Makoto Aso

Automated steering control for the intelligent multimode transit system

We develop the intelligent multimode transit system (IMTS), as a new transit system, which uses platooning of automated driving buses. This paper describes the steering control of an automated bus for IMTS. It is confirmed in the experiments that the feedforward controller and the linear quadratic integral feedback controller designed have good performance of lane following both on a curve and a straight course. The feedforward steering angle and the desired states of the feedback controller are calculated by using road geometry, vehicle characteristics and velocity, assuming steady state turning of the vehicle. The road geometry map used by the vehicle is constructed according to the design of the road. The state variables for feedback are estimated by means of a hybrid Kalman filter. The hybrid Kalman filter provides accurate estimation at low speed even when using discrete reference markers for the measurement of vehicles lateral deviation.

Lateral State Prediction for Automated Steering using Reliability-Weighted Measurements from Multiple Sensors

This paper presents a time-varying Kalman filter that combines the measurements from four different lateral deviation sensors to produce a reliable prediction of lateral vehicle dynamics state for automated steering. The measurement noises used in the filter vary continuously according to a reliability index, which itself is a function of individual sensor capabilities and the current driving environment. The reliability index is continuously varying and differs from previous work that simply changes the source of the lateral deviation measurement for the filter according to the reliability index. The practicality of the prediction method is shown through the implementation on a test vehicle.