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Vehicle sensors data analysis for detection of driver styles in braking maneuvers

Giuseppe Suriano

Vehicle sensors data analysis for detection of driver styles in braking maneuvers.

Rel. Paolo Garza. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2023

Abstract:

I aimed to uncover and analyze braking patterns in driving behavior using machine learning and Controller Area Network-BUS (CAN-BUS) data. My primary goal was to understand how drivers employ the brake pedal across different scenarios, including highways, rural areas, and urban settings. I followed a structured approach, starting with data preprocessing and feature selection, leading to the development of a Variational AutoEncoder (VAE)-based deep learning framework. I carefully selected input signals like speed, steering angle, and accelerations for their consistency across various vehicle models. Streamlining the model's complexity and enhancing its interpretability were key considerations. During data preprocessing, I addressed challenges in resampling signals to a common 5 Hz frequency, applying noise-cleaning techniques such as interpolation and filtering. Segmenting driving behaviors by environment was crucial. This segmentation allowed me to explore how braking behavior differs across driving scenarios. The core of my project was the VAE, a generative model capable of extracting latent data representations. The encoder-decoder architecture learned and reconstructed driving patterns. To bolster the model's robustness, I included a classifier to distinguish real braking data from noise within the latent space. Training involved typical VAE loss functions, including reconstruction and KL divergence loss. The trained VAE transformed complex braking data into compact vectors. Clustering similar braking scenes was a challenge, but I optimized cluster numbers by minimizing variance in key performance indicators (KPIs). Insights from clusters were extracted using t-SNE visualizations, speed and acceleration plots, and KPI percentiles.

Relators: Paolo Garza
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 78
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Data Science And Engineering
Classe di laurea: New organization > Master science > LM-32 - COMPUTER SYSTEMS ENGINEERING
Ente in cotutela: Toyota Motor Europe (BELGIO)
Aziende collaboratrici: Toyota Motor Europe
URI: http://webthesis.biblio.polito.it/id/eprint/28647
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