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Road accidents in mountainous and deep-curve road sections are primarily caused due to limited visibility, sharp turns, fog, and environmental hazards such as landslides. To address these safety concerns, this project introduces a smart, sensor-based pre-crash alert system designed to detect approaching vehicles, reduced visibility due to fog, and unstable ground conditions. Infrared sensors are used to monitor vehicle presence around blind curves, while visibility sensors detect fog density to warn drivers in advance. A vibration-based ADXL sensor is employed for identifying ground movement that may indicate landslides. The data from all sensors is processed using an Arduino-based microcontroller and the hazard information is communicated to nearby drivers using display units and wireless modules. Real-time alerts through LEDs, buzzers, or LCD messages enable timely action, thereby reducing collision risks. The system is cost-effective, energy efficient, and capable of being deployed in accident-prone ghat roads and hilly terrains. This integrated safety system significantly improves driver awareness and supports proactive accident prevention, ensuring safer transportation in hazardous road environments.
Keywords:
Pre-crash System; Deep Curves Safety; Landslide Alert; Fog Detection; Vehicle Collision Avoidance; IR Sensors; ADXL Sensor; Arduino-Based System
Cite Article:
"Pre-Crash System Including Landslide and Fog Alert for Vehicles at Deep Curves", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.b592-b598, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511168.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator