Trajectory planning is essential for ensuring the safe operation of autonomous vehicles. However, existing methods rarely consider the vehicle’s multi-coupled dynamics, including lateral-longitudinal motion coupling, tire force coupling, and lateral instability. This omission can result in infeasible trajectories, vehicle instability, or even accidents under extreme conditions. To address this challenge, this study presents a multi-coupled dynamics trajectory planning (MCTP) scheme. MCTP establishes a coupled kinematics model to accurately represent vehicle motion states and constructs a tire force representation model, which based solely on vehicle motion states, facilitating seamless integration into trajectory planning. By incorporating coupled tire force characteristics and lateral stability analysis, a set of coupled dynamic constraints is formulated to ensure trajectory feasibility and lateral stability. Additionally, a multi-objective function is designed to further optimize trajectory safety, dynamic feasibility, and lateral stability, with the optimal trajectory obtained through receding horizon optimization. Closed-loop validation on both hardware-in-the-loop and real-vehicle experimental platforms demonstrates that, MCTP generates trajectories with enhanced safety and feasibility. It also improves tracking stability margins and dynamics performance, highlighting its effectiveness in handling extreme conditions.

The active crash avoidance system of intelligent vehicles faces challenges in accurately triggering and achieving effective multiobjective coordination control in complex driving environments characterized by multiple vehicles and variable road conditions, thereby posing a threat to driving safety for human. To address those issues, this article introduces a decision-making, path planning, and tracking integrated predictive control method (DMIC). First, DMIC incorporates vehicle actuation system characteristics, road conditions, and environmental information to design a dynamic characteristic-based risk indicator, which is applied to design different control modes. DMIC then applies continuous activation functions to activate optimized states and control inputs under various control modes, forming an integrated event-triggered continuous decision-making objective function. Afterward, DMIC employs the receding horizon optimization to calculate the front-wheel steering angle and wheel torques based on the integrated predictive model and time-varying constraints. Verification on a driver-in-the-loop (DiL) platform demonstrates that DMIC can accurately and smoothly switch among different optimization states, ensuring crash avoidance with arbitrary approaching vehicles under varying road conditions, thereby maintaining smooth vehicle responses, enhancing the driving stability and ride comfort, while meeting real-time and robustness requirements.

Adaptive cruise control (ACC) systems currently face the challenge of balancing tracking performance and avoiding collisions with arbitrary cut-in vehicles from different lanes. The multiobjective ACC proposed in this article is based on a novel integrated structure. The novel integrated ACC structure consists of an adaptive controller and the associated switching mechanism. The controller combines the upper and lower layers, which are common in today's hierarchical controllers. The switching mechanism is designed to switch between different modes to avoid collisions and maintain tracking capability in complex driving scenarios. Complex scenarios are designed to validate the integrated structure's effectiveness, real-time performance, and robustness, and a driver-in-the-loop platform is established. The results indicate that the novel integrated structure is capable of tracking the preceding vehicle accurately while avoiding colliding with the surrounding vehicle from various directions, thereby ensuring vehicle stability under varying road adhesion and system uncertainties.

The increasing requirements for vehicle driving safety improvement have led to numerous and in-depth studies on vehicle stability, especially for autonomous vehicles. The main concerns of vehicle stability research in autonomous vehicles include the vehicle stability analyzing, criterion constructing and controller designing. Therefore, this paper provides a comprehensive review of state-of-the-art vehicle stability criterion and control application for autonomous vehicles. First, the lateral vehicle linear stability criterion and widely-used active stability control applications are introduced. Next, the nonlinear vehicle stability analysis algorithm and criterion, based on the well-known phase plane method, are discussed in detail. The stability controller design, including the activation strategy and tracking objectives, is reviewed. In addition, emerging research challenges and trends for future improvement in lateral stabilization of autonomous vehicles are finally summarized.

This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.

