Mahdi Rahmani

Minghao Ning, Yufeng Yang, Keqi Shu et al.

Vehicle-infrastructure (V2I) cooperative perception can substantially extend the range, coverage, and robustness of autonomous driving systems beyond the limits of onboard-only sensing, particularly in occluded and adverse-weather environments. However, its practical value is still difficult to quantify because existing benchmarks do not adequately capture large-scale multi-node deployments, realistic communication conditions, and adverse-weather operation. This paper presents CoInfra, a deployable cooperative infrastructure perception platform comprising 14 roadside sensor nodes connected through a commercial 5G network, together with a large-scale dataset and an open-source system stack for V2I cooperation research. The system supports synchronized multi-node sensing and delay-aware fusion under real 5G communication constraints. The released dataset covers an eight-node urban roundabout under four weather conditions (sunny, rainy, heavy snow, and freezing rain) and contains 294k LiDAR frames, 589k camera images, and 332k globally consistent 3D bounding boxes. It also includes a synchronized V2I subset collected with an autonomous vehicle. Beyond standard perception benchmarks, we further evaluate whether infrastructure sensing improves awareness of safety-critical traffic participants during roundabout interactions. In structured conflict scenarios, V2I cooperation increases critical-frame completeness from 33%-46% with vehicle-only sensing to 86%-100%. These results show that multi-node infrastructure perception can significantly improve situational awareness in conflict-rich traffic scenarios where vehicle-only sensing is most limited.

Mahdi Rahmani, Melika Sabaghian, Seyyede Mahila Moghadami et al.

Object detection has always been practical. There are so many things in our world that recognizing them can not only increase our automatic knowledge of the surroundings, but can also be lucrative for those interested in starting a new business. One of these attractive objects is the license plate (LP). In addition to the security uses that license plate detection can have, it can also be used to create creative businesses. With the development of object detection methods based on deep learning models, an appropriate and comprehensive dataset becomes doubly important. But due to the frequent commercial use of license plate datasets, there are limited datasets not only in Iran but also in the world. The largest Iranian dataset for detection license plates has 1,466 images. Also, the largest Iranian dataset for recognizing the characters of a license plate has 5,000 images. We have prepared a complete dataset including 20,967 car images along with all the detection annotation of the whole license plate and its characters, which can be useful for various purposes. Also, the total number of license plate images for character recognition application is 27,745 images.

Mahdi Rahmani, AmirHossein Saffari, Reyhane Rahmani

Small and medium-sized enterprises (SMEs) in Iran increasingly leverage Telegram for sales, where real-time engagement is essential for conversion. However, developing AI-driven chatbots for this purpose requires large, high-quality question-and-answer (Q&A) datasets, which are typically expensive and resource-intensive to produce, especially for low-resource languages like Persian. In this paper, we introduce MegaChat, the first fully synthetic Persian Q&A dataset designed to evaluate intelligent sales chatbots in Telegram-based e-commerce. We propose a novel, automated multi-agent architecture that generates persona-aware Q&A pairs by collecting data from active Telegram shopping channels. The system employs specialized agents for question generation, validation, and refinement, ensuring the production of realistic and diverse conversational data. To evaluate answer generation, we compare three classic retrieval-augmented generation (RAG) models with our advanced agentic system, which features multi-query retrieval, reranking, and persona-aligned response synthesis. Using GPT-5.1 for evaluation across six quality dimensions, our results show that the agentic architecture outperformed traditional RAG models in 4 out of 5 diverse channels, demonstrating its ability to generate scalable, high-quality datasets without relying on expensive human annotation or complex fine-tuning. MegaChat provides SMEs with an efficient, cost-effective solution for building intelligent customer engagement systems in specialized commercial domains, enabling advancements in multilingual conversational AI for low-resource languages. Download: https://github.com/MegaChat-Tech/MegaChat-DataSet