VEHIQL-AI: An Intelligent Automotive Marketplace Integrating Visual Vehicle Recognition and AI-Powered Calling Agent Assistance

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Prof. Nita Ingale
Chetan Harisagar Gupta
Siddhesh Kishor Gawade
Sneha Ashish Dubey
Sahil Subhash Mandavkar

This research introduces an AI-driven automotive marketplace platform designed to enhance the vehicle buying and selling ecosystem through intelligent automation and data-driven decision-making. The system enables image-based vehicle search using computer vision models capable of extracting vehicle attributes such as make, model, variant, and features directly from user-uploaded images. The platform integrates an AI-powered conversational calling agent that analyzes customer intent, budget, and usage patterns to provide personalized vehicle recommendations and automated test drive booking.


Experimental evaluation demonstrates that the proposed system achieves 93.4% vehicle recognition accuracy, an R² score of 0.87 for price prediction, and an average recommendation response time below 2.1 seconds, improving decision-making efficiency compared to traditional automotive marketplace platforms.

VEHIQL-AI: An Intelligent Automotive Marketplace Integrating Visual Vehicle Recognition and AI-Powered Calling Agent Assistance. (2026). International Journal of Latest Technology in Engineering Management & Applied Science, 15(2), 762-776. https://doi.org/10.51583/IJLTEMAS.2026.15020000066

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References

X. Ni and H. Huttunen, “Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification,” Journal of Signal Processing Systems, vol. 93, pp. 357–368, Apr. 2021.

A. Amirkhani and A. H. Barshooi, “DeepCar 5.0: Vehicle Make and Model Recognition Under Challenging Conditions,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 541–553, Jan. 2023.

M. Manzoor and Y. Morgan, “Vehicle Make and Model Recognition Using Random Forest Classification for Intelligent Transportation Systems,” 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, USA, pp. 148–154, 2018.

S. Lee, R. Ratan, and T. Park, “The Voice Makes the Car: Enhancing Autonomous Vehicle Perceptions and Adoption Intention through Voice Agent Gender and Style,” Multimodal Technologies and Interaction, vol. 3, no. 1, art. 20, 2019.

X. Liu and Z. Liao, “Emotional Experience Design Strategy for In-Vehicle Intelligent Voice Assistant,” Frontiers in Social Science and Technology, vol. 6, no. 5, 2024.

J. Li, H. Cheng, and H. Guo, “Survey on Artificial Intelligence for Vehicles,” Automotive Innovation, vol. 1, pp. 2–14, Jan. 2018.

A. Qian, A. A. Musa, M. Biswas, Y. Guo, W. Liao, and W. Yu, “Survey of Artificial Intelligence Model Marketplace,” Future Internet, vol. 17, no. 1, art. 35, 2025.

V. Mandala and S. N. R. D. Surabhi, “Integration of AI-Driven Predictive Analytics into Connected Car Platforms,” International Advanced Research Journal in Science, Engineering and Technology (IARJSET), vol. 7, no. 12, Dec. 2020.

J. Lin, “Predicting Used Car Price Based on Machine Learning,” Proceedings of the 1st International Conference on E-Commerce and Artificial Intelligence (ECAI’24), vol. 1, pp. 553– 560, 2024.

I. Fayyaz, G. G. M. N. Ali, and S. S. Khairunnesa, “Advanced Feature Engineering and Machine Learning Techniques for High Accurate Price Prediction of Heterogeneous Pre-Own Cars,” Vehicles, vol. 7, no. 3, art. 94, 2025.

M. Z. Ali and S. Patel, “AI-Based Vehicle Valuation Using Predictive Modelling and Market Data Analytics,” IEEE Access, vol. 11, pp

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VEHIQL-AI: An Intelligent Automotive Marketplace Integrating Visual Vehicle Recognition and AI-Powered Calling Agent Assistance. (2026). International Journal of Latest Technology in Engineering Management & Applied Science, 15(2), 762-776. https://doi.org/10.51583/IJLTEMAS.2026.15020000066