Smart EV Safety and Alert System

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@article{1716447,
      author = {I. Revathi, T. Naga Sai Saran, T. Sri Lakshmi, R. Lakshmi Sailaja, G. Sundar Raj},
      title = {Smart EV Safety and Alert System},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {1455-1463},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716447.pdf},
      abstract = {This paper presents the design and implementation of a Smart EV Safety and Alert System — a working prototype built around a dual-microcontroller architecture using an ESP32-CAM and Arduino Uno. The system demonstrates three key capabilities validated on a physical prototype: (1) AI-based object detection using YOLOv8n on a connected laptop that automatically stops the car when a person is detected and slows it when a bus is detected, achieving 180–220 ms response latency; (2) crash and flip detection via an MPU6050 IMU that halts all motors immediately on impact or rollover; and (3) a web-based manual control dashboard that allows full directional control and emergency stop from any browser on the same Wi-Fi network. The motors are demonstrated running and stopping automatically in response to both sensor triggers and web commands, confirming real-time hardware responsiveness. Battery temperature monitoring via DS18B20 sensors and spoken voice alerts via DFPlayer Mini provide additional safety layers. Lane detection using OpenCV is identified as a near-term upgrade. Testing on the working prototype confirmed zero false positives during normal operation. The system is built entirely from open-source tools and components costing under ₹4,000, demonstrating a cost-effective architecture for intelligent vehicle safety systems.},
      keywords = {ESP32-CAM; YOLOv8n; Object Detection; Crash Detection; MPU6050; Smart EV; Web Control; Lane Detection; OpenCV; Arduino Uno},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716447}
  }