Safe Turn: Intelligent U-turn Collision Avoidance System Using Arduino

• Author(s):

  Shinde H. Rajendra

• Keywords:

  U-turn Safety, Collision Avoidance, Arduino Microcontroller, Intelligent Transportation System (ITS), Sensor Fusion, Ultrasonic Detection, Vehicle-to-Infrastructure (V2I) Communication, Real-time Monitoring, Embedded Control, Traffic Management.

• Abstract:

  Road Safety At U-turn Intersections Remains A Critical Challenge In Modern Traffic Management Systems, Primarily Due To Poor Visibility, Human Error, And Inadequate Decision Support During Vehicular Maneuvers. Conventional U-turn Designs Often Lack Intelligent Monitoring Or Warning Systems, Resulting In A High Frequency Of Collisions, Particularly In Congested Urban Environments. To Address These Shortcomings, This Paper Presents “Safe Turn” — An Intelligent U-turn Collision Avoidance System Based On Arduino Microcontroller Technology. The Proposed System Integrates Multiple Sensors And Embedded Control Logic To Detect Oncoming Traffic, Evaluate Real-time Safety Conditions, And Alert Drivers Before Executing A U-turn Maneuver. The System Employs A Network Of Ultrasonic Sensors, Infrared (IR) Proximity Detectors, And Light Detection And Ranging (LiDAR) Modules Strategically Positioned Along The U-turn Curve To Monitor Both Approaching And Crossing Vehicles. These Sensors Continuously Transmit Data To An Arduino Uno Controller, Which Processes Environmental Inputs Through A Decision-making Algorithm Optimized For Low-latency Response. Upon Detecting Potential Collision Risks, The System Triggers Visual Indicators (LED Arrays) And Auditory Alerts (buzzers) To Warn Drivers. The Embedded Software Algorithm Dynamically Adjusts Sensitivity Thresholds Based On Real-time Traffic Flow And Vehicle Speed, Thereby Reducing False Positives And Improving Accuracy Under Diverse Environmental Conditions. Extensive Simulation And Prototype Testing Were Conducted Under Varied Traffic Scenarios Using MATLAB–Simulink And Arduino IDE Environments. Results Demonstrate That The Proposed System Effectively Predicts Collision Trajectories And Issues Early Warnings Within 0.6 Seconds Of Risk Detection, Achieving A Collision Prediction Accuracy Of 95.4%. The Low Power Consumption And Modular Design Also Ensure Compatibility With Existing Roadside Infrastructure And Smart City Frameworks. Furthermore, The System’s Scalability Allows Integration With IoT-based Traffic Management Systems And Vehicle-to-infrastructure (V2I) Communication Platforms, Enabling Future Autonomous And Semi-autonomous Traffic Coordination.

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Other Details

• Paper id:

  IJSARTV11I11104372

• Published in:

  Volume: 11 Issue: 11 November 2025

• Publication Date:

  2025-11-27

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