Advanced Smart Helmet for Off-Road Biking: Enhancing Rider Safety with IoT-Based Features

Abstract

In this paper, we have mainly focused on designing an Advanced Smart Helmet for Off-Road Biking to enhance driver’s safety in difficult terrains, curved roads and highways. The helmet is equipped with RCWL sensors, vibration motors, ultrasonic sensors and GPS, which are used to detect vehicles coming from front and rear side and track real-time location of the driver respectively. The RCWL sensors are attached in front and ultrasonic sensors are attached at the back for dual confirmation for the driver. Helmet is attached with highly-efficient components which are being used to have a good detection process so that no mistake happens. Helmet is made by using an IoT-based safety system which ensures that the driver does not face any hectic situation on roads. The system mainly works on low power, has high reliability, lightweight and is made very comfortable to wear. The system promises that there will not be any delay in alerting the driver about the approaching vehicles and improving the overall protection of the driver for safe riding. To further strengthen the solidity of the helmet, it is equipped with a low-power and highly-efficient microcontroller for well-organized usage of power for longer working duration.

Introduction

The growing concern for road safety, particularly for two-wheeler riders, highlights risks such as accidents caused by reduced situational awareness and delayed emergency response. While helmets are mandated by regulations like India’s Motor Vehicles Act and reduce fatalities and severe injuries significantly, conventional helmets offer only physical protection and do not address blind spots or emergency communication needs.

To address these gaps, the project proposes an IoT-based smart helmet that enhances rider safety through proactive accident prevention and rapid emergency response. Key features include blind-spot detection, corner peeking, and real-time alerts, aiming to reduce collisions and provide immediate assistance during critical moments.

Literature Review: Previous smart helmet studies focused on alcohol detection, accident alerts via GSM/GPS, trauma measurement, ultrasonic and accelerometer sensors, and voice-assisted navigation, highlighting a trend toward integrating IoT and real-time communication for enhanced rider safety.

Proposed Methodology: The system targets motorcyclists on mountainous or curved roads. It combines forward detection via RCWL-0516 microwave radar sensors, rear proximity monitoring using HC-SR04 ultrasonic sensors, haptic feedback through helmet-mounted vibration motors, GPS-based real-time positioning, and a central ESP32 microcontroller for processing. All components are battery-powered for standalone operation.

Technologies Used:

• Microwave Radar Sensors: Detect forward motion even in low visibility.

• Ultrasonic Sensors: Monitor rear threats and alert riders to fast-approaching vehicles.

• Haptic Feedback Motors: Deliver non-visual, non-auditory alerts to maintain rider focus.

• GPS Module: Tracks location for potential future enhancements such as route logging or emergency response integration.

Conclusion

Ensuring the safety of two-wheeler commuters, particularly in hilly regions and busy city streets, is a significant issue because of reduced visibility, unexpected turns, and erratic vehicle behaviour. Prompt notifications can aid in avoiding accidents that are often caused by these conditions. Our innovative helmet project aimed to tackle these challenges by offering real- time alerts for vehicles and providing clear feedback to the rider. The system includes two rcwl-0516 microwave radar sensors mounted at the front of the helmet, which can detect vehicles approaching from blind turns or curved roads, like those encountered in ghat sections. When the sensors detect movement, they trigger the vibration motors located within the helmet. This tactile feedback immediately notifies the rider of an approaching vehicle, enabling them to react accordingly without taking their eyes off the road. The helmet incorporates two ultrasonic sensors placed at the rear to keep an eye on the surroundings. These devices measure the distance between the rider and vehicles approaching from behind. If a vehicle comes within a 200 cm distance, the rear vibration motor is activated, giving the rider an immediate warning. The feature is advantageous in areas with heavy traffic and a higher chance of unexpected overtaking or rear-end collisions. The ESP32 is responsible for controlling the helmet because of its exceptional performance, numerous connections, and energy efficiency. It receives input from all sensors and controls the functioning of vibration motors. Additionally, a modern GPS module is integrated into the device to consistently monitor the rider\'s whereabouts. Although the current implementation primarily focuses on detecting and alerting vehicles, this gps data can also be utilized for future enhancements such as automatic accident reporting and sharing location information with emergency services. The helmet is powered by a 3.7v lithium-ion battery, which is connected to a 5v boost converter. This setup ensures a stable voltage for all the components of the helmet, without adding much weight to it. This ensures usability without compromising on portability and convenience. Through rigorous testing in various real-world scenarios, such as curved roads and controlled traffic environments, it was proven that the system consistently delivers precise and prompt alerts.

References

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Copyright

Copyright © 2025 Madhuri Barhate, Rutuj Bhandari, Utkarsh Bhanse, Bhargav Dahake, Bhavesh Belhekar, Jayram Bedkute, Aaditya Bhand. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.