A Remote Control Strategy for an Autonomous Vehicle with Slow Sensor Using Kalman Filtering and Dual-Rate Control.

Area of Science:

• Robotics and Control Systems
• Networked Autonomous Systems
• State Estimation and Prediction

Background:

• Autonomous vehicles (AVs) require robust remote control systems.
• Network-induced delays and packet disorder challenge real-time control.
• Slow sensing devices, like cameras, necessitate advanced control strategies.

Purpose of the Study:

• To develop a novel remote control solution for AVs addressing network-induced challenges.
• To maintain nominal dynamic control performance despite communication impairments.
• To enhance the reliability and efficiency of AV remote operation.

Main Methods:

• Utilized an Extended Kalman Filter (EKF) for state estimation and noise reduction.
• Implemented an h-step-ahead state prediction within the EKF to manage network delays.
• Employed a dual-rate control framework to handle slow sensors and conserve network bandwidth.
• Applied a packet-based control strategy and pure pursuit for path tracking.

Main Results:

• The EKF effectively fused sensor data, reducing noise and estimating the AV state.
• The h-step prediction and packet-based control successfully mitigated network-induced delays.
• The dual-rate controller achieved desired dynamic performance with slow camera measurements.
• The proposed system demonstrated reliable path tracking for the AV under adverse network conditions.

Conclusions:

• The novel remote control solution is effective for AVs operating over unreliable networks.
• The integration of EKF, state prediction, and dual-rate control addresses key challenges in networked AV systems.
• The system maintains nominal control performance, enabling robust path tracking despite communication limitations.