Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

Abstract

In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle's movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state; and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications.

On the Application of Time Frequency Convolutional Neural Networks to Road Anomalies’ Identification with Accelerometers and Gyroscopes

“…We decided to place the sensor on the top of car’s dashboard in the middle of the car, because it is a common placement position for the IMU in the literature [ 12 ]. In the recent review by Menegazzo et al [ 4 ], the placement of the IMU on the top of car’s dashboard was the one mostly adopted in the literature. The results from [ 8 ] also showed that the placement on the dashboard provided the smallest classification error in comparison to the placement of the IMU in other locations on the vehicles (e.g., left or right side of the car).…”

“…The data collection was performed using the controlled positioning strategy. As described in [ 4 ], this strategy consists of a simple technique where the sensor is placed on the vehicle so that the axes in both reference frames coincide, i.e., the sensor axes are aligned with the vehicle axes, and it is not necessary to apply preprocessing for reorientation. This technique was used for both accelerometer and gyroscope data.…”

“…The loop includes 15 main road anomalies and 4 obstacles of two different types, which were selected for the data analysis. With reference to the classification of exteroceptions identified in [ 4 ], the road anomalies included potholes, cracks, transverse cracks, and patches, while the obstacles were of types rumble strips and speed humps/speed bumps. A picture of some examples of the speed bumps and the road anomalies (potholes in the road) are shown in Figure 3 with the related recordings with the accelerometer in the Z direction in Figure 4 .…”

“…This is also due to the decreasing costs of IMUs and their increasing use in the automotive sector either because they are inserted and used in the vehicle systems or because smartphones (which are equipped with IMUs) can be deployed and installed in vehicles. A very recent and detailed survey on the use of accelerometers and gyroscopes (as inertial sensing sources of information) was presented in [ 4 ] where an analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle’s movement was presented. The results of the survey show that a limited amount of the reviewed papers used time-frequency analysis, and no paper in the review used the combination of the time-frequency and convolutional neural networks, as proposed in this paper.…”

“…Detection of road anomalies can be performed using different techniques including machine learning algorithms [ 4 , 5 ], and in recent times, Deep Learning (DL) algorithms have been adopted with significant success. In many cases, DL is used to detect and identify road anomalies on the basis of the images collected by the camera installed on the vehicle [ 6 , 7 ].…”