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Intelligent Brake System Vigilance: Real-time Diagnostics for Enhanced Automotive Safety
Issue Vol. 3 No. 01 (2026): Volume 03 Issue 01 --- Section Articles
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Abstract
Vehicle safety is profoundly impacted by the condition of its braking system, with worn brake pads being a significant contributor to road accidents. Traditional brake monitoring methods often fall short, providing alerts only at advanced stages of wear, thereby increasing accident risk and maintenance inefficiencies. This article proposes an intelligent brake system vigilance platform designed to enhance automotive safety through real-time monitoring and predictive diagnostics of brake pad health. The system integrates multiple sensor technologies, including wear, temperature, vibration, and pressure sensors, to collect comprehensive data. Leveraging IoT for low-latency data transmission and advanced machine learning algorithms, particularly CNN-LSTM networks, the platform performs real-time wear estimation, anomaly detection, and predictive modeling of remaining useful life (RUL) for brake pads. This proactive approach facilitates early warnings to drivers and maintenance personnel, enabling timely interventions that mitigate accident risks, optimize maintenance schedules, and reduce operational costs. Furthermore, the extensive data collected will provide invaluable insights for future automotive design and material science advancements. This intelligent system represents a significant step towards a safer, more efficient, and data-driven automotive future.
Keywords
References
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