Performance Analysis of a Voice-Integrated Overtaking Assistance Application based on LiDAR and Fuzzy Logic
Keywords:
overtaking assistance, Golang, Sherpa-ONNX, edge computing, voice assistantAbstract
This research aims to enhance driver situational awareness during high-risk overtaking maneuvers by developing Navienta (Navigation Intelligent Assistant), a localized AI-powered navigation assistant. Conventional driver assistance systems often suffer from high latency and cloud dependencies that compromise real-time safety. To address these challenges, we implemented a localized edge-computing architecture utilizing a TF-350 LiDAR sensor and an Intel NUC as a processing hub, specifically designed to facilitate a high-speed, voice-driven interface. The system utilizes an Extended Kalman Filter (EKF) and a Mamdani Fuzzy Inference System (FIS) as the computational core to transform complex environmental dynamics into instantaneous voice instructions, ensuring low-latency feedback for the driver. The scientific contribution of this work lies in the synergistic integration of kinematic smoothing and fuzzy decision-making within a fully localized, high-concurrency architecture, eliminating cloud-dependency for safety-critical maneuvers. Experimental results confirm the system's precision with an average relative distance error of 0.22% and a consistent 50 ms end-to-end latency via a high-concurrency Golang backend. Experimental trials demonstrated that the localized Sherpa-ONNX engine achieved a 95.1% command recognition rate, which directly contributed to a significant 38.8% reduction in driver reaction time (from 1.8s to 1.1s). By maintaining operational integrity without external API dependencies, the Navienta framework provides a robust, cross-platform solution for modern intelligent transportation systems, offering a reliable approach for localized, safety-critical driver assistance.
References
[1] E. Yurtsever, J. Lambert, A. Carballo, & K. Takeda, “A Survey of Autonomous Driving: Common Practices and Emerging Technologies,” IEEE Access, vol. 8, pp. 58443–58469, April 2020. https://doi.org/10.1109/ACCESS.2020.2983149
[2] Y. Yamada, A. S. M. Bakibillah, K. Hashikura, M. A. S. Kamal, & K. Yamada, “Autonomous Vehicle Overtaking: Modeling and an Optimal Trajectory Generation Scheme,” Sustainability, vol. 14, no. 3, pp. 1807, January 2022. https://doi.org/10.3390/su14031807
[3] M. A. Alsuwaiket, “Optimizing Autonomous Vehicle Navigation Through Reinforcement Learning in Dynamic Urban Environments,” World Electric Vehicle Journal, vol. 16, no. 8, pp. 472, August 2025. https://doi.org/10.3390/wevj16080472
[4] A. K. Mengel, I. Nastjuk, H. Lechte, & L. M. Kolbe, “How can I trust you? The development of trust in autonomous vehicles,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 114, pp. 354–375, January 2025. https://doi.org/10.1016/j.trf.2025.05.032
[5] R. Kaufman, J. Costa, & E. Kimani, “Effects of multimodal explanations for autonomous driving on driving performance, cognitive load, expertise, confidence, and trust,” Scientific Reports, vol. 14, pp. 13061, June 2024. https://doi.org/10.1038/s41598-024-62052-9
[6] S. Y. Alaba & J. E. Ball, “A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving,” Sensors, vol. 22, no. 24, pp. 9577, December 2022. https://doi.org/10.3390/s22249577
[7] E. Y. Rineh & R. L. Cheu, “Fuzzy inference systems for discretionary lane changing decisions: Model improvements and research challenges,” International Journal of Transportation Science and Technology, vol. 17, pp. 312–327, March 2025. https://doi.org/10.1016/j.ijtst.2024.05.001
