Time-Distributed Non-Convex Optimized Support Vector Machine for Vehicular Tracking Systems

Abstract

This article presents a non-convex optimized support vector machine (NCVX OSVM) algorithm for active steering stability of vehicles on a curved road. Initially, we considered a curved road geometrics formulation and designed a time-distributed (TD) model for NCVX OSVM to compute the steering angle 0°–180° at 10 m/s to follow active navigation at the highest curve entry speed. The proposed TD NCVX OSVM is interconnected with three modules. In the first module, formulated NCVX cost functions and Optimized SVM for smooth steering stability. The second module is based on improving faster training time (IFTT) by using the Naive Bayes probabilistic classifier (NBPC). The third module uses an optimized non-convex (NCVX) cost function to reduce the error phenomenon. The performance of these three modules is evaluated by several 100 data points from vehicle onboard sensors. Further, it is pre-processed in the curved road (start, continue, exit) conditions. The decisive of TD-NCVX OSVM design is demonstrated by using experimental learning on FPGA Zynq 7000 processor and programmed with python script. The empirical calculation shows an accuracy of 98.36%. Furthermore, the proposed design predicts an acceptable upper limit for curved steering whenever the vehicle turning speed is greater than 30 mi/h.