Vehicle Surroundings Perception Using Micro‐electromechanical Systems Inertial Sensors

Sensors enable vehicles to perceive their surroundings even while parked. Camera‐based systems for vehicle surroundings perception are already available as of today. However, camera‐based systems result in excessive power consumption and data collection. In this article, a study on always‐on surroundings perception of parked vehicles is done using micro‐electromechanical systems (MEMS) inertial sensors as an alternative to camera‐based systems. The measurements of the MEMS inertial sensors are used to detect and classify 17 different types of events. These events include accidents, vandalism, rain, as well as normal use. A convolutional neural network (CNN) is used to classify the events. The data used to train the CNN is recorded using a test vehicle in a laboratory setting and on a test track. The CNN is quantized for deployment on a CNN hardware accelerator which is packaged with the sensor as a system‐in‐package (SiP). The novel sensor system achieves a classification accuracy of 88% at a total system power dissipation of 27 μW. The research article considers surroundings perception of parked vehicles using micro‐electromechanical systems (MEMS) inertial sensors. By employing a machine learning approach, different types of events such as accidents, vandalism, and rain can be detected with an accuracy of 88%. The power consumption of the whole system is only 27 μW.