Poster Abstract: State Estimation and Sensor Fusion for Autonomous Driving in Mixed-Traffic Urban Environments

This poster investigates sensory data processing, filtering and sensor fusion methods for autonomous vehicles operating in real-life, urban environments with human and machine drivers, and pedestrians. Extended Kalman Filters were used to develop decentralized data fusion algorithms for communicating vehicles, Particle Filters were improved by assigning trust/confidence values in order to overcome faulty/compromised sensors, and the computational cost of particle filters were distributed by parallelizing the load using the developed Shared-Memory Systematic Resampling algorithm.