• Ashiwini Biradar
• Aayush Ainchwar
• Rohan Chaudhari
• Karan Dhole
• Yajas Gandhi

In the realm of automotive safety and Advanced Driver Assistance Systems (ADAS), the integration of auto- braking technology with maneuver prediction holds significant promise for enhancing safety on the roads. This paper explores the convergence of various detection systems including vehicle, velocity, position, and heading angle detection, to develop a robust framework for predicting potential maneuvers of surrounding vehicles. Leveraging a meticulously curated dataset, our research focuses on creating a neural model capable of real-time analysis and decision-making based on the collected data. By incorporating predictive algorithms, our proposed system aims to preemptively identify potential collision risks and trigger auto-braking mechanisms, thereby mitigating the likelihood of accidents and enhancing overall road safety. Through a comprehensive examination of detection mechanisms and neural network architectures, this paper contributes to the advancement of ADAS technologies, paving the way for more intelligent and proactive safety systems in modern vehicles.

Auto-braking, Maneuver prediction, Advanced Driver Assistance Systems (ADAS), Deep learning, Artificial Neural Networks (ANN), Haar cascading, Object tracking, Lane detection, Real-time dataset, Vehicle detection, Velocity detection, Position detection, Heading angle detection, Collision avoidance, Proactive safety measures, Road safety, Neural model, Vehicle trajectory prediction

"Auto Braking Using Manevuver Prediction To Improve Safety And ADAS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.i631-i636, March-2024, Available :http://www.jetir.org/papers/JETIR2403881.pdf

"Auto Braking Using Manevuver Prediction To Improve Safety And ADAS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppi631-i636, March-2024, Available at : http://www.jetir.org/papers/JETIR2403881.pdf