Driver Steering Research Articles

High authenticity steering feel control strategy for Steer-by-Wire system based on terminal sliding mode rack force observer

This article proposes a high authenticity steering feel control strategy for Steer-by-Wire (SBW) system based on terminal sliding mode rack force observer, in response to the issue of how to design the steering feel of the SBW system to reflect road conditions. Firstly, a terminal sliding mode rack force observer is designed to achieve real-time and accurate estimation of steering system rack force. Then, based on the vehicle motion state, design the feedback characteristic curve of the rack force and the compensation torque of the handwheel to obtain the expected steering feel, and use the automatic disturbance rejection controller (ADRC) method to control the voltage of the handwheel motor. In simulation testing, a SBW open loop control system model is established to verify the performance of the designed rack force observer. The objective evaluation indicators are used to verify that the designed feedback method can intuitively adjust and meet the driver's steering feel needs, and the impact of various compensation torques on the evaluation indicators is analyzed. Finally, the results of actual vehicle experiments indicate that the designed method can provide suitable steering feel, verifying the feasibility of the design technology.

Research on Variable Transmission Ratio Control Method to Improve Vehicle Handling Comfort Based on Steer-by-Wire System

The steer-by-wire system severs the mechanical link between the steering wheel and the steering gear. This configuration enhances the angular transmission characteristics. Entering the nonlinear region of the tires could result in a reduction in the vehicle’s steering gain. In order to improve the comfort of vehicle steering operation, we have developed a variable transmission ratio controller for the steer-by-wire (SBW) system. This controller utilizes information on the vehicle speed and steering wheel angle to generate a variable transmission ratio coefficient, thereby adjusting the steering ratio. We introduce a multi-objective comprehensive evaluation index that takes into account vehicle lateral deviation, driver steering burden, vehicle stability, and safety. To harmonize the transmission ratio weights of constant steering gain, we employ the coefficient of variation method. Ultimately, a fuzzy neural network is employed to craft a nonlinear controller. We conducted steady-state circular motion tests, double lane-change tests, and step input tests to validate the performance of the variable transmission ratio control. The results suggest that, in comparison to conventional fixed transmission ratio systems, the variable transmission ratio control within the steer-by-wire system significantly alleviates the driver’s operational burden while enhancing the vehicle’s handling stability and safety.

Nonlinear shimmy and dynamic bifurcation in vehicle system with driver steering input

Based on the dynamic coupling analysis of the front wheel shimmy and vehicle plane motion, a three degrees of freedom (DOF) nonlinear shimmy model considering driver steering input is established. Firstly, when the driver steering input is not considered, the modal properties and dynamic stability of the system are investigated by solving the Jacobian matrix. Then, the nonlinear shimmy behavior considering the driver steering input is discussed by means of numerical calculations. The slow flow equations of the vehicle system considering the driver steering input are derived based on the complexification-averaging (CA) method. According to the nonlinear dynamics theory, the saddle node (SN) and Hopf bifurcation characteristics of the system are analyzed, the analytical solution of the front wheel shimmy angle is also obtained. Finally, with the help of the understeer gradient, the influence of the linear cornering stiffness of the front wheel on the nonlinear shimmy motion is discussed. It is summarized that the SN and Hopf bifurcation are more likely to occur in the shimmy system for an oversteered vehicle. The relevant conclusions are useful for the early anti-shimmy design of vehicles.

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi-Task Time-Series Transformer

Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.

An individualized robust stability control strategy for active front steering vehicles

To improve the vehicle stability and driver steering performance, this paper presents an individualized yaw stability control strategy based on H∞ robust control for active front steering (AFS) vehicles. A driver-vehicle system, including a driver steering model and a vehicle dynamics model with AFS, is formed. To analyze the steering characteristics of different drivers, a set of driving data of 36 drivers is collected, and the driver’s characteristics parameters are identified by using the particle swarm optimization (PSO) algorithm. A general evaluation function considering the trajectory tracking performance, vehicle stability, driver workloads, and driver’s characteristics parameters are established to evaluate the comprehensive steering performance. To accomplish the personalized control of vehicle yaw stability, an individualized H∞ robust yaw stability controller is presented by adjusting the gain of the weighting function according to the general evaluation of each driver. Driver-in-the-loop experiment is conducted based on the Matlab/Simulink-CarSim®-Prescan co-simulation platform, and the results demonstrates that the proposed control strategy can provide driver with individualized driving assistance while improving the overall driving performance and reducing the driver’s workloads.

Road Hypnosis Detection using Lane Detection

The study "Road Hypnosis Detection Using Lane Detection" focuses on the phenomenon known as "road hypnosis" or "highway hypnosis," a mental state in which a person can operate a vehicle for extended distances while reacting to outside events as expected, all without recalling that they did so consciously. Long stretches of time spent driving on smooth, featureless highways can cause this syndrome, which lowers awareness and causes a lack of stimulation. The study suggests a novel approach that makes use of lane identification algorithms to identify road hypnosis. It keeps track of the car's location in the lane and the driver's steering movements using sophisticated image processing and machine learning algorithms. Unusualities in these patterns, which could signal the start of road hypnosis, can be detected by the system. This system attempts to improve road safety and avoid accidents caused by decreased driver alertness due to road hypnosis by giving the driver real-time feedback or initiating automatic safety measures. The suggested approach is thoroughly tested and validated throughout the study in a variety of driving situations and conditions, proving its efficacy and suitability for incorporation into contemporary car safety systems. This study addresses a prevalent but frequently disregarded problem that might result in catastrophic accidents, making a substantial contribution to the field of driver safety systems. It also creates new opportunities for utilizing technology to raise driver awareness and enhance general traffic safety.

Driver-Centric Lane-Keeping Assistance System Design: A Noncertainty-Equivalent Neuro-Adaptive Control Approach

Vehicle roadway departure accidents are a major traffic safety concern as they oftentimes result in severe injuries and fatalities. To address such an issue, this article originates a novel driver-centric and neuro-adaptive-control-based lane-keeping assistance system (LKAS). The proposed control strategy synergizes a noncertainty-equivalent adaptive control design scheme, an adaptive radial-basis-function-based neural network (RBFNN) that captures the human driver's lane-keeping steering behavior, and a Gudermannian-function-based smooth parameter projection operator. The benefit and uniqueness of the proposed solution are threefold. First and foremost, the noncertainty-equivalent adaptive control design, which leverages the immersion-and-invariance-like methodology, ensures the asymptotical convergence of the parameter-estimation-error-induced perturbation despite the reference signal's persistency of excitation condition. Second, the LKAS is devised to be driver-centric, i.e., an adaptive RBFNN-based human driver steering model is embedded inside the LKAS's algorithm such that a human driver is assisted in a personalized and adaptive manner. Third, the Gudermannian-function-based smooth parameter projection operator ascertains the prescribed boundedness of the control parameters while maintaining the control action's smoothness. A pilot human-subject study using a high-fidelity moving-base driving simulator is conducted to validate the proposed LKAS. Further, its performance is compared with a baseline certainty-equivalent neuro-adaptive controller.

Experimental Evaluation of a Game-Theoretic Human Driver Steering Control Model.

Automated vehicle steering control systems have great potential to improve road safety. The development of such systems calls for mathematical driver models able to represent human drivers' steering behavior in response to automated steering intervention. This article concerns the experimental evaluation of a game-theoretic driver steering control model. The driver model centers on a steering control strategy developed based on the Nash equilibrium of a theoretic noncooperative game between the driver and automated steering controller. The key parameters of the game-theoretic driver model are identified by fitting the model to real driver steering behavior measured from six driver subjects in an experiment using a driving simulator. The game-theoretic driver model is evaluated by compared to a ``conventional'' optimal-control-theoretic driver model, and analyzing their model fitting errors. Results from the analysis demonstrate that the game-theoretic driver model is statistically significantly better than the conventional driver model for representing three out of the six subjects' steering behavior. For the other three subjects, both the two models perform statistically equivalently well.

Ground vehicle lane-keeping assistance system via differential flatness output feedback control and algebraic derivative estimation

Vehicle run-off-road is one of the most frequent and fatal traffic accidents in the United States. Various lane-keeping assistance (LKA) systems have been developed in the last decade to help drivers stay on the road. Most of them are built upon linear driver–vehicle–road (DVR) models and treat road curvature as a disturbance. Albeit effective, their control performance would degrade if road curvature varies rapidly. This paper proposes a novel nonlinear DVR model by integrating a driver steering model into vehicle–road kinematics, which explicitly considers road curvature. Particularly, this nonlinear DVR system has been proven to be differentially flat, and a flatness-based LKA system is designed. Additionally, Model-Free Control is introduced to compensate for system modeling errors. Hardware-in-the-loop simulations and diver-in-the-loop experiments validate the proposed control framework and demonstrate the performance enhancement with respect to a representative linear robust LKA system.

Design and Evaluation of Lane-Change Collision Avoidance Systems in Semi-Automated Driving

Hands on the steering wheel partial driving automation, in which the system controls the lateral and longitudinal vehicle motion while the driver holds the steering wheel, monitors the roadway, and intervenes when necessary, is an example of shared control driving. To insure mutual safety in shared control driving, the system also needs to guide the driver's interventions to avoid hazardous actions, such as risky lane changes. This study proposes three steering interventions that activate automatically when the system detects a lane change by monitoring the steering wheel angle inputted by the driver and road section and determining that the two vehicles are on a collision course. A driving simulation experiment with 80 drivers was conducted to compare four conditions: 1) no steering intervention; 2) a haptic steering intervention that increases stiffness against steering toward the lane where a collision may occur; 3) an automatic steering intervention that decouples the driver's steering input and autonomously steered the vehicle away from the hazard; and 4) multiple steering interventions that generate different degrees of haptic and automatic steering interventions based on the time headway and time-to-collision between vehicles. Analysis of driving performance and safety under conditions with and without steering interventions indicate that all participants, at some points, initiated lane changes that are likely to result in a crash during partial driving automation. However, the three interventions effectively reduced lane-change collisions compared to the baseline. While the automatic steering intervention avoided all collisions, the multiple steering interventions were determined to provide sufficient safety while being more thoroughly accepted by the drivers than the haptