Steering Angle Of Front Wheels Research Articles

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

The resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.

Path tracking with personalized handling stability constraints matched to driving styles

In the path-tracking control process, drivers have different expectations of the lateral response of vehicles. To meet drivers’ expectations of handling stability, this paper proposes a model predictive control (MPC)-based path-tracking controller with personalized handling stability constraints matched to driving style. Firstly, the categories of driving styles are obtained by applying the K-Means algorithm to the collected driving data. Then the sideslip angle-longitudinal velocity map is constructed based on the nonlinear dynamics model of the vehicle to generate the front-wheel steering angle constraints that are matched with the maximum sideslip of the vehicle expected by drivers of different styles. After that, an MPC-based path-tracking controller has been built, and the front-wheel steering angle is incorporated into the MPC controller. Furthermore, compared to the strategy of directly using sideslip angle constraints for handling stability control, this approach avoids introducing additional constraint variables, leading to improved real-time performance. Simulation experiments are conducted to compare the dynamic vehicle responses between the handling stability control that takes into account the driving style and the control system that does not consider the driving style. In addition, the subjective evaluation is conducted to verify the proposed path-tracking control method that considers the stability constraints of driving style. The evaluation results show that the proposed method can obtain better subjective evaluation results than a path-tracking controller that does not consider the driving style. Finally, through the hardware-in-the-loop experiments, the real-time computational capability of the proposed controller is verified.

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.

Adaptive heading control strategy for unmanned ground vehicle with variable wheelbase based on robust-active disturbance rejection control

In order to fully exploit the variable wheelbase characteristics of the unmanned ground vehicle (UGV) with variable configuration, this paper proposes an adaptive heading tracking control strategy based on wheelbase changes. This strategy involves adjusting the wheelbase according to different working conditions to optimize various driving performance aspects. Firstly, the impact of changing wheelbase on the response characteristics of sideslip angle and heading angle relative to the front-wheel steering angle is analyzed by using a lateral vehicle dynamic model. The configurations where the 2nd axle moves forward by 0.5 m, remains unchanged, and moves backward by 0.5 m are defined as the ‘stable configuration’, ‘balanced configuration’, and ‘maneuverable configuration’, respectively. Considering time lag effects of hydraulic systems and external random noise interference, a robust-active disturbance rejection control (r-ADRC) method is developed by incorporating a robust term into active disturbance rejection control (ADRC) in order to achieve desired front-wheel steering angle. Additionally, a torque distribution method based on the tire load rate and the real-time vertical load of each wheel is applied to ensure uniform tire wear and enhance longitudinal driving stability. Finally, simulations under various typical working conditions as well as scaled prototype experiments are conducted, and both the theory behind the changing wheelbases and the effectiveness of proposed control strategy are verified. According to different vehicle configurations and mission requirements, the maneuverability and stability of the ground unmanned vehicle are ensured respectively.

Real-Time Path Planning for Obstacle Avoidance in Intelligent Driving Sightseeing Cars Using Spatial Perception

The increasing prevalence of intelligent driving sightseeing vehicles in the tourism industry underscores the critical importance of real-time planning for effective local obstacle avoidance paths when these vehicles encounter obstacles during operation. To fulfill this requirement, it is imperative to establish real-time dynamic perception as the foundational element. Thus, this paper introduces a novel local path planning algorithm founded on the principles of spatial perception. In the diverse array of road environments characterized by varying spatial features, sightseeing vehicles can effectively achieve safe and comfortable obstacle avoidance maneuvers. The proposed approach employs a high-precision positioning module and a real-time dynamic perception module to acquire real-time spatial information pertaining to the sightseeing vehicle and the road environment. It comprehensively integrates spatiotemporal safety constraints and obstacle avoidance curvature constraints to derive control points for the obstacle avoidance path. Specific control points undergo optimization and adjustment, ultimately resulting in the generation of the obstacle avoidance spatiotemporal path through discrete interpolation using B-spline curves. These locally tailored paths are subsequently compared with local obstacle avoidance paths generated using Bezier curves. The empirical validation of the proposed local obstacle avoidance path algorithm is conducted through a combination of simulation analysis and real vehicle verification. The research outcomes affirm that the algorithm can indeed produce smoother local obstacle avoidance paths, resulting in reduced front-wheel steering angles and yaw angle variations. This enhancement substantially contributes to the overall stability of sightseeing vehicles during obstacle avoidance maneuvers.

An algorithm for solving the equilibrium points of high-dimensional nonlinear vehicle dynamic system based on a combination of genetic algorithm, sequential quadratic programming method, and continuation method

The paper proposes an algorithm that combines Genetic Algorithm, Sequential Quadratic Programming, and Continuation Method to solve the equilibrium points of a multi-degree-of-freedom vehicle nonlinear system. The algorithm’s effectiveness is demonstrated by applying it to search for equilibrium points of a 5-degree-of-freedom (5DOF) nonlinear vehicle model, which considers both longitudinal and lateral motion. The dynamic equilibrium points of front-wheel-drive, rear-wheel-drive, and all-wheel-drive vehicles with different front-wheel steering angle inputs are calculated. Taking the front-wheel-drive system as an example, the system equilibrium points are further analyzed using phase space analysis and eigenvalue analysis for verification. In addition, the impact of the driving effect on the equilibrium points bifurcation is investigated. The results show that compared with the Genetic Algorithm alone and the combination method of Genetic Algorithm and Sequential Quadratic Programming, the proposed algorithm can effectively and accurately solve the equilibrium points of the 5DOF vehicle model. The study also reveals that the driving effect significantly influences the vehicle equilibrium point bifurcation.

Research on trajectory tracking and body attitude control of autonomous ground vehicle based on differential steering.

The differential steering can be used not only as the backup system of steer-by-wire, but also as the only steering system. Because the differential steering is realized through the differential moment between the coaxial left and right driving wheels, the sharp reduction of the load on the inner driving wheel will directly lead to the failure of the differential steering when the four-wheel independent drive electric vehicle approaches the rollover. Therefore, this paper not only realizes the trajectory tracking of autonomous ground vehicle through the differential steering, but also puts forward the body attitude control to improve the handling stability. Firstly, the dynamic and kinematic models of differential steering autonomous ground vehicle (DSAGV) and its roll model are established, and the linear three-degree of freedom vehicle model is selected as the reference model to generate the ideal body roll angle. Secondly, a model predictive controller (MPC) is designed to control the DSAGV to track the given reference trajectory, and obtain the required differential moment and the resulting front-wheel steering angle. Then, a sliding mode controller (SMC) is adopted to control the DSAGV to track the ideal body roll angle, and obtain the required roll moment. The simulation results show that the proposed MPC and SMC can not only make the DSAGV realize the trajectory tracking, but also achieve the body attitude control.

Linear quadratic regulator based on extended state observer–based active disturbance rejection control of autonomous vehicle path following control

This article studies the control problem of autonomous vehicle path following with coordination of active front steering and differential steering. A hierarchical control scheme including upper layer and lower layer is proposed. In the upper layer controller, a linear quadratic regulator based on extended state observer is proposed to generate the front-wheel steering angle and external yaw moment, where extended state observer is used to estimate and compensate for the system uncertainty and external disturbance which enhances the capability of the vehicle to suppress the disturbance. A brake force distribution scheme based on the theory of control allocation is proposed in the lower layer controller to optimize and coordinate each wheel brake force to achieve differential steering. Finally, the effectiveness of proposed control scheme is verified in a co-simulation platform based on CarSim/Simulink; it can be concluded that the linear quadratic regulator based on extended state observer scheme not only has a few parameters need to be tuned, but also has the capability of active disturbance rejection.

Coordination control method of autonomous ground electric vehicle for simultaneous trajectory tracking and yaw stability control

A hierarchical trajectory tracking and yaw stability combined control strategy of autonomous ground vehicle within-wheel motor is proposed in this paper to achieve simultaneous and accurate trajectory tracking and vehicle yaw stability control. A trajectory-tracking controller is designed on the basis of model predictive control algorithm, and the ideal front-wheel steering angle requirement is calculated to follow the referenced trajectory. In order to improve the accuracy of vehicle steering control, a vehicle steering controller is designed based on high-order sliding mode control method, in which the control demand of front-wheel steering angle is satisfied by real-time torque control of vehicle steering motor. Simultaneously, a double power reaching rate-based sliding mode control method is applied to design the vehicle yaw stability controller, in which the yaw moment control requirement is met by an optimal oriented tire force allocation algorithm. The simulation and experiment results show that the presented control method can improve the accuracy and real-time performance of trajectory tracking control while ensuring vehicle yaw stability.

Vehicle rollover warning system based on TTR method with inertial measurement

This paper presents a theoretical and experimental study conducted on the rollover warning of wheeled off-road operating vehicles. The time to rollover warning algorithm was studied with real-time vehicle roll angle and roll angle velocity as the input variables, and lateral load transfer ratio was used as the rollover determination index. Subsequently, a vehicle dynamics model was built using CarSim software, and a warning algorithm was established in the MATLAB/Simulink environment. The rollover joint simulation in CarSim and MATLAB/Simulink was conducted under typical working conditions. Finally, combined with inertial measurements, a rollover warning system was independently developed. In addition, the rollover warning system was installed on a light forest firefighting truck to verify the feasibility of the system via a real vehicle experiment, and the law of vehicle rollover motion was also studied. The serpentine experiment and steady-state rotation experiment were conducted. The experimental results showed that at identical front-wheel steering angles, the roll angle and lateral acceleration increased with an increase in the vehicle speed. Furthermore, for identical vehicle speeds, the roll angle and lateral acceleration of the vehicle increased with an increase in the front-wheel steering angle. The dangerous vehicle speed was 50 km h−1 in the serpentine condition and 40 km h−1 in the steady-state rotation condition. The risk trend and alarm signal obtained by the rollover warning system were consistent with the actual situation. Thus, this can assist drivers in judging the rollover risk and effectively improve the active safety of special vehicles. Furthermore, it also provides a reference for further research on active rollover control technology of special vehicles.

A Hierarchical Anti-Disturbance Path Tracking Control Scheme for Autonomous Vehicles Under Complex Driving Conditions

Path tracking is one of the most crucial issues of autonomous vehicles, but uncertain factors including road roughness and wind gusts could cause lateral deviation and even divergence of the control system. Therefore, this paper proposes a hierarchical anti-disturbance tracking architecture based on the steer-by-wire (SBW) system to improve tracking accuracy and dynamic stability for autonomous vehicles. The proposed architecture is robust strongly against different types of disturbances in the path tracking process by means of hierarchical decoupling. Firstly, in the upper-layer path tracking module, a robust model predictive control (MPC) method with a feedback correction term calculates the expected front-wheel steering angle. Secondly, in the lower-layer SBW module, a nonlinear high gain rack force observer is proposed to compensate for the generalized rack force disturbance. Then, an improved non-singular fast terminal sliding mode auxiliary control law based on the super-twisting algorithm is presented to attenuate the norm-bounded rack disturbance effectively. Simulation and experiment results show that the proposed control architecture can improve the path tracking accuracy and has strong interference resistance and robustness against both modeled and unmodeled disturbances.

Robust Control with Uncertain Disturbances for Vehicle Drift Motions

Professor drivers, including racing drivers, can drive cars to achieve drift motions by taking control of the steering angle in high tire slip ratios, which provides a way to improve the driving safety of autonomous vehicles. The existing studies can be divided into two kinds based on analysis methods, and the theory-based is chosen in this study. Because the recent theory based is most applied for planar models with neglect of the rollover accident risk, the nonlinear vehicle model is established by considering longitudinal, lateral, roll, and yaw motions and rolling safety with the nonlinear tire model UniTire. The drift motion mechanism is analyzed in steady and transient states to obtain drift motion conditions, including the velocity limitation and the relationship between sideslip angle and yaw rate, and vehicle main status parameters including the velocity, side-slip angle and yaw rate in drift conditions. The state-feedback controller is designed based on robust theory and LMI (linear matrix inequation) with uncertain disturbances to realize circle motions in drift conditions. The designed controller in simulations realizes drift circle motions aiming at analyzed status target values by matching the front-wheel steering angle with saturated tire forces, which satisfies the Lyapunov stability with robustness. Robust control in drift conditions solves the problem of how to control vehicles to perform drift motions with uncertain disturbances and improves the driving safety of autonomous vehicles.

