEV)
    Nayak, Abhishek; Rathinam, Sivakumar; Gopalswamy, Swaminathan (SAFE-D: Safety Through Disruption National University Transportation Center, 2020-06)
    The objective of this project was to explore how an autonomous vehicle identifies and safely responds to emergency vehicles using visual and other onboard sensors. Emergency vehicles can include police, fire, hospital and other responders’ vehicles. An autonomous vehicle in the presence of an emergency vehicle must have the ability to accurately sense its surroundings in real-time and be able to safely yield to the emergency vehicle. This project used machine learning algorithms to identify the presence of emergency vehicles, mainly through onboard vision, and then maneuver an in-path non-emergency autonomous vehicle to a stop on the curbside. Two image processing frameworks were tested to identify the best combination of vision-based detection algorithms, and a novel lateral control algorithm was developed for maneuvering the autonomous vehicle.