[8] R. Rokhana, S. Anggraini, R. Sukmaningrum, H. Oktavianto, P. S. Wardana, A. W. Rahmaning, M. Rochmad, Kemalasari, H. H. Efendi, and Z. Arief, “Automatic Control of Oxygen Flow for Hypoxemia Therapy Based on Fuzzy Method,” Journal of Electrical and Intelligent Systems (JEIS), vol. 1, no. 1, pp. 9–17, April 2026. https://journals.pens.ac.id/jeis/article/view/46
[9] C. Al Haddad, M. Abouelela, G. Hancox, F. Pilkington-Cheney, T. Brijs, & C. Antoniou, “A Multi-Modal Warning–Monitoring System Acceptance Study: What Findings Are Transferable?” Sustainability, vol. 14, no. 19, pp. 12017, September 2022. https://doi.org/10.3390/su141912017
[10] T. Salomon, L. Maile, P. Meyer, F. Korf, & T. C. Schmidt, “Negotiating strict latency limits for dynamic real-time services in vehicular time-sensitive networks,” Vehicular Communications, vol. 57, pp. 100985, January 2026. https://doi.org/10.1016/j.vehcom.2025.100985
[11] E. Kohanpour, S. R. Davoodi, & K. Shaaban, “Trends in Autonomous Vehicle Performance: A Comprehensive Study of Disengagements and Mileage,” Future Transportation, vol. 5, no. 2, pp. 38, April 2025. https://doi.org/10.3390/futuretransp5020038
[12] S. E. Tsai & C. H. Hsieh, “A Real-Time Collision Warning System for Autonomous Vehicles Based on YOLOv8n and SGBM Stereo Vision,” Electronics, vol. 14, no. 21, pp. 4275, October 2025. https://doi.org/10.3390/electronics14214275
[13] G. Yan, K. Liu, C. Liu, & J. Zhang, “Edge Intelligence for Internet of Vehicles: A Survey,” IEEE Transactions on Consumer Electronics, vol. 70, no. 2, pp. 4858–4877, May 2024. https://doi.org/10.1109/TCE.2024.3378509
[14] B. Yang, J. Li, and T. Zeng, “A Review of Environmental Perception Technology Based on Multi-Sensor Information Fusion in Autonomous Driving,” World Electric Vehicle Journal, vol. 16, no. 1, p. 20, January 2025. https://doi.org/10.3390/wevj16010020
[15] K. D. P. Damsara & A. G. de Barros, “A Systematic Review on User Acceptance of Advanced Driver Assistance Systems (ADAS),” Transportation Research Procedia, vol. 82, pp. 3472–3482, January 2025. https://doi.org/10.1016/j.trpro.2024.12.082
[16] M. Sadaf et al., “Connected and Automated Vehicles: Infrastructure, Applications, Security, Critical Challenges, and Future Aspects,” Technologies, vol. 11, no. 5, p. 117, September 2023. https://doi.org/10.3390/technologies11050117
[17] S. Hussein, S. J. Abbas, G. F. Ali, N. K. Hadi, and M. M. M. Maadi, “A Comparative Analysis of Programming Languages Used in Microservices,” International Journal of Computational and Experimental Science and Engineering, vol. 11, no. 1, February 2025. https://doi.org/10.22399/ijcesen.977
[18] B. A. Enache, C. K. Banica, and A. G. Bogdan, “Performance Analysis of MQTT Over Websocket for IoT Applications,” The Scientific Bulletin of Electrical Engineering Faculty, vol. 23, no. 1, pp. 46–49, September 2023. https://doi.org/10.2478/sbeef-2023-0008
[19] A. Valladares-Poncela, P. Fraga-Lamas, and T. M. Fernández-Caramés, “On-Device Automatic Speech Recognition for Low-Resource Languages in Mixed Reality Industrial Metaverse Applications: Practical Guidelines and Evaluation of a Shipbuilding Application in Galician,” IEEE Access, vol. 13, pp. 77017–77038, April 2025. https://doi.org/10.1109/ACCESS.2025.3564137
[20] Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, & J. Zhang, “Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1738–1762, August 2019. https://doi.org/10.1109/JPROC.2019.2918951
[21] T. Sun, Z. Gao, F. Gao, T. Zhang, S. Chen, & K. Zhao, “A Brain-Inspired Decision-Making Linear Neural Network and Its Application in Automatic Drive,” Sensors, vol. 21, no. 3, pp. 794, January 2021. https://doi.org/10.3390/s21030794
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dafit Ody Endriantono, Dedid Cahya Happyanto, Niam Tamami (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
