Neural Network Based Kalman Filter Design for the Vehicle Lateral Maneuver

Precise state estimation during a lateral maneuver is not just a theoretical concept but a practical necessity. The performance of the Kalman filter is directly impacted by the comprehensive research and innovative approaches to counter nonlinearity and uncertainty. The use of machine learning in control theory is one such development that has significantly enhanced the effectiveness of our work. This paper provides an enhanced adaptive Kalman filter architecture with a neural network for a rapid obstacle avoidance maneuver. The proposed design exemplifies not just its effectiveness in terms of better state estimation in the presence of complex nonlinear vehicle dynamics and disturbances but also its potential downsides sometimes. Simulation results verify the same by ensuring a significant improvement to the traditional design, demonstrating better accuracy and the need for such advances in vehicle dynamics and control.