| KEYWORD |
Estimation of vehicle model parameters starting from telemetry data
keywords AUTOMOTIVE, DATA ANALYSIS, DRIVING SIMULATION, VEHICLE DYNAMICS
Reference persons MASSIMILIANA CARELLO, HENRIQUE DE CARVALHO PINHEIRO
External reference persons GRANO ELIA
Research Groups 27- Veicoli innovativi elettrici e ibridi
Thesis type DATA ANALYSIS & MODELING, MODELING AND SIMULATION
Description The technical details of F1 cars are secrets that teams keep private, but there is plenty of telemetry data on the Internet that can be accessed by the public. This data includes information like spatial coordinates of the car, vehicle speed, tire compound and age, and much more. Telemetry data contains precious information that can be used to individuate the parameters that are needed to describe the vehicle behavior.
While the simulation of vehicle performance starting from the car’s parameters is a well-established topic in the literature, not so much work has been done to estimate the vehicle parameters starting from its performance (i.e. telemetry data). Many different phenomena play a role in the overall vehicle performance, including tire wear, vehicle mass reduction due to fuel consumption, traffic, evolution of track conditions and driving style variations. All these phenomena must be carefully considered when analyzing the vehicle telemetry data.
The final goal of this thesis activity is to create a software that takes the telemetry data as inputs and returns the key parameters of the vehicle model as output. After an initial literature review on the approaches that are available to simulate vehicle performance, research effort should be dedicated to determine which is the most suitable algorithm for processing the telemetry data. Then, a code capable of processing the telemetry data should be developed in order to reach the final goal.
Required skills - Software: knowledge of Python is mandatory. Knowledge of MATLAB/Simulink is considered as a plus. Knowledge of AI algorithms is considered as a plus.
- Mechanics/automotive: basic knowledge of the fundamentals of vehicle longitudinal and lateral dynamics is considered as plus.
- Language: knowledge of English is mandatory.
- Personal skill: proactive attitude to problem solving is mandatory. Ability of working in team is considered as a plus.
Deadline 26/07/2024
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