Methodology to Recognize Vehicle Loading Condition - An Indirect Method Using Telematics and Machine Learning

Connected vehicles technology is experiencing a boom across the globe. Vehicle manufacturers have started using telematics devices which leverage mobile connectivity to pool the data. Though the primary purpose of the telematics devices is location tracking, the additional vehicle information gathered through the devices can bring in much more insights about the vehicles and its working condition. Cloud computing is one of the major enabled for connected vehicles and its data-driven solutions. On the other hand, machine learning and data analytics enable a rich customer experience understanding different inferences from the available data. From a fleet owner perspective, the revenue and the maintenance costs are directly related to the usage conditions of the vehicle. Usage information like load condition could help in efficient vehicle planning, drive mode selection and proactive maintenance [1]. A common approach to vehicle load condition detection is by using exclusive load sensing sensors. This paper explores a possibility of detecting vehicle load conditions without making use of any sensors. Instead, a supervised machine learning model is developed to recognize real-time loading condition, by analyzing vehicle driving behavior. This paper covers a machine learning based approach for load detection of small commercial vehicles, which are less then 1Ton of loading capacity. In this study, the focus is given to differentiating the vehicle behavior at different loading conditions and to select the accurate parameters for machine learning model development. These selected features are based on the domain expertise in vehicle dynamics and statistics of the data. The output of this novel method can be used for optimizing different ADAS functionalities [2] at very low-cost, leveraging telematics units.


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  • Accession Number: 01695613
  • Record Type: Publication
  • Source Agency: SAE International
  • Report/Paper Numbers: 2019-26-0019
  • Files: TRIS, SAE
  • Created Date: Jan 15 2019 11:11AM