Agente VAMOS! Planejamento de Rotas Veiculares Cientes de Contexto Semântico com Agentes de LLM
Resumo
Sistemas de navegação tradicionais priorizam a eficiência métrica, como tempo ou distância, mas falham frequentemente na interpretação de intenções humanas complexas e dependentes do contexto. Embora os Grandes Modelos de Linguagem (LLMs) demonstrem potencial para preencher essa lacuna semântica, sua integração direta em Sistemas de Transporte Inteligentes (ITS) enfrenta barreiras críticas de escalabilidade, latência e dependência de conectividade. Para superar esses desafios, este trabalho apresenta o VAMOS(Vehicular Agent for Multi-objective Optimization and Semantics), um agente híbrido desenhado para operar eficientemente embarcado. O VAMOS desacopla o raciocínio semântico da otimização espacial, combinando Pequenos Modelos de Linguagem (SLMs) locais para a interpretação de intenções com algoritmos de grafos clássicos para a execução de rotas. A avaliação experimental em três cenários urbanos demonstra que o VAMOS atinge acurácia e completude superiores a 91% utilizando modelos compactos. Além disso, os resultados evidenciam um trade-off favorável: embora modelos massivos apresentem um ganho marginal de qualidade (3%), o VAMOS oferece uma redução significativa no overhead computacional e de comunicação, validando a viabilidade de assistentes de navegação semanticamente conscientes.Referências
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Yang, K., Guo, Z., Lin, G., Dong, H., Huang, Z., Wu, Y., Zuo, D., Peng, J., Zhong, Z., WANG, X., Guo, Q., Jia, X., Yan, J., and Lin, D. (2025). Trajectory-LLM: A language-based data generator for trajectory prediction in autonomous driving. In The Thirteenth International Conference on Learning Representations.
Zhang, S., Li, J., Shi, L., Ding, M., Nguyen, D. C., Tan, W., Weng, J., and Han, Z. (2024a). Federated learning in intelligent transportation systems: Recent applications and open problems. IEEE Transactions on Intelligent Transportation Systems, 25(5):3259–3285.
Zhang, S., Luo, Z., Yang, L., Teng, F., and Li, T. (2024b). A survey of route recommendations: Methods, applications, and opportunities.
Zheng, K., Zheng, Q., Chatzimisios, P., Xiang, W., and Zhou, Y. (2015). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Communications Surveys & Tutorials, 17(4):2377–2396.
Zhou, Y., Lai, S., Han, J., and Liu, H. (2025). An llm-powered cooperative framework for large-scale multi-vehicle navigation.
Zhou, Z., Lin, Y., Jin, D., and Li, Y. (2024). Large language model for participatory urban planning.
Benmessaoud, Y., Cherrat, L., and Ezziyyani, M. (2023). Real-time self-adaptive traffic management system for optimal vehicular navigation in modern cities. Computers, 12(4):80.
Braun, C., Jarczewski, R. O., Talasso, G. U., Villas, L. A., and de Souza, A. M. (2025). Beyond shortest path: Agentic vehicular routing with semantic context.
Chen, A., Ge, X., Fu, Z., Xiao, Y., and Chen, J. (2024a). Travelagent: An ai assistant for personalized travel planning. arXiv preprint arXiv:2409.08069.
Chen, R., Song, W., Zu, W., Dong, Z., Guo, Z., Sun, F., Tian, Z., and Wang, J. (2024b). An llm-driven framework for multiple-vehicle dispatching and navigation in smart city landscapes. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pages 2147–2153.
de Souza, A. M., Braun, T., Botega, L. C., Cabral, R., Garcia, I. C., and Villas, L. A. (2019). Better safe than sorry: a vehicular traffic re-routing based on traffic conditions and public safety issues. Journal of Internet Services and Applications, 10(1):17.
De Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., and Villas, L. A. (2017). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4):1550147716683612.
de Souza, A. M., Oliveira, H. F., Zhao, Z., Braun, T., Loureiro, A. A., and Villas, L. A. (2022). Enhancing sensing and decision-making of automated driving systems with multi-access edge computing and machine learning. IEEE Intelligent Transportation Systems Magazine, 14(1):44–56.
Gong, L., Lin, Y., Zhang, X., Lu, Y., Han, X., Liu, Y., Guo, S., Lin, Y., and Wan, H. (2024). Mobility-llm: Learning visiting intentions and travel preference from human mobility data with large language models. Advances in Neural Information Processing Systems, 37:36185–36217.
