Master’s thesis: Trailer Motion estimation

Background
Motion estimation for autonomous vehicles is concerned with extracting the dynamic characteristics and behavior of the whole vehicle combination. It has the task to provide accurate values in order to allow the controllers of all the available actuators in a vehicle to perform safe and efficient maneuvers, e.g., follow the intended path along the road. One of the major challenges is to guarantee accurate and reliable values in real time combining different sensors.
Objective
The objective of the thesis is to develop algorithms for data fusion from different sensors in the trailer in order to obtain information about the dynamic behavior of the unit. The main motivation is to obtain pose, angles, velocities, accelerations, and properties (e.g., mass, inertial, axle loads, etc.) of the trailer and the estimation of the movement capabilities of the combination. Especially, the roll-angle is of interest, as it is linked to the very dangerous case of roll-over. The articulation angle between tractor and trailer is also of special interest, due to its importance in planning the required road-space and avoiding jackknifing of the combination. How to use the trailer’s available sensors and evaluation of additional sensors in order to improve the estimation has to be evaluated. The reliability of the estimations should be evaluated using high-quality instrumentation in real vehicle tests.
Scope and Method
First, a literature survey about the state of the art of the data fusion methods for vehicles needs to be conducted. The flexibility of the frame of tractor and trailer has to be taken into consideration. After this step, a suitable selection of the signals from the current sensors in the trailer and additional sensors should be identified. After this, one or multiple methods using the selected sensors shall be implemented to obtain a high-quality estimate. An analysis of their reliability given realistic external disturbances and noise as encountered in real driving situations should be conducted. The final, fused estimations and algorithms shall be verified both in simulation and in tests with a vehicle combination.
The duration of the study will be 20 weeks (30 ETCs, MSc thesis). The work will be carried out at Volvo Group. The outcome has potential to be published in a scientific journal or conference (depending on quality and ambitions). A suitable background is vehicle dynamics, physical modeling, data fusion and signal processing. Knowledge of Matlab/Simulink would be beneficial.
Thesis Level: Master, 1-2 Students
Language: English
Starting date: January 2020
Academic Supervisor:
Leon Henderson, Chalmers/Volvo Group, leon.henderson@chalmers.se
José Vilca, Volvo Group, jose.vilca@volvo.com
Thorsten Helfrich, Volvo Group, thorsten.helfrich@volvo.com

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