Browsing by Author "Rathinam, Sivakumar"
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- Autonomous Vehicles for Small Towns: Exploring Perception, Accessibility, and SafetyLi, Wei; Ye, Xinyue; Li, Xiao; Dadashova, Bahar; Ory, Marcia G.; Lee, Chanam; Rathinam, Sivakumar; Usman, Muhammad; Chen, Andong; Bian, Jiahe; Li, Shuojia; Du, Jiaxin (Safe-D University Transportation Center, 2023-09)As of 2021, there were 18,696 small towns in the US with a population of less than 50,000. These communities typically have a low population density, few public transport services, and limited accessibility to daily services. This can pose significant challenges for residents trying to fulfill essential travel needs and access healthcare. Autonomous vehicles (AVs) have the potential to provide a convenient and safe way to get around without requiring human drivers, making them a promising transportation solution for these small towns. AV technology can become a first-line mobility option for people who are unable to drive, such as older adults and those with disabilities, while also reducing the cost of transportation for both individuals with special needs and municipalities. The report includes our research findings on 1) how residents in small towns perceive AV, including both positive and negative aspects; 2) the impacts of ENDEAVRide—a novel “Transport + Telemedicine 2-in-1” microtransit service delivered on a self-driving van in central Texas—on older adults’ travel and quality of life; and 3) the potential safety implications of AVs in small towns. This report will help municipal leaders, transportation professionals, and researchers gain a better understanding of how AV deployment can serve small towns.
- Reference Machine Vision for ADAS FunctionsNayak, 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.
- 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.
- A Sensor Fusion and Localization System for Improving Vehicle Safety in Challenging Weather ConditionsSingh, Abhay; Vegamoor, Vamsi Krishna; Rathinam, Sivakumar (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-12)SAE Level 5 autonomy requires the autonomous vehicle to be able to accurately sense the environment and detect obstacles in all weather and visibility conditions. This sensing problem becomes significantly challenging in weather conditions that include such events as sudden change in lighting, smoke, fog, snow, and rain. There is no standalone sensor currently on the market that can provide reliable perception data in all conditions. We demonstrate that a combination of Long Wave Infrared (LWIR) cameras with radar provide a viable sensing system that is robust to adverse visibility conditions. We have validated this prototype system both in simulation as well as in real-world traffic using a 2017 Lincoln MKZ operating in College Station, TX.
- Technology to Ensure Equitable Access to Automated Vehicles for Rural AreasNinan, Stephen; Rathinam, Sivakumar (Safe-D National UTC, 2023-08)A significant majority of state-of-the-art autonomous sensing and navigation technologies rely on good lane markings or detailed 3D maps of the environment, making them more suited for urban communities. Conversely, many rural roads in the U.S. do not have lane markings and have irregular boundaries. These challenges are common to many small and rural communities (SRCs), which are sparsely connected and cover huge areas. The objective of this project was to develop an efficient sensing and navigation system for SRCs that uses crowdsourced topological maps, such as OpenStreetMap, and provides high-level road network information in concert with onboard sensing systems that include lidar and cameras to localize and navigate an autonomous vehicle. The system was tested and validated on rural roads in an SRC around Bryan, TX.