The future of vehicle automation heavily relies on vehicle simulations for the upbringing of different autonomous applications, whether it is related to path planning, motion planning or control. The backbone of vehicle simulations is a vehicle model upon which the application is tested. Since the development happens at different stages, there is a need of different vehicle model fidelities.
High fidelity models are generally used for off-line simulations and in fast computing rigs where the high fidelity model tries to mimic as much as possible the dynamics and physics of a real truck. High fidelity models are verified against measured data and used as a reference for simpler, low fidelity model verification.
The aim of this thesis proposal is to develop a process to validate high-fidelity models based on real test data collected in Volvo automation projects. The focus will be on a statistical data driven approach for model validation where we can answer questions related to the quantity and quality of the data collected in relation to the validity of the high-fidelity model developed at V.A.S now. Those efforts are crucial for the development of both the validation techniques as well as the vehicle models used at V.A.S.
The complete picture can be depicted as:
Given a vehicle model, and for specific maneuver, provide a procedure for:
1. Set quality requirements for test data. Break down maneuver and perform required tests.
2. Construct error model for vehicle model.
3. Validate vehicle model + error model
The proposed validation process is intended for high fidelity vehicle models that are used for simulation purposes, such as safety assessments, control verification, software integration. This thesis will focus on the first step of the project aim on the topic of test data requirements.
Talented master students in Automotive, Mathematics, Computer Science or Data Science, with some knowledge of Python or similar programming skills.
Please provide CV & transcript.
Thesis Level: Master
Starting date: January 10, 2022 (flexible)
Number of students: 1 or 2
Academic supervisor: Hans-Martin Heyn, firstname.lastname@example.org.
Industrial supervisors: Mohamed Takkoush, email@example.com, and Rikard Wadman, firstname.lastname@example.org.
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