Collision avoidance and stabilisation are two of the most crucial concerns when an autonomous vehicle finds itself in emergency situations, which usually occur in a short time horizon and require large actuator inputs, together with highly nonlinear tyre cornering response. In order to avoid collision while stabilising autonomous vehicle under dynamic driving situations at handling limits, this paper proposes a novel emergency steering control strategy based on hierarchical control architecture consisting of decision-making layer and motion control layer. In decision-making layer, a dynamic threat assessment model continuously evaluates the risk associated with collision and destabilisation, and a path planner based on kinematics and dynamics of vehicle system determines a collision-free path when it suddenly encounters emergency scenarios. In motion control layer, a lateral motion controller considering nonlinearity of tyre cornering response and unknown external disturbance is designed using tyre lateral force estimation-based backstepping sliding-mode control to track a collision-free path, and to ensure the robustness and stability of the closed-loop system. Both simulation and experiment results show that the proposed control scheme can effectively perform an emergency collision avoidance manoeuvre while maintaining the stability of autonomous vehicle in different running conditions.

The challenging issue of "human-machine co-pilot" opens up a new frontier to enhance driving safety. However, driver-machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering system, which degrades the system's path-tracking ability and lowers driving safety. This paper proposes a novel stochastic game-based shared control framework to model steering torque interaction between driver and intelligent electric power steering (IEPS) system. A six-order driver-vehicle dynamic system including driver/external uncertainty is established for path-tracking. Then the affine-linear quadratic (LQ)-based path-tracking problem is proposed to model the maneuvers of driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming (SDP). Two cases of co-pilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between stochastic Nash and Stackelberg strategies are investigated based on the results. And the steering-in-the-loop (SIL) experiment reveals the potential of applying the proposed shared control framework to handle driver-IEPS conflicts and uncertain driver/external turbulence. Finally, the co-piloting experiments with human driver further demonstrate the rationality of the game-based pattern between both agents.

Stability as well as robustness are the major concerns in the design of trajectory tracking controller for autonomous vehicle. In this paper, a novel lateral stability controller design for vehicle path tracking is developed. Firstly, using dynamic game theory as a general framework, vehicle lateral stability can be viewed as a dynamic difference game so that its two players, namely, the active front steering (AFS) system and active rear steering (ARS) system can work together to provide more stability for vehicle path tracking control. The interactive steering control strategies between AFS and ARS are obtained by non-cooperative closed-loop feedback Stackelberg game theory to ensure optimal performance for vehicle path tracking. Then based on the proposed path-following shared control paradigm, by applying the method of zero-sum game theory, a finite-time robust regulator is developed to make the interaction model more robust to uncertain lateral disturbances. Finally, double lane change and serpentine driving condition with and without uncertain time-varying lateral disturbance are used to evaluate the proposed control algorithm. Simulation and hardware-in-loop (HIL) implementation results show that the proposed shared control paradigm-based robust path-tracking controller can robustly provide better lateral stability when time-varying lateral disturbances are bounded.

A new methodology to design the vehicle GCC (global chassis control) nonlinear controller is developed in this paper. Firstly, to handle the nonlinear coupling between sprung and unsprung masses, the vehicle is treated as a mechanical system of two-rigid-bodies which has 6 DOF (degree of freedom), including longitudinal, lateral, yaw, vertical, roll and pitch dynamics. The system equation is built in the yaw frame based on Lagrange's method, and it has been proved that the derived system remains the important physical properties of the general mechanical system. Then the GCC design problem is formulated as the trajectory tracking problem for a cascade system, with a Lagrange's system interconnecting with a linear system. The nonlinear robust control design problem of this cascade interconnected system is divided into two H _∞ control problems with respect to the two sub-systems. The parameter uncertainties in the system are tackled by adaptive theory, while the external uncertainties and disturbances are dealt with the H _∞ control theory. And the passivity of the mechanical system is applied to construct the solution of nonlinear H _∞ control problem. Finally, the effectiveness of the proposed controller is validated by simulation results even during the emergency manoeuvre.

Road adhesion coefficient is an important parameter in vehicle active safety control system. Many researchers estimate road adhesion coefficient by total tire self-aligning torque (SAT, also called front-axle aligning torque), which obtains the average road adhesion coefficient of front wheels, thus leading large estimation error. In this paper, a novel estimation of road adhesion coefficient based on single tire SAT, which is obtained by tire aligning torque distribution, is brought forward. Due to the use of SAT, the proposed estimation method is available in steering only condition. The main idea of the proposed method is that road adhesion coefficient is estimated by single tire SAT instead of total tire SAT. The single tire SAT is closer to real tire torque state, and it can be obtained by aligning torque distribution, which makes use of the ratio for the aligning torque of front-left wheel and front-right wheel. Tire sideslip angle used in torque distribution is estimated by unscented Kalman filter (UKF). Two coefficients, including front-left and front-right tire-road friction coefficients, are estimated by iteration algorithm form single tire SAT. The final road adhesion coefficient is determined by a coefficient identification rule, which is designed to determine which tire-road friction coefficient as the final road adhesion coefficient. Both simulations and tests that use gyroscope/lateral accelerometer/global position system (GPS)/strain gauge are conducted, to validate the proposed methodology that can provide accurate road adhesion coefficient to vehicle active safety control.

Driver-machine shared control scheme opens up a new frontier for the design of driver assistance system, especially for improving active safety in emergency scenario. However, the driver's stress response to steering assistance and strong tire nonlinearity are main challenges suffered by controller designing for collision avoidance. These unfavorable factors are particularly pronounced during emergency steering maneuvers and sharply degrade shared control performance. This paper proposes a fuzzy-linear quadratic regulator (LQR) game-based control scheme to simultaneously enhance vehicle stability while compensating driver's inappropriate steering reaction in emergency avoidance. A piecewise linear-based Takagi–Sugeno (T–S) fuzzy structure is presented to mimic driver's knowledge about vehicle lateral nonlinearity, and the control authority is shared between driver and emergency steering assistance (ESA) system through steer-by-wire (SBW) assembly. An identical piecewise internal model is chosen for ESA and the shared lane-keeping problem is modeled as a fuzzy linear quadratic (LQ) problem, where the symmetrical fuzzy structure further enhances vehicle's ability to handle extreme driving conditions. In particular, the feedback Stackelberg equilibrium solutions of the fuzzy-LQ problem are derived to describe the interactive steering behavior of both agents, which enables the ESA to compensate driver's irrational steering reaction. Hardware-in-the-loop (HIL) experiment demonstrates the ESA's capability in compensating driver's aggressive steering behavior, as well as the copiloting system's excellent tracking and stabilizing performance in emergency collision avoidance.