Anomaly Detection in Simulated Vehicle Dynamics Using BeamNG.tech and the TEDA Framework - Medeiros, Morsinaldo; Andrade, Matheus; Medeiros, Thaís; Silva, Marianne; Silva, Ivanovitch (Anais do Workshop Latinoamericano de Dependabilidade e Segurança em Sistemas Veiculares (SSV), 2024)
Simulations are crucial in the automotive industry for enhancing safety and cost efficiency, especially given the growing complexity of vehicle systems. This study proposes a methodology that integrates the BeamNG.tech simulation platform with a data collection system and the Typicality and Eccentricity Data Analytics (TEDA) framework to detect anomalies in vehicle speed data, facilitating driver behavior analysis. A case study was conducted where a driver navigated a predefined route while performing maneuvers with intentional speed variations. The TEDA framework successfully identified 1,110 outliers out of 6,388 speed samples, representing approximately 17.38% of the total data. The proposed methodology enables the detection of atypical driving behaviors, demonstrating its potential to contribute to the development of advanced driver assistance systems and improve safety technologies.