The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h).

In this brief, a novel decoupling solution is proposed for the variable-geometry turbine (VGT) and exhaust gas recirculation (EGR) system of diesel engines based on the principle of active disturbance rejection control. This complex, double-input, double-output, VGT-EGR system is treated as two ideal, decoupled integrators for the purpose of control design, while all other dynamics that deviate from the ideal model, including cross coupling and dynamic variations due to operating point changes, and all other uncertainties are treated as the total disturbance to be estimated and canceled. Since the estimation of the total disturbance is bandwidth limited in the VGT-EGR system, feedforward controllers, one static and one dynamic, are used to speed up the response. The proposed controller is simple to implement, easy to tune, and able to handle a wide range of uncertainties without retuning or gain-scheduling. Its effectiveness has been validated in simulation, experimental tests, as well as in mathematical analysis, proving the inverse relationship between the tracking error and the controller bandwidth.

A key research area in autonomous driving is how to model the driver’s decision-making behavior, due to the fact it is significant for a self-driving vehicles considering their traffic safety and efficiency. However, the uncertain characteristics of vehicle and pedestrian trajectories affect urban roads, which poses severe challenges to the cognitive understanding and decision-making of autonomous vehicle systems in terms of accuracy and robustness. To overcome the abovementioned problems, this paper proposes a Bayesian driver agent (BDA) model which is a vision-based autonomous vehicle system with learning and inference methods inspired by human driver’s cognitive psychology. Different from the end-to-end learning method and traditional rule-based methods, our approach breaks the driving system up into a scene recognition module and a decision inference module. The perception module, which is based on a multi-task learning neural network (CNN), takes a driver’s-view image as its input and predicts the traffic scene’s feature values. The decision module based on dynamic Bayesian network (DBN) then makes an inferred decision using the traffic scene’s feature values. To explore the validity of the Bayesian driver agent model, we performed experiments on a driving simulation platform. The BDA model can extract the scene feature values effectively and predict the probability distribution of the human driver’s decision-making process accurately based on inference. We take the lane changing scenario as an example to verify the model, the intraclass correlation coefficient (ICC) correlation between the BDA model and human driver’s decision process reached 0.984. This work suggests a research in scene perception and autonomous decision-making that may apply to autonomous vehicle system.

This paper proposes the extended state observer (ESO)-based active disturbance rejection control (ADRC) for the speed tracking of an autonomous vehicle. Uncertainties, both in the vehicle plant and in the sensors, such as nonlinear uncertainties due to the powertrain dynamics, variations in rolling resistance and air resistance, are all estimated in real-time by an extended state observer (ESO). Furthermore, a simple vehicle longitudinal dynamics model, including a mean value engine model (MVEM), is implemented to obtain the parameters in ADRC and design a feedforward controller to enhance the controller’s performance. The proposed controller is validated through CarSim^®/Simulink^® simulations and road tests. The simulation validates the adaptiveness of the proposed controller against the well-tuned proportional integral derivative (PID) controller, and the speed tracking error of the proposed controller is within 1.26% in simulation. Simulation results also show that fuel consumption can be improved by 3.6% by changing the accelerator pedal depth and positive rate. Finally, the road tests are completed under four kinds of road conditions, and the maximum tracking error is smaller than 0.5 km/h.