Vehicle stability control is the core technology required for improving driving safety of advanced driver assistance systems (ADAS). In this paper, vehicle dynamic stability characteristics are investigated, and an improved vehicle stability controller is proposed to enhance the vehicle’s performance. The sum-of-squares programming is introduced to estimate its stability region and qualitative analysis is utilized to investigate the effect of various driving conditions on the stability region. An approximate dynamic stability boundary is established for different steering angle inputs. A new Lyapunov-function-based vehicle dynamic stability (LFVDS) controller is then designed to improve vehicle stability and dynamics performance based on the hierarchical structure. A test on a Hardware-In-the-Loop platform is formulated to validate the vehicle state response under the traditional and the proposed stability controllers. The results indicate that, compared with the traditional stability controller, the LFVDS controller can effectively reduce longitudinal velocity drop by 33% on a slippery road surface with ensured vehicle stability.

In the research of autonomous vehicles, most existing studies treat the decision/planning and control as two separate problems. This idea originates from robotics. But since there are essential differences between robot and autonomous vehicle, the structure in Robotics may not be suitable for autonomous vehicles. Considering decision/planning and control separately may affect the performance of autonomous vehicle under complex driving conditions. To fill in the research gap, this paper proposes a novel scheme which considers the local motion planning and control in a combined manner. Firstly, the local motion planning is transformed into the longitudinal control problem based on the proposed scenario adaptive MPC, by which the motion behavior (driving along the global path, car-following, lane-change) can be automatically decided. Then, the lateral MPC controller is designed to track the global path and conduct the local motion commands. To ensure the performance of the path tracking control and a smooth lane-change process simultaneously, an adaptive weight mechanism is introduced in the lateral controller. Comprehensive case studies including both straight and curve road are conducted based on Carsim-Simulink co-simulation platform. The results show that the proposed algorithm can not only ensure the vehicle safety in complex driving conditions, but also ensure that the vehicle can drive at its desired velocity as much as possible by intelligently judging the most proper motion behaviors.

With the improvements in sensor performance (cameras, Lidars) and the application of deep learning in object detection, autonomous vehicles (AVs) are gradually becoming more notable. After 2019, AV has produced a wave of enthusiasm, and many papers on object detection were published, boasting both practicality and innovation. Due to hardware limitations, it is difficult to accomplish accurate and reliable environment perception using a single sensor. However, multi-sensor fusion technology provides an acceptable solution. Considering the AV cost and object detection accuracy, both the traditional and existing literature on object detection using image and point-cloud was reviewed in this paper. Additionally, for the fusion-based structure, the object detection method was categorized in this paper based on the image and point-cloud fusion types: early fusion, deep fusion, and late fusion. Moreover, a clear explanation of these categories was provided including both the advantages and limitations. Finally, the opportunities and challenges the environment perception may face in the future were assessed.

This article presents a novel integrated path-following, crash avoidance, and crash mitigation control algorithm for autonomous vehicles. To improve stability and tracking accuracy of the algorithm in extreme conditions, combined-slip tire forces are considered in the system model. A predictive control framework that monitors slip conditions at each tire is then developed to achieve good dynamics performance by controlling active front steer and brake modulation at each corner. A novel switching mechanism that does not rely on a separate path generation module is designed for avoidance and mitigation phases, which is verified in various harsh driving conditions. Another strong point is the objective function for the crash mitigation phase that is developed based on real-world crash statistics. Simulation results confirm that the proposed algorithm can not only track the desired path in normal driving phase, but also avoid crash and reduce crash severity with ensured vehicle stability.