Huang, Z., Shi, G., and Sukhatme, G. S. (2024). From words to routes: Applying large language models to vehicle routing. CoRR, abs/2403.10795.
Irugalbandara, C., Mahendra, A., Daynauth, R., Arachchige, T. K., Dantanarayana, J., Flautner, K., Tang, L., Kang, Y., and Mars, J. (2024). Scaling down to scale up: A cost-benefit analysis of replacing openai’s llm with open source slms in production. In 2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pages 280–291.
Jiang, K., Cai, X., Cui, Z., Li, A., Ren, Y., Yu, H., Yang, H., Fu, D., Wen, L., and Cai, P. (2025). Koma: Knowledge-driven multi-agent framework for autonomous driving with large language models. IEEE Transactions on Intelligent Vehicles, 10(10):4655–4668.
Luca, M., Barlacchi, G., Lepri, B., and Pappalardo, L. (2021). A survey on deep learning for human mobility.
Ning, Y. and Liu, H. (2024). Urbankgent: A unified large language model agent framework for urban knowledge graph construction. Advances in Neural Information Processing Systems, 37:123127–123154.
Paiva, S., Ahad, M. A., Tripathi, G., Feroz, N., and Casalino, G. (2021). Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges. Sensors, 21(6):2143.
Qin, Z., Zhang, P., Wang, L., and Ma, Z. (2025). Lingotrip: Spatiotemporal context prompt driven large language model for individual trip prediction. Journal of Public Transportation, 27:100117.
Wang, J., Jiang, R., Yang, C., Wu, Z., Onizuka, M., Shibasaki, R., Koshizuka, N., and Xiao, C. (2024). Large language models as urban residents: An llm agent framework for personal mobility generation. In Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J., and Zhang, C., editors, Advances in Neural Information Processing Systems, volume 37, pages 124547–124574. Curran Associates, Inc.
Wang, R., Zhou, M., Gao, K., Alabdulwahab, A., and Rawa, M. J. (2022). Personalized route planning system based on driver preference. Sensors, 22(1).
Wang, X., Fang, M., Zeng, Z., and Cheng, T. (2023). Where would I go next? large language models as human mobility predictors. CoRR, abs/2308.15197.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837.
Wu, Z., Peng, R., Han, X., Zheng, S., Zhang, Y., and Xiao, C. (2023). Smart agent-based modeling: On the use of large language models in computer simulations.
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., Zheng, R., Fan, X., Wang, X., Xiong, L., Zhou, Y., Wang, W., Jiang, C., Zou, Y., Liu, X., Yin, Z., Dou, S., Weng, R., Cheng, W., Zhang, Q., Qin, W., Zheng, Y., Qiu, X., Huang, X., and Gui, T. (2023). The rise and potential of large language model based agents: A survey.
Yang, K., Guo, Z., Lin, G., Dong, H., Huang, Z., Wu, Y., Zuo, D., Peng, J., Zhong, Z., WANG, X., Guo, Q., Jia, X., Yan, J., and Lin, D. (2025). Trajectory-LLM: A language-based data generator for trajectory prediction in autonomous driving. In The Thirteenth International Conference on Learning Representations.
Zhang, S., Li, J., Shi, L., Ding, M., Nguyen, D. C., Tan, W., Weng, J., and Han, Z. (2024a). Federated learning in intelligent transportation systems: Recent applications and open problems. IEEE Transactions on Intelligent Transportation Systems, 25(5):3259–3285.
Zhang, S., Luo, Z., Yang, L., Teng, F., and Li, T. (2024b). A survey of route recommendations: Methods, applications, and opportunities.
Zheng, K., Zheng, Q., Chatzimisios, P., Xiang, W., and Zhou, Y. (2015). Vehicular networking: A survey and tutorial on requirements, architectures, challenges, standards and solutions. IEEE Communications Surveys & Tutorials, 17(4):2377–2396.
Zhou, Y., Lai, S., Han, J., and Liu, H. (2025). An llm-powered cooperative framework for large-scale multi-vehicle navigation.
Zhou, Z., Lin, Y., Jin, D., and Li, Y. (2024). Large language model for participatory urban planning.
Publicado
25/05/2026
Como Citar
BRAUN, Carnot; GUIDONI, Daniel L.; CERQUEIRA, Eduardo; COSTA, Joahannes B. D. da; VILLAS, Leandro; SOUZA, Allan M. de.
Agente VAMOS! Planejamento de Rotas Veiculares Cientes de Contexto Semântico com Agentes de LLM. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 44. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 57-70.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2026.19806.
