Kidnapped Vehicle Using Particle Filters-Self-Driving Car Nanodegree
Sunil Dhull, Payal Thakur, Yashwant Singh Sangwan, Navjot Singh Talwandi, Rajender Kumar, Surender Kumar · 2024
Detecting and tracking stolen vehicles is an important task with self-driving cars. This study focuses on the use of particle filters to detect and track stolen vehicles. Particle filters are a probabilistic estimation method that can effectively handle the uncertainties and nonlinearities associated with vehicle tracking. The study examines the application of particle filters in the Nanodegree project to self-driving cars, specifically for the detection and tracking of stolen vehicles. The results demonstrate the efficiency of particle filters to accurately estimate the location and routes of hijacked vehicles, enabling effective recovery operations Vehicle occupied scenarios refer to situations in which a self-driving vehicle loses its location awareness The vehicle can measure its position observations based on a sensor. Using small- scale filters, this research aims to improve the ability of self- driving vehicles to reconstruct reality, accurately restoring its position in cases where they are unpredictable, ultimately contributing to the robustness and reliability of autonomous vehicle systems.