Data Filtering Method for Intelligent Vehicle Shared Autonomy Based on a Dynamic Time Warping Algorithm

Abstract

Big data already covers intelligent vehicles and is driving the autonomous driving industry’s transformation. However, the large amounts of driving data generated will result in complex issues and a huge workload for the test and verification processes of an autonomous driving system. Only effective and precise data extraction and recording aimed at the challenges of low efficiency, poor quality, and a long-time limit for traditional data acquisition can substantially reduce the algorithm development cycle. Based on the premise of driver-dominated vehicle movement, the virtual decision-making of autonomous driving systems under the accompanying state was considered as a reference. Based on a dynamic time warping algorithm and forming a data filtering approach under a dynamic time window, an automatic trigger recording control model for human-vehicle difference feature data was suggested. In this method, the data dimension was minimized, and the efficiency of the data mining was improved. The experimental findings showed that the suggested model decreased recorded invalid data by 75.35% on average and saved about 2.65 TB of data storage space per hour. Compared with industrial-grade methods, it saves an average of 307 GB of storage space per hour.

Application of Dynamic Time Warping to Determine the Shear Wave Velocity from the Down-Hole Test

“…Some other applications of DTW for pattern recognition include the recognition of gestures from inertial data [24][25][26], sign language [27], human movement from movies [28][29][30], as well as applications in medicine, such as the classification of heart sounds [31,32] or ECG arrhythmias [33,34]. In the case of machines, DTW can be used in the context of diagnostics of, for example, motor drives [35] or location from inertial data [36], as well as data reduction generated in autonomous driving systems [37]. The DTW method can also be extended to more dimensions.…”