Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise

Intelligent driving system requires more precise and reliable steering control, and steering-by-wire (SBW) technology can better adapt to the development of autonomous driving. The permanent magnet synchronous motor (PMSM) servo system is the actuator of the SBW. Aiming at the uncertainties of the PMSM servo system, a new adaptive control method for the PMSM servo system is proposed. Parameter adaptive laws are designed for each slowly varying parameter and unknown parameter, and robust terms are introduced to the adaptive laws for the parameter uncertainties. The convergence speed of the parameter adaptive law proposed can be improved by adjusting the parameters related to the convergence rate. To reduce the scope of disturbance uncertainties, the steering load disturbances are divided into a bounded unknown time-varying part and a predictable time-varying disturbance. Based on the Luenberger observer, the prediction term of the load torque is estimated by the current of PMSM in real time. By introducing boundary estimation and robust term of unknown disturbance into the control law and parameter adaptive law, respectively, the uncertainties of parameters and unknown external disturbance can be compensated. Simulation and hardware-in-the-loop tests are carried out on the SBW platform. Experiments show that the control strategy of the PMSM servo system designed in this article has good tracking accuracy and stability.

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.

In intelligent vehicle cooperative systems, the mismatch in driving characteristics between a human and a machine and the driver misoperation caused by this mismatch result in human–machine conflicts, which significantly affect driving safety. Therefore, an intelligent vehicle human–machine cooperative steering torque control method is proposed herein. To adapt the intelligent system to the varying previewing characteristics of a human, a time-varying previewing driver model is constructed, and a penalty factor for human–machine intervention is designed based on fuzzy rules to assign driving control rights by assessing the driver’s state. Consequently, a human–vehicle–road model with driver preview time and penalty factor as varying parameters is established. Based on gain-scheduling control, a human–machine cooperative steering torque controller is designed to adapt to the varying previewing characteristics of a human and the change in human–machine intervention. The stability and robustness of the entire parameter space are guaranteed by constraining the poles in a certain region. Finally, the proposed human–machine cooperative control scheme demonstrates the effective alleviation of conflicts between the driver and the intelligent driving system.