Modern vehicles nowadays are equipped with highly sensitive sensors which continuously log in the information when the vehicle is in motion. These vehicles also deal with some performance issues like more fuel consumption, breakdown, or failure, etc. The information logged in by the sensors can be useful to analyze and evaluate these performance issues. As vehicles are there in the market and are used in multiple places. These vehicles can perform differently based on the way they are operated and driven and the usage of a vehicle varies from time to time. Moreover, the European Accident Research and Safety Report from Volvo Organization describes the factors responsible for road fatalities and accidents. It explains that 90\% of road fatalities are caused by the style of the vehicle being driven and 30\% is caused by the external weather and environmental factor. Therefore, in this work, vehicle usage modeling is done based on time to determine the different usage styles of a vehicle and how they can affect a vehicle's performance. The proposed framework is divided into four separate modules namely: Data pre\textendash processing, Data segmentation, Unsupervised machine learning, and Pattern Analysis. Mainly, ensemble clustering methods are used to extract the pattern of the vehicle usage style and vehicle performance in different seasons using truck logged vehicle data (LVD). From the results, we could build a strong correlation between the vehicle usage style and the vehicle performance that would require further investigation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hh-45730 |
| Date | January 2021 |
| Creators | Kalia, Nidhi Rani, Bagepalli Ashwathanarayana, Sachin Bharadwaj |
| Publisher | Högskolan i Halmstad, Akademin för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.002 seconds