Anomaly Detection in Simulated Vehicle Dynamics Using BeamNG.tech and the TEDA Framework

Resumo


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.
Palavras-chave: vehicle simulation, anomaly detection, driver behavior

Referências

J. Zhou, Y. Zhang, S. Guo, and Y. Guo, “A survey on autonomous driving system simulators,” in 2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE, 2022, pp. 301–306.

F. Khan, H. Anwar, and D. Pfahl, “Simulation-based safety testing of automated driving systems,” in International Conference on Product-Focused Software Process Improvement. Springer, 2023, pp. 133–138.

R. E. Amini, E. Michelaraki, C. Katrakazas, C. Al Haddad, B. De Vos, A. Cuenen, G. Yannis, T. Brijs, and C. Antoniou, “Risk scenario designs for driving simulator experiments,” in 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2021, pp. 1–6.

E. Castellano, A. Cetinkaya, and P. Arcaini, “Analysis of road representations in search-based testing of autonomous driving systems,” in 2021 IEEE 21st International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2021, pp. 167–178.

T. Zhang, H. Liu, W. Wang, and X. Wang, “Virtual tools for testing autonomous driving: A survey and benchmark of simulators, datasets, and competitions,” Electronics, vol. 13, no. 17, p. 3486, 2024.

D. Garikapati and S. S. Shetiya, “Autonomous vehicles: Evolution of artificial intelligence and the current industry landscape,” Big Data and Cognitive Computing, vol. 8, no. 4, p. 42, 2024.

N. Fouladinejad, M. K. A. Jalil, and J. M. Taib, “Reduction of computational cost in driving simulation subsystems using approximation techniques,” in 2014 International Conference on Industrial Automation, Information and Communications Technology. IEEE, 2014, pp. 111–117.

C. Birchler, N. Ganz, S. Khatiri, A. Gambi, and S. Panichella, “Cost-effective simulation-based test selection in self-driving cars software with sdc-scissor,” in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2022, pp. 164–168.

P. Angelov, “Anomaly detection based on eccentricity analysis,” 2014, pp. 1–8.

M. Silva, T. Flores, P. Andrade, J. Silva, I. Silva, and D. G. Costa, “An online unsupervised machine learning approach to detect driving related events,” in IECON 2022–48th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2022, pp. 1–6.

C. Birchler, C. Rohrbach, T. Kehrer, and S. Panichella, “Sensodat: Simulation-based sensor dataset of self-driving cars,” in 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR). IEEE, 2024, pp. 510–514.

M. Azevedo, M. Andrade, M. Medeiros, T. Medeiros, M. Silva, I. Silva, E. Sisinni, and P. Ferrari, “Optimizing vehicle IoT systems: SUMO-digital twin performance analysis,” in 2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0 & IoT). IEEE, 2024, pp. 204–209.

M. Medeiros, T. Flores, M. Silva, and I. Silva, “A multi-layered methodology for driver behavior analysis using TinyML and edge computing,” in 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2024, pp. 1–8.

M. Biagiola and S. Klikovits, “SBFT tool competition 2024-cyber-physical systems track,” in Proceedings of the 17th ACM/IEEE International Workshop on Search-Based and Fuzz Testing, 2024, pp. 33–36.

P. Andrade, I. Silva, M. Silva, T. Flores, J. Cassiano, and D. G. Costa, “A TinyML soft-sensor approach for low-cost detection and monitoring of vehicular emissions,” Sensors, vol. 22, no. 10, 2022.

B. Team, “Outgauge support — BeamNG documentation,” 2023, accessed: 2024-10-11.

M. Medeiros, M. Azevedo, M. Amaral, C. M. Viegas, M. Silva, I. Silva, and D. G. Costa, “A framework for efficient communication in IoT applications in the automotive industry with OBD-II edge,” in 2023 Symposium on Internet of Things (SIoT). IEEE, 2023, pp. 1–5.

A. García, X. Oregui, J. Franco, and U. Arrieta, “Edge containerized architecture for manufacturing process time series data monitoring and visualization,” in IN4PL, 2022, pp. 145–152.
Publicado
28/11/2024
MEDEIROS, Morsinaldo; ANDRADE, Matheus; MEDEIROS, Thaís; SILVA, Marianne; SILVA, Ivanovitch. Anomaly Detection in Simulated Vehicle Dynamics Using BeamNG.tech and the TEDA Framework. In: WORKSHOP LATINOAMERICANO DE DEPENDABILIDADE E SEGURANÇA EM SISTEMAS VEICULARES, 1. , 2024, Recife/PE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 25-28. DOI: https://doi.org/10.5753/ssv.2024.32625.