Date of Publication :8th March 2025

Abstract: Traffic control on roads, especially in hill view areas, is crucial for preventing accidents and ensuring public safety. Traffic controllers play a vital role in managing vehicles on such challenging terrains, where conventional traffic rules, such as keeping left, are often inadequate due to sharp curves like hairpin bends. These bends limit visibility, making it difficult for drivers to anticipate oncoming vehicles. Traditional traffic signals, which indicate only stop, ready, and go, are ineffective in such scenarios. To address this issue, advanced technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and sensor-based automation can be integrated into intelligent traffic management systems. Massive Multiple-Input Multiple-Output (MIMO) technology, widely recognized for enhancing spectral efficiency and link reliability in 5G and future wireless communication networks, can also be leveraged for real-time data processing. However, implementing massive MIMO for uplink signal detection faces challenges in balancing computational load, parallel processing efficiency, and detection accuracy. The optimal Maximum Likelihood (ML) detection algorithm, though highl y accurate, becomes computationally expensive as the number of users increases. To overcome this, our system design incorporates digital sensor-based multi-input data collection, processed through parallel computing on a Very Large Scale Integration (VLSI) architecture. This approach enhances emergency traffic management at hairpin bends in hilly terrains and enables AI-driven vehicle parking systems in multi-apartment complexes, effectively reducing collisions and ensuring road safety through smart, automated decision-making.

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