Deep Learning for Error Modeling of Tractor-semitrailer Dynamics Model
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Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2022-10-17
Department
Major/Subject
Data Science
Mcode
SCI3115
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
46+5
Series
Abstract
In the field of autonomous driving, vehicle models are the basis for a variety of research. Vehicle models provide simulated data describing trajectories and dynamics of vehicles, and the property that the utility of vehicle models is independent of real vehicles makes it possible to obtain a large amount of data quickly. Efforts have been made surrounding the construction and validation of vehicle models. However, currently, vehicle models could produce simulation errors under certain circumstances such as extreme speed driving and complex steering. Our solution to the simulation error problem is to predict the error and combine vehicle model simulation and error prediction. In this paper, we propose to apply deep learning to build point prediction error models and uncertainty-aware error models. Valid error models that pass validation tests are expected to offset residual between vehicle models and real systems, which allows using of non-accurate vehicle models. Besides, error prediction is useful to collect cases that have high simulation errors, which are important to analyze and fix vehicle models. The above methods are applied to a tractor-semitrailer dynamics model based on physical principles, provided by Volvo Autonomous Solutions. Involved data is collected from the corresponding tractor-semitrailer, under various driving contexts, and over diverse manoeuvres. Statistics-based evaluation results show that deep learning is potential in vehicle model error modeling, although the uncertainty-aware error model trained for the tractor-semitrailer fails in the validation testDescription
Supervisor
Solin, ArnoThesis advisor
Takkoush, MohamedHelfrich, Thorsten
Keywords
error modeling, deep learning, uncertainty-aware model, recurrent neural network, error reduction, tractor-semitrailer dynamics models