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The analysis and use of motor vehicle telemetry data

Palovuori, Tomi (2022)

 
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Palovuori, Tomi
2022

Master's Programme in Computing Sciences
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited.
Hyväksymispäivämäärä
2022-10-20
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202206305943
Tiivistelmä
In order to operate a vehicle, information about the vehicle must be communicated to the operator, as it is difficult or impossible to determine otherwise. Information such as vehicle speed, remaining fuel or whether seatbelts are on can be measured and transmitted electrically. Some measurements can be specific to a vehicle type or require processing with other metrics to provide value. The measurements can be displayed immediately or stored for a longer period of time before they are sent for a longer time period analysis.
Besides the immediate need to access vital operating metrics, there are also other uses for gathered data. Analyzing measurements from a longer period of time can show trends in time. Combining various metrics can also provide a tangible metric for more abstract concepts, such as vehicle efficiency, convenience or safety. Data is also more valuable if gathered from a larger number of similar vehicles. A larger business can use a larger dataset to obtain good comparison points and a robust average for future estimates.
The value derived from vehicle telemetry in businesses appears in different forms. Most notably, business analytics can be applied to reduce vehicle downtime and predict future profits and costs. The performance of hired drivers can also be compared to each other to improve performance by rewarding good driving practices. Measurements a vehicle makes can also be used to discover indicators for parts wear and predict future failures. Vehicle telemetry is not only limited to private use, as services such as vehicle live location are brought to consumers as well through different applications.
In the future, more data is required for more complicated applications. For autonomous vehicles, the vehicle will require measurements an operator would normally make themselves, such as a visual of other vehicles. Besides the operating capacity, the ability to detect wear and smaller malfunctions within the vehicle itself is imperative, as there might be no person to notice and report them. A concrete stepping stone for this is the ability to track passengers within the vehicle. For public transportation, the vehicle must know when passengers are done entering and leaving. If the amount of people is measured, it is also possible to deduce the number of people inside the vehicle at a given point in time. This information can be used by a business to measure the number of passenger miles a vehicle provides, or by consumers in order to avoid crowded transportation vehicles.
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