Real-Time State Estimation and Security in Autonomous Vehicles Using Particle Filters

Autonomous vehicles (A Vs) rely on complex software, extensive sensor networks, and communication systems, making them vulnerable to a range of cyber threats, including malware, GPS spoofing, and data manipulation. The objective of this paper is to improve the robustness and security of A V systems by leveraging particle filters to fuse data from various sensors, such as GPS, IM Us, and radars sensors, and to detect and mitigate potential security breaches. Particle filters are employed to continuously update the vehicle's state estimate by integrating noisy and potentially compromised sensor data. By effectively managing uncertainty and detecting anomalies in sensor data, particle filters provide resilience against spoofing attacks and system manipulations. Additionally, particle filters adapt to new data, providing resilience to changing conditions and helping detect anomalies that may indicate sensor malfunctions or security threats. By ensuring precise state estimation, particle filters enhance the vehicle's safety and performance, enabling it to navigate effectively and respond to its surroundings with greater accuracy.