Steering Control Of Autonomous Vehicles Research Articles

Event‐triggered adaptive secure lateral stabilization for autonomous vehicles under actuator attacks

SummaryFalse data injection attacks can disrupt the steering control actions and make a real threat to both the security and safety of autonomous vehicles. In this paper, a secure event‐triggered lateral control approach of autonomous vehicles subject to actuator attacks is investigated. Firstly, an arbitrary unknown actuator attacks is considered in the secure lateral steering control of autonomous vehicles. Thus, to save communication resources for the limited bandwidth CAN bus, the periodic event‐triggered transmission scheme is utilized, transforming the established lateral steering control into a time‐delay system through the consideration of periodic event‐triggered sampling. Then, an adaptive control compensation scheme is developed to mitigate the malicious effects caused by actuator attacks. The proposed secure control approach is skilled in compensating the unknown attacked steering control actions in an adaptive way. The stabilization criteria under the adaptive secure control law is well derived by Lyapunov–Krasovskii method and some linear inequality matrices operations. At last, the effectiveness of the proposed secure control scheme is verified by some numerical experiments borrowed from a practical vehicle.

Robust Predictive Steering Control for Autonomous Vehicles with Polynomial Noise Resilience Neural Dynamics

Robust Predictive Steering Control for Autonomous Vehicles with Polynomial Noise Resilience Neural Dynamics

Optimizing YOLO Performance for Traffic Light Detection and End-to-End Steering Control for Autonomous Vehicles in Gazebo-ROS2

Optimizing YOLO Performance for Traffic Light Detection and End-to-End Steering Control for Autonomous Vehicles in Gazebo-ROS2

Parallel Learning-Based Steering Control for Autonomous Driving

Steering control for autonomous vehicles at high speeds is challenging due to the highly nonlinear vehicle dynamics. The traditional model-based controllers usually degrade significantly in this case. With the development of artificial intelligence, learning-based control methods are emerging as promising alternatives. These methods require a tremendous amount of training data to achieve acceptable performances. However, the data collection process is costly or inefficient. To solve this problem, we propose a parallel learning-based steering control method. Specifically, we first build a neural network (NN) based trajectory generative model (GeneratingNN) based on limited steering-trajectory raw data. The GeneratingNN can efficiently generate sufficient steering-trajectory data by enumerating the allowable steering actions sequences. Then, based on the raw data and generated data, we train another NN (RecallingNN) to learn the inverse mapping relationship between steering actions and trajectories. Hence, the RecallingNN can efficiently recall appropriate steering actions once given the previewed trajectory points. In addition, to further enhance the control accuracy and robustness, we use a simple feedback controller to handle the unmodeled dynamics and external disturbance. Testing results validate that the proposed method can achieve better tracking accuracy, stability and computational efficiency.

Steering Control in Electric Power Steering Autonomous Vehicle Using Type-2 Fuzzy Logic Control and PI Control

The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system’s output is the steering angle value. This was used as input for the secondary control, PI control. This control is in charge of adjusting the motor’s position as a manifestation of the steering angle. The study results applied to the EPS system of autonomous vehicles revealed that type-2 fuzzy logic control and PI control produced better and smoother control than type-1 fuzzy logic control and PI. The slightest disturbance in the type-1 fuzzy logic control showed a significant change in steering, while this did not occur in the type-2 fuzzy logic control. The results indicate that type-2 fuzzy logic control and PI control could be used for autonomous vehicles by maintaining the comfort and safety of the users.

Adaptive type-2 fuzzy sliding mode control of steer-by-wire systems with event-triggered communication

The steering-by-wire (SbW) system is one of the main subsystems of automatic vehicles, realizing the steering control of autonomous vehicles. This paper proposes an event-triggered adaptive sliding mode control for the SbW system subject to the uncertain nonlinearity, time-varying disturbance, and limited communication resources. Firstly, an event-triggered nested adaptive sliding mode control is proposed for SbW systems. The uncertain nonlinearity is approximated by the interval type-2 fuzzy logic system (IT2 FLS). The time-varying disturbance, modeling error, and event-triggering error can be offset by robust terms of sliding mode control. The key advantage is that the high-frequency switching of sliding mode control only appears on the time derivate of control input without increasing the input-output relative degree of closed-loop SbW systems, such that the chattering phenomenon can be eliminated. Finally, theoretical analysis shows that the practical finite-time stability of the closed-loop SbW system can be achieved, and communication resources in the controller-to-actuator channels can be saved while avoiding the Zeno-behavior. Numerical simulations and experiments are given to evaluate the effectiveness of the proposed method.

Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

Human drivers have rich and diverse driving characteristics on curved roads. Finding the characteristic quantities of the experienced drivers during curve driving and applying them to the steering control of autonomous vehicles is the research goal of this article. We first recruited 10 taxi drivers, 5 bus drivers, and 5 driving instructors as the representatives of experienced drivers and conducted a real car field experiment on six curves with different lengths and curvatures. After processing the collected driving data in the Frenet frame and considering the free play of a real car’s steering system, it was interesting to observe that the shape enclosed by steering wheel angles and the coordinate axis was a trapezoid. Then, we defined four feature points, four feature distances, and one feature steering wheel angle, and the trapezoidal steering wheel angle (TSWA) model was developed by backpropagation neural network with the inputs were vehicle speeds at four feature points, and road curvature and the outputs were feature distances and feature steering wheel angle. The comparisons between TSWA model and experienced drivers, model predictive control, and preview-based driver model showed that the proposed TSWA model can best reflect the steering features of experienced drivers. What is more, the concise expression and human-like characteristic of TSWA model make it easy to realize human-like steering control for autonomous vehicles. Lastly, an autonomous vehicle composed of a nonlinear vehicle model and electric power steering (EPS) system was established in Simulink, the steering wheel angles generated by TSWA model were tracked by EPS motor directly, and the results showed that the EPS system can track the steering angles with high accuracy at different vehicle speeds.

Collision-avoidance steering control for autonomous vehicles using neural network-based adaptive integral terminal sliding mode

Due to the complex driving conditions confronted by an autonomous vehicle, it is significant for the vehicle to possess a robust control system to achieve effective collision-avoidance performance. This paper proposes a neural network-based adaptive integral terminal sliding mode (NNAITSM) control scheme for the collision-avoidance steering control of an autonomous vehicle. In order to describe the vehicle’s lateral dynamics and path tracking characteristics, a two-degrees-of-freedom (2DOF) dynamic model and a kinematic model are adopted. Then, an NNAITSM controller is designed, where a radial basis function neural network (RBFNN) scheme is utilized to online approximate the optimal upper bound of lumped system uncertainties such that prior knowledge about the uncertainties is not required. The stability of the control system is proved via Lyapunov, and the selection guideline of control parameters is provided. Last, Matlab-Carsim co-simulations are executed to test the performance of the designed controller under different road conditions and vehicle velocities. Simulation results show that compared with conventional sliding mode (CSM) and nonsingular terminal sliding mode (NTSM) control, the proposed NNAITSM control scheme owns evident superiority in not only higher tracking precision but also stronger robustness against various road surfaces and vehicle velocities.

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

Fuzzy steering control for autonomous vehicles under actuator saturation: Design and experiments

This paper presents a new control method for autonomous vehicles. The design goal is to perform the automatic lane keeping under multiple system constraints, namely actuator saturation of the steering system, roads with unknown curvature and uncertain lateral wind force. Such system constraints are explicitly taken into account in the control design procedure. To achieve this goal, we propose a new constrained Takagi–Sugeno fuzzy model-based control method using fuzzy Lyapunov control framework. The resulting non-parallel distributed compensation controller is able to handle not only various system constraints but also a large variation range of vehicle speed. In particular, Taylor’s approximation method is exploited to reduce not only the numerical complexity for real-time implementation but also the conservatism of the results. The design conditions are strictly expressed in terms of linear matrix inequalities which can be efficiently solved with available numerical solvers. The effectiveness of the proposed control method is demonstrated through both simulation and hardware experiments with various driving scenarios.

Takagi-Sugeno Model-based Steering Control for Autonomous Vehicles with Actuator Saturation

This paper is devoted to the development of a novel control law for an autonomous vehicle. The control goal is to perform lane keeping under multiple constraints, namely actuator saturation of the steering system, roads with unknown curvature, and uncertain lateral wind force. Such system constraints will be explicitly considered in the control design procedure. To achieve this goal, we propose a novel constrained Takagi-Sugeno control method using non-quadratic Lyapunov stability framework. The obtained non-parallel distributed compensation controller is not only able to handle various system constraints but also a large variation range of vehicle speed. Especially, by means of the well-known Taylor’s approximation method we can significantly reduce the numerical complexity of the proposed control method to ease the real-time control implementation. The control synthesis conditions are expressed in terms of linear matrix inequalities which can be efficiently solved with available solvers. The effectiveness of the proposed method is demonstrated with different simulation scenarios.

Fuzzy logic steering control of autonomous vehicles inside roundabouts

The expansion of roads and the development of new road infrastructures have increased in recent years, linked to the population growing in large cities. In the last two decades, roundabouts have largely replaced traditional intersections in many countries. They have the advantage of allowing drivers continuous flow when traffic is clear, without the usual delay caused by traffic lights. Although roundabouts with and without traffic-signal control have been widely used and considered in the literature, driverless control on roundabouts has not been studied in depth yet. The behavior of autonomous vehicles in roundabouts can be divided into three stages: entrance, inside, and exit. The first and last may be handled as an extension of intersections. However, autonomous driving on the roundabout requires special attention. In this paper, the design and implementation of a fuzzy logic system for the steering control of autonomous vehicles inside the roundabout is proposed. Cascade architecture for lateral control and parametric trajectory generation are used. Fuzzy control has proved to be easy to define using expert knowledge. Experiments with a real prototype have been carried out, taking into account different speed profiles and lane change maneuvers inside the roundabout, with very satisfactory results.