Coordination of Lateral Vehicle Control Systems Using Learning-Based Strategies

The paper proposes a novel learning-based coordination strategy for lateral control systems of automated vehicles. The motivation of the research is to improve the performance level of the coordinated system compared to the conventional model-based reconfigurable solutions. During vehicle maneuvers, the coordinated control system provides torque vectoring and front-wheel steering angle in order to guarantee the various lateral dynamical performances. The performance specifications are guaranteed on two levels, i.e., primary performances are guaranteed by Linear Parameter Varying (LPV) controllers, while secondary performances (e.g., economy and comfort) are maintained by a reinforcement-learning-based (RL) controller. The coordination of the control systems is carried out by a supervisor. The effectiveness of the proposed coordinated control system is illustrated through high velocity vehicle maneuvers.

Evaluation of tractor driving comfort according to the steering angle and speed using a virtual prototype

As tractors mainly transit rural roads, drivers can suffer from harmful vibrations transmitted though their seats. Thus, the driving comfort in tractors with agricultural equipment should be evaluated. To investigate the effects of forward motion and front-wheel steering on the driving comfort of two-wheel-drive tractors with agricultural equipment, we establish a man–machine–road virtual prototype for simulations and validate it experimentally. First, a tractor that has suspended agricultural equipment and is operating at front-wheel steering angles of 0°, 12°, 24°, and 36° is driven on a random level-D road. Simultaneously, the vibration accelerations experienced by the driver are obtained along the X, Y, and Z axes with the coordinate system defined according to the ISO 2631-1 standard. The combined weighted root-mean-square (RMS) acceleration corresponding to the driver’s whole-body vibration increases from 0.205 to 0.909 m/s2 when increasing the steering angle. Then, considering the forward speed as the variable, the front-wheel steering angle is set at 30°, and the vibration accelerations experienced by the driver are obtained when the tractor travels at 4, 5, and 6 km/h on a random level-D road. The combined weighted RMS acceleration corresponding to the driver’s whole-body vibration increases from 0.68 to 1.03 m/s2 when increasing the speed. Therefore, we found that the velocity of a tractor should be reduced while steering, and excessively large front-wheel steering angles should be avoided to increase driving comfort.

Obstacle-avoidance algorithm design for autonomous vehicles considering driver subjective feelings

Normally, autonomous vehicles (AVs) are limited to be widely used in market not only for technical factors, but also psychological reasons. Considering the psychological feelings of drivers during switching manned to unmanned operation modes, an algorithm for avoiding obstacles is designed for AVs by considering driver psychological feelings. A so-called confidence-limit-distance (denoted as CLD) for driver to avoid obstacle is experimentally obtained by a number of real track tests with 100 volunteer test drivers as required to approach the obstacle in a certain way. Based on Artificial Potential Field (APF) method, a road potential field is established accordingly to characterize the information on the real road. To express the different influences of obstacles on the driver’s psychological feeling in both longitudinal and lateral directions, a confidence potential field also is established based on a two-dimensional normal distribution combining von Mises distribution. Hence, the second-order Taylor expansions of the road potential field and the confidence potential field are firstly introduced into the cost functions for model predictive control (MPC). The corresponding MPC algorithm used here selects front-wheel steering angle as the control variable to be solved. The CLD and range of sensed vehicle motion state variables are taken as the constraints of the MPC. Co-simulations and Hardware-in-the-Loop (HIL) tests are carried out, showing the effectiveness of designed algorithm, which can be useful in the development and design for Advanced Driving Assistant System (ADAS) and AVs.

Trajectory tracking control of steer-by-wire autonomous ground vehicle considering the complete failure of vehicle steering motor

This paper investigates the trajectory tracking control problem of steer-by-wire autonomous ground vehicle considering the complete failure of vehicle steering motor. In order to achieve the trajectory tracking control of autonomous ground vehicle in presence of the complete failure of vehicle steering motor, the mechanical transmission mechanism of vehicle steering system is analyzed with the steering motor fault being considered, and the differential steering is used to actuate the turning of vehicle in case of emergency. A model predictive controller with the constraints of tire cornering angle and road adhesion is designed to compute the expected front-wheel steering angle for vehicle trajectory tracking control, and a nonsingular terminal sliding mode controller is proposed to track the expected front-wheel steering angle using differential-steering moment, in which a novel observer design method is presented to estimate the actual front-wheel steering angle. Two case studies are implemented in a CarSim-Simulink co-simulation platform, and the simulation results have verified that the proposed method have satisfactory trajectory tracking control effectiveness in presence of the complete failure of vehicle steering motor.

Coordinated control based path following of distributed drive autonomous electric vehicles with yaw-moment control

This research seeks to study the path following issue for distributed drive autonomous electric vehicles (DDAEV). In order to improve the tracking accuracy and vehicle stability, coordinated control of tracking deviation compensation and vehicle stability by yaw-moment is proposed. Based on the three degree-of-freedom vehicle model, model predictive control is used to calculate the steering angle of front wheels to control the vehicle along the reference path by autonomous steering. In this process, two sliding surfaces whose control targets are tracking deviation and yaw rate deviation are first set, then two sliding surfaces are combined into one coordinated sliding surface with weight coefficients considering the vehicle stability, and finally the yaw-moment is figured out and distributed with the constant torque requirement of the vehicle. Carsim co-simulation with Simulink and Hardware-in-the-Loop experiment are conducted, which shows that compared to autonomous steering control, coordinated control achieves better path following accuracy and vehicle stability for DDAEV under different driving conditions.

Research on Curved Path Tracking Control for Four-Wheel Steering Vehicle considering Road Adhesion Coefficient

Curved path tracking control is one of the most important functions of autonomous vehicles. First, small turning radius circular bends considering bend quadrant and travel direction restrictions are planned by polar coordinate equations. Second, an estimator of a vehicle state parameter and road adhesion coefficient based on an extended Kalman filter is designed. To improve the convenience and accuracy of the estimator, the combined slip theory, trigonometric function group fitting, and cubic spline interpolation are used to estimate the longitudinal and lateral forces of the tire model (215/55 R17). Third, to minimize the lateral displacement and yaw angle tracking errors of a four-wheel steering (4WS) vehicle, the front-wheel steering angle of the 4WS vehicle is corrected by a model predictive control (MPC) feed-back controller. Finally, CarSim® simulation results show that the 4WS autonomous vehicle based on the MPC feed-back controller can not only significantly improve the curved path tracking performance but also effectively reduce the probability of drifting or rushing out of the runway at high speeds and on low-adhesion roads.

Four-Wheel Differential Steering Control of IWM Driven EVs

This paper aims to realize the four-wheel differential steering function of an in-wheel motor (IWM) driven electric vehicle (EV) with a steer-by-wire (SBW) system. The dynamic models of four-wheel differential steering vehicle (4WDSV) and four-wheel steering vehicle (4WSV) are established, and a front-wheel steering vehicle (2WSV) with the neutral steering characteristics and a drop filter of the sideslip angle is employed as the reference model to provide the ideal yaw rate and sideslip angle. According to the reference model, the decoupling and fractional PI λ D μ controllers are designed for the 4WSV to obtain the standard inputs, i.e., the front and rear wheel steering angles, which is finally drawn as the MAP. Furthermore, the sliding mode controller (SMC) and torque distributor are designed to control the 4WDSV to have the same yaw rate as the 4WSV, whose inputs are consulted from the MAP drawn in advance according to the vehicle speed. The simulation results of several vehicle models with/without the controller of two working conditions (reverse and in phase steering) are presented, which indicates that the proposed decoupling and fractional PI λ D μ controller for the 4WSV is effective to obtain its standard inputs, and the proposed SMC and torque distributor for the 4WDSV ensure that it has the same yaw rate as the 4WSV, as well as the good robustness.

Robust Hierarchical Model Predictive Control for Trajectory Tracking with Obstacle Avoidance

A robust hierarchical path planning and trajectory tracking framework is proposed to maintain a collision-free path for autonomous vehicles. For the path-planning, a constrained finite-time optimal problem is solved to generate a feasible and collision-free trajectory considering the vehicle kinematics. For the trajectory-tracking, a motion controller is proposed by solving a constrained model predictive control problem, obtaining the front wheel steering angles. Furthermore, to enhance the robustness of the motion controller against the uneven network-induced time delay and unmodelled lateral vehicle dynamics, an additional error feedback mechanism is introduced in the motion controller. Simulations are conducted when both moving obstacles and static obstacles exist. Simulation results show that the proposed hierarchical control framework can effectively guarantee safe and feasible driving maneuvers.