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A Modular Detection System for Smart Cities: Integrating Monocular and LiDAR Solutions for Scalable Traffic Monitoring 11

A Modular Detection System for Smart Cities: Integrating Mon...

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11th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2025

作者： Borau-Bernad, Javier Ramajo-Ballester, Álvaro María Armingol Moreno, José Sanchis de Miguel, Araceli Intelligent Systems Lab University Carlos III of Madrid Av. de la Universidad 30 Leganés Madrid28911 Spain Control Learning and Systems Optimization Group University Carlos III of Madrid Av. de la Universidad 30 Leganés Madrid28911 Spain

ISBN: (纸本)9789897587450

As smart cities continue to develop, they require scalable and efficient traffic monitoring systems. This paper presents a modular detection system that switches between monocular and multimodal modes, depending on the available sensors. The monocular mode, based on the MonoLSS algorithm, offers a cost-effective vehicle detection solution using a single camera, ideal for simpler or low-budget setups. In contrast, the multimodal mode integrates camera and LiDAR data via the MVX-Net model, enhancing 3D accuracy in complex traffic scenarios. This dual-mode flexibility allows smart cities to adapt the system to their infrastructure and budgetary needs, ensuring scalability as urban demands evolve. Inference results demonstrate the superior accuracy of the multimodal approach in challenging environments while validating the efficiency of the monocular mode for simpler settings. Therefore, the modular detection system offers a flexible solution that optimizes both cost and performance, effectively addressing the varied requirements of smart city traffic management. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Budget control

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BEHAVIORAL BASED DECISION-MAKING SCHEME FOR COOPERATIVE CONNECTED AUTOMATED MOBILITY 9

BEHAVIORAL BASED DECISION-MAKING SCHEME FOR COOPERATIVE CONN...

引用

9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on Computer Science and Information systems 2024, MCCSIS 2024

作者： Yagüe-Cuevas, David Marín-Plaza, Pablo Paz-Sesmero, María Sanchis, Araceli Intelligent Systems Lab. University Carlos III Madrid Spain Control Learning and Systems Optimization Group University Carlos III Madrid Spain

ISBN: (纸本)9789898704597

For some time now, the automotive industry has been undergoing major adjustments in recent years. Current technological solutions aimed to achieve an autonomous level 5 platform implement increasingly complex software and hardware systems. On the one hand, high-tech fields such as smart cities, the Internet of Things (IoT), and intelligent transportation systems (ITS) will benefit from connected and cooperative vehicles in the future. On the other hand, the more complex these systems are, the more knowledge, computing power and precision they require, and the more difficult they become to debug. There are still several challenges in the field of autonomous vehicles to address. From sensor perception to system localization, through arbitration and decision making. The goal of this paper is to address some of the problems regarding those latter fields by aggregating all the structured traffic knowledge extracted from the vehicle context. This knowledge will be used to deploy an effective decision-making system based on behaviors capable of choosing what to do when certain conditions are met. Actions such as changing its operation mode, selecting the most suitable trajectory or even adjusting the systems to the current situation for a better automated vehicle experience. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Automotive industry

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