This paper proposes a novel robust path-following strategy for autonomous road vehicles based on type-2 fuzzy PID neural network (PIDT2FNN) method coupled to an Extended Kalman Filter-based Fuzzy Neural Network (EKFNN) observer. Uncertain Gaussian membership functions (MFs) are employed to self-adjust the universe of discourse for MFs using the adaptation mechanism derived from Lyapunov stability theory and Barbalat's lemma. External disturbances are significant in autonomous vehicles by changing the driving condition. Furthermore, parametric uncertainties related to the physical limits of tires and the change of the vehicle mass may significantly affect the desired performance of autonomous vehicles. The robustness of the proposed controller against the parametric uncertainties and external disturbances is compared with one active disturbance rejection control (ADRC) algorithm, and a linear-quadratic tracking (LQT) method. The obtained results in terms of the maximum error and root mean square error (RMSE), demonstrate the effectiveness of the proposed control algorithm to reach the minimized path-tracking error.

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim-Simulink simulation.

This paper investigates the lane keeping control of autonomous ground vehicles (AGVs) considering the rollover prevention and input saturation. An enhanced state observer-based sliding mode control (SMC) strategy is proposed to achieve the control purpose and maintain the lane keeping errors as well as the roll angle within the prescribed performance boundaries. Three contributions are made in this paper. First, a prescribed performance function (PPF) is proposed in the controller design, aiming to implement the error transformation so as to constrain the controlled variables within the prescribed performance boundaries. Second, a modified sliding surface is developed incorporating two nonlinear functions, whose specialities and benefits are taken advantage of: one is a barrier function to restrict the load transfer ratio (LTR) in a safe boundary to guarantee the roll stability; another is a monotonely decreasing function to adaptively change the damping ratio of the closed-loop system to improve the transient performance, including reducing the transient overshoots and steady-state errors. Third, a modified multivariable adaptive SMC controller is proposed to achieve the integrated lane-keeping and roll control in the presence of the input saturation and bound-unknown disturbances. The stability of the closed-loop system is rigorously proved via the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified with a high-fidelity and full-car model via the CarSim platform.

An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV), which is based on driving conditions recognition and genetic algorithm (GA). The proposed controller can be used in the real-time application. First, the studied multimode PHEV is modeled and four traction operation modes are introduced in detail. Second, the principal component analysis (PCA) algorithm is utilized to classify the real historical driving conditions data. Four types of driving conditions are constructed to describe the representative scenarios. Then, GA is applied to search the optimal values for seven control actions offline. These parameters for different driving conditions are preserved and can be activated online. Finally, the driving condition is identified online and the corresponding control actions are loaded and adopted. Simulation results indicate that the proposed approach is close to the globally optimal method, dynamic programming, and is superior to the charge-depleting/charge-sustaining technique. Also, hardware-in-the-loop experiment is built to validate the real-time characteristic of the proposed strategy.

This paper investigates the lane keeping control for four-wheel independently actuated autonomous vehicles. To guarantee the vehicle safety when the active-steering motor entirely fails, the steering manoeuvre is accomplished by the differential drive assisted steering (DDAS), which is generated by the differential moment between the front wheels. A novel adaptive multivariable super-twisting control strategy is proposed to realize the control objective in finite time, considering the multiple unknown and mismatched disturbances of the steering system with the chattering effect removed. In the sliding surface, a nonlinear function is designed to adaptively change the damping ratio of the closed-loop system so as to improve the transient performance of the lane keeping control in the faulty condition. The finite-time convergence of the closed-loop system is proved by Lyapunov function technique. Results of CarSim-Simulink simulations with a high-fidelity and full-car model have verified the effectiveness and robustness of the proposed controller in the lane keeping control with DDAS and guaranteeing high performance.

A path planning approach to generate a path which mitigates the effects of an inevitable crash for autonomous vehicles is presented in this brief. The model predictive control algorithm is adopted here for path planning. The artificial potential field, which describes the obstacles and the potential crash severity, are added to the control objectives to avoid the obstacle, and also to mitigate the inevitable crash. The vehicle dynamic is also considered as an optimal control objective. Based on the analysis above, the model predictive controller can guarantee the command following, obstacle avoidance, vehicle dynamics, and mitigate the inevitable crash. Simulation results verified that the proposed MPC has the abilities of obstacles avoidance and mitigation of the inevitable crash.