Autonomous driving requires efficient and safe decision making and motion planning in dynamic and uncertain environments. Future movement of surrounding vehicles is often difficult to represent. Besides, most existing studies consider decision making and planning/control separately. Both them may lead to the oscillation and unsafe for autonomous driving. This paper proposes an integrated framework of decision making and motion planning with oscillation-free capability. The proposed approach overcomes the shortcomings of autonomous driving for lane change/keeping maneuvers and is able to: i) make oscillation-free behavior decisions given biased prediction; ii) cut through in the traffic efficiently and safely when being in squeezed; iii) accelerate computation efficiency by building a state transfer model based on prediction uncertainty; iv) reduce the dissonance between decision-making and motion planning. A belief decision planner is designed with the uncertainty of the prediction trajectories. Lateral and longitudinal drivable corridors including the reference state and the related boundary constraints are built, which provide better suited information for planning to solve the optimal motion sequence more quickly and stably, and improve its consistency with decision module. Finally, the problem is formulated as an optimal control problem considering the vehicle dynamics and some soft constraints and the motion trajectory is solved by OSQP. Simulation and experimental tests are implemented to evaluate the feasibility and effectiveness of the proposed approach. Test results show that the integrated approach can make proper, safe and continuous decision and planning for autonomous vehicles and the calculation time is very low.

The tire-road peak adhesion coefficient (TRPAC) describes the tire adhesion limit that a road can provide. The TRPAC is a key parameter for precise vehicle motion control and an important basis for decision-making and planning of intelligent vehicles. Considering the critical and difficult problems in the estimation of TRPAC, such as slow convergence and low accuracy, a TRPAC estimation method based on the fusion of vehicle dynamics and machine vision is proposed in this paper. Based on the observability theory of nonlinear systems, local weak observability of the dynamics-based estimator is analyzed to explain the limitation of a single dynamics-based estimator. The framework of dynamics-image-based fusion estimator is then proposed, including the fusion of data, model and decision levels. A dynamics-based fusion estimator is designed by considering the coupling relationship of longitudinal and lateral tire forces to adapt the conditions of complex excitations. Start-and-stop strategy for the dynamics-based fusion estimator is designed by setting excitation thresholds for different types of road surfaces, which are identified using vision information. Parameter self-tuning for the dynamics-based fusion estimator based on the image-based estimator is proposed to improve convergence speed and reduce oscillation. The results of the simulation and vehicle test show that the road estimation error of the proposed method is within 0.03 and the convergence time is within 0.5 s. Compared with other existing estimators, the fusion estimator achieved better accuracy, sensitivity and stability, particularly when complex excitations were present.

In the automotive field, electro-hydraulic brake systems (EHB) has been developed to take place of the vacuum booster, having the advantage of faster pressure built-up and continuously pressure regulation. Pressure control, one of the most crucial issues to be solved for EHB, is influenced by the system nonlinearities (e.g., pressure-position, friction). Recently, there are a series of studies that focus on this issue. However, the pressure control based on pressure estimation is rarely investigated in the previous literatures. In order to achieve cost-effective and highly integrated, the pressure is regulated without any add-in sensors, making it very difficult to achieve a satisfactory performance. A pressure control system based on pressure joint estimation is developed in this article according to a requirement-oriented model. The pressure joint estimation is designed considering actuator characteristics and vehicle dynamics. The wheel speed feedback is used for modification via a proportional-integral observer. A pressure control combining feedforward, torque-adaptive friction compensation with feedback controller is employed to improve the pressure-tracking performance. The constraint of feedback gain of controller is derived based on the small-gain theorem to remain the input–output stability of the overall control system. Superior to the estimation method based on actuator characteristics, the joint estimation method improves the accuracy by more than 10%. The pressure-tracking performance and robustness of system are validated by vehicle experiments under the typical common braking situations.