Road Condition Estimation With Data Mining Methods Using Vehicle Based Sensors
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Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors
Author: Masino, Johannes
language: en
Publisher: KIT Scientific Publishing
Release Date: 2021-03-31
The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation.
Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
Author: Scheubner, Stefan
language: en
Publisher: KIT Scientific Publishing
Release Date: 2022-06-03
This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
Experimental investigation of relevant road surface descriptors for tire-road noise measurements on low-absorbing road surfaces
Author: Pinay, Julien
language: en
Publisher: KIT Scientific Publishing
Release Date: 2024-01-16
Ihrer Arbeit in der Originalsprache: This work aims at identifying relevant road surface characteristics to mitigate tire-road noise of free-rolling tires using a systematic approach. As using open porous roads is already known as an efficient measure to reduce tire rolling noise, this study will focus on compact road surfaces which have a low acoustic absorption. Measurements on standardized ISO 10844 test tracks and on public roads are used to study the norm's representativity and its completeness.