Long Range Path Prediction and Target Classification Algorithm using connected vehicle data and others

By | September 14, 2018

<p id=”p-0001″ num=”0000″>Self-driving and autonomous vehicles are very popular these days for scientific, technological, social, and economical reasons. In one aspect of this technology, path prediction and target classifications are main pillars and enablers for safety and mobility applications. Accurate Estimation of the lane assignment of the remote vehicle with respect to the host vehicle lane results in the accurate threat assessment. V2X and C-V2X are emerging technology for safety applications, and they cover relatively long range. Long range means more room for error in path prediction/target classification. Therefore, here, we offer an efficient path prediction and target classification algorithm to enable these technologies performing safety and mobility applications. The method fuses target vehicles data and host vehicle data in an efficient algorithm to perform this task. Other variations are also presented here.</p>

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