A Software-Based Approach for Detecting Tire Blowouts Through Inertial Measurement Anomalies in Autonomous Vehicles with Machine Learning

A prominent issue among ground vehicles is tire blowout or other catastrophic tire failure, which occurs when at least one of the vehicle's tires rapidly deflates or detaches completely. This can cause a driver to lose control of the vehicle's steering which can lead to dangerous, potentially fatal accidents. These incidents can become even more perilous when tire failures occur on vehicles without a human driver behind the wheel, specifically in autonomous vehicles. Therefore, we propose a method for detecting tire failures by analyzing data from the inertial measurement unit (IMU) for anomalies in accelerometer and gyroscope values, which signify such incidents. To test our method's accuracy, we simulate tire failure in highway conditions by physically detaching a wheel on our RACECAR, a 1/14 scale model autonomous car, while it performs line following on a curved path. We develop and compare a threshold-based algorithm, a Z-score-based algorithm, a convolutional neural network (CNN), and a recurrent neural network (RNN) to predict when blowouts occur during our trials, the latter three of which validate the use of IMUs in the detection of tire failures.