Inferring Driver Behavior Profiles Using Digital Twins in Simulated Environments
Resumo
Understanding driver behavior is crucial for enhancing road safety and improving fuel efficiency. However, collecting data on these behaviors in real-world settings is challenging due to vehicle complexity and high instrumentation costs. Driving simulators offer a viable alternative by creating environments that replicate real-life situations. This study explores the use of digital twins in simulated environments, specifically in Euro Truck Simulator 2 (ETS2), to infer driver behavior using virtual sensors. A case study was conducted where a driver simulated routes under two driving conditions: cautious and aggressive. The telemetry data collected during these simulations were analyzed to identify behavioral patterns and assess fuel consumption efficiency. The results demonstrated that digital twins enable real-time capture of driver behavior information, revealing significant differences between driving styles. Analysis of the accumulated data and the radar area soft sensor indicated that cautious driving practices are associated with greater fuel efficiency.
Palavras-chave:
driver behavior analysis, digital twins, simulated environments, virtual sensors, fuel efficiency
Referências
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T. Flores, M. Silva, P. Andrade, J. Silva, I. Silva, E. Sisinni, P. Ferrari, and S. Rinaldi, “A tinyml soft-sensor for the internet of intelligent vehicles,” in 2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive). IEEE, 2022, pp. 18–23.
H. Shu, “Safety prompt advanced driver-assistance system,” 2024.
S. Stavrev and D. Ginchev, “Detection and analysis of commercial drivers’ focus and attention using sensors and simulators,” in AIP Conference Proceedings, vol. 3064, no. 1. AIP Publishing, 2024.
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. Andrade, M. Medeiros, T. Medeiros, M. Azevedo, M. Silva, D. G. Costa, and I. Silva, “On the use of biofuels for cleaner cities: Assessing vehicular pollution through digital twins and machine learning algorithms,” Sustainability, vol. 16, no. 2, p. 708, 2024.
M. S. Dihan, A. I. Akash, Z. Tasneem, P. Das, S. K. Das, M. R. Islam, M. M. Islam, F. R. Badal, M. F. Ali, M. H. Ahmed et al., “Digital twin: Data exploration, architecture, implementation and future,” Heliyon, 2024.
S. Deng, L. Ling, C. Zhang, C. Li, T. Zeng, K. Zhang, and G. Guo, “A systematic review on the current research of digital twin in automotive application,” Internet of Things and Cyber-Physical Systems, vol. 3, pp. 180–191, 2023.
T. Mecheva, R. Furnadzhiev, and N. Kakanakov, “Modeling driver behavior in road traffic simulation,” Sensors, vol. 22, no. 24, p. 9801, 2022.
F. Alonso, M. Faus, J. V. Riera, M. Fernandez-Marin, and S. A. Useche, “Effectiveness of driving simulators for drivers’ training: a systematic review,” Applied Sciences, vol. 13, no. 9, p. 5266, 2023.
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.
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.
S. Tomažič, “Intelligent soft sensors,” p. 6895, 2023.
A. A. Al-Atawi, S. Alyahyan, M. N. Alatawi, T. Sadad, T. Manzoor, M. Farooq-i Azam, and Z. H. Khan, “Stress monitoring using machine learning, iot and wearable sensors,” Sensors, vol. 23, no. 21, p. 8875, 2023.
T. Flores, M. Silva, P. Andrade, J. Silva, I. Silva, E. Sisinni, P. Ferrari, and S. Rinaldi, “A tinyml soft-sensor for the internet of intelligent vehicles,” in 2022 IEEE International Workshop on Metrology for Automotive (MetroAutomotive). IEEE, 2022, pp. 18–23.
H. Shu, “Safety prompt advanced driver-assistance system,” 2024.
S. Stavrev and D. Ginchev, “Detection and analysis of commercial drivers’ focus and attention using sensors and simulators,” in AIP Conference Proceedings, vol. 3064, no. 1. AIP Publishing, 2024.
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. Andrade, M. Medeiros, T. Medeiros, M. Azevedo, M. Silva, D. G. Costa, and I. Silva, “On the use of biofuels for cleaner cities: Assessing vehicular pollution through digital twins and machine learning algorithms,” Sustainability, vol. 16, no. 2, p. 708, 2024.
Publicado
28/11/2024
Como Citar
ANDRADE, Matheus; MEDEIROS, Morsinaldo; MEDEIROS, Thaís; SILVA, Marianne; SILVA, Ivanovitch.
Inferring Driver Behavior Profiles Using Digital Twins in Simulated Environments. In: WORKSHOP LATINOAMERICANO DE DEPENDABILIDADE E SEGURANÇA EM SISTEMAS VEICULARES, 1. , 2024, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 29-32.
DOI: https://doi.org/10.5753/ssv.2024.32626.
