Tire-Road Friction Coefficient Estimation and Control of Autonomous Vehicles

This thesis aims to design and develop trajectory planning and tracking strategies integrating with the estimated tire-road friction coefficient (TRFC) so as to enhance the safety and reliability of the autonomous vehicles (AVs) under varying road friction conditions. A two-stage hierarchical framework is firstly developed for estimating TRFC in a computationally efficient manner considering vehicle’s lateral dynamic responses to double (DLC) and single (SLC) lane-change maneuvers. An alternate two-stage TRFC estimation framework is developed further on the basis of the longitudinal dynamics of the vehicle. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure needed for reliable estimation of TRFC with minimal interference with the vehicle motion. In the second stage, a constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the precise TRFC for achieving rapid convergence and enhanced estimation accuracy. A trajectory planning scheme integrating the estimated TRFC is subsequently developed for path-change maneuvers considering both the maneuver safety and the occupant’s comfort. For this purpose, a 7th-order polynomial function is constructed to ensure continuity up to the derivative of the acceleration (jerk). The friction-adaptive acceleration and speed-adaptive jerk limits are further defined and integrated in the framework to enhance occupant’s comfort and acceptance. Both numerical simulation and Quanser self-driving car (QCar) experimental results have revealed the effectiveness and practicability of the proposed lane change trajectory planning scheme. An adaptive model predictive control (MPC) tracking scheme is proposed for tracking the desired lane-change path considering wide variations in vehicle speed and TRFC. With integrated consideration of output weights in the cost function together with constraints on the magnitude of the outputs, the proposed MPC scheme required only lateral position for tracking the planned path. An interesting way of integrating adaptive control gains with consideration of steering saturation by using the backstepping technique is also designed for low-speed AVs to enhance trajectory tracking, while respecting to the input boundaries. The effectiveness of the proposed tracking control scheme is verified experimentally using the QCar test platform