Framework for leveraging data from autonomous trucks to improve state maintenance operations
| dc.contributor.advisor | Walton, C. Michael | |
| dc.creator | Avera, Morgan Paige | |
| dc.creator.orcid | 0000-0002-5210-9695 | |
| dc.date.accessioned | 2021-09-09T21:16:32Z | |
| dc.date.available | 2021-09-09T21:16:32Z | |
| dc.date.created | 2020-08 | |
| dc.date.issued | 2020-08-14 | |
| dc.date.submitted | August 2020 | |
| dc.date.updated | 2021-09-09T21:16:33Z | |
| dc.description.abstract | Freight is key in fueling the economy and trucks are a vital connector which moved approximately 30,000 tons of cargo per day in 2018 [92]. Technology is improving, a shortage of drivers continues to expand, and fatalities occur on highways each day. An urgent need to move goods safely and efficiently has propelled the development of autonomous trucks (ATs). Using a combination of technologies, computing can replace a human driver for long, monotonous stretches of highway driving. As ATs hit the road, they are collecting massive amounts of information which could be valuable to those managing and maintaining roadways. Simultaneously, state agencies lack the resources needed to maintain the roadways which provide vital connectivity. Routine maintenance addresses day to day concerns which are often the hardest to track considering the lack of predictability. While it is simple to replace a fallen sign, it is hard to know when a sign has fallen. States have worked to develop systems for sourcing feedback from those who travel on their roadways, but there would be distinct value in adding another source of information. The data being collected by newly deployed ATs can be leveraged to assist in identifying routine maintenance concerns, so they can be addressed quickly which keeps roads in a better condition. This study lays out how state agencies can implement a data-sharing framework to leverage the operation of ATs on their roadways. Working together, a platform can be built into existing systems that allows AT companies to report maintenance events they spot during operation on state-owned highways. These reports would have higher veracity than typical reporting mechanisms because ATs are equipped with high-quality cameras. Using data provided by Kodiak Robotics, a prototype mapping module was created to showcase how this system would work. Input was provided by private sector and public sector representatives. Both groups agree that there is valuable data available which could be leveraged by the state. Since the data involved in reporting maintenance events is not critical to AT operation, this could provide a starting point for state agencies to work with autonomous technology developers without navigating complex data-sharing agreements. | |
| dc.description.department | Civil, Architectural, and Environmental Engineering | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/2152/87572 | |
| dc.identifier.uri | http://dx.doi.org/10.26153/tsw/14516 | |
| dc.language.iso | en | |
| dc.subject | Autonomous vehicle | |
| dc.subject | Freight planning | |
| dc.subject | Data-sharing | |
| dc.subject | Road maintenance | |
| dc.title | Framework for leveraging data from autonomous trucks to improve state maintenance operations | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Civil, Architectural, and Environmental Engineering | |
| thesis.degree.discipline | Civil Engineering | |
| thesis.degree.grantor | The University of Texas at Austin | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science in Engineering |
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