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Road accidents are one of the global safety concerns leading to loss of millions of lives every year. One of the factors leading to this is over-steering. Oversteering is a phenomenon that occurs when the rear wheels of the vehicle lose grip, which causes the vehicle to turn more than expected. The detection of oversteering in real-time is crucial for the improvement of vehicle safety to prevent accidents as well as for advanced driving assistance systems (ADAS). This paper presents a holistic approach to over-steering detection using a decision tree algorithm. The proposed system analyzes various vehicle dynamics parameters such as lateral acceleration, yaw rate, and steering angle to identify the patterns that cause over-steering. The system incorporates collection of real-time data from Inertial Measurement Unit (IMU) sensors that enhances the reliability of oversteering detection under various conditions. The model is trained from the data obtained using the decision tree algorithm and achieved an accuracy of 96.08%. The hardware implementation is done by placing ESP-32 integrated with MPU 6050 and Arduino Nano 33 BLE Sense accordingly in the vehicle. Based on thresholds of the parameters mentioned in the paper, oversteering is detected.

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Real-Time Over-Steering Detection of Vehicle Using Machine Learning and Embedded System Integration

  • 会议论文

  • 发表日期：

    2026年

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  • Devika Vijapur,
  • Nidhi Desai,
  • Aishwarya Naik,
  • Smita Ganur,
  • Supriya Katwe

摘要

Road accidents are one of the global safety concerns leading to loss of millions of lives every year. One of the factors leading to this is over-steering. Oversteering is a phenomenon that occurs when the rear wheels of the vehicle lose grip, which causes the vehicle to turn more than expected. The detection of oversteering in real-time is crucial for the improvement of vehicle safety to prevent accidents as well as for advanced driving assistance systems (ADAS). This paper presents a holistic approach to over-steering detection using a decision tree algorithm. The proposed system analyzes various vehicle dynamics parameters such as lateral acceleration, yaw rate, and steering angle to identify the patterns that cause over-steering. The system incorporates collection of real-time data from Inertial Measurement Unit (IMU) sensors that enhances the reliability of oversteering detection under various conditions. The model is trained from the data obtained using the decision tree algorithm and achieved an accuracy of 96.08%. The hardware implementation is done by placing ESP-32 integrated with MPU 6050 and Arduino Nano 33 BLE Sense accordingly in the vehicle. Based on thresholds of the parameters mentioned in the paper, oversteering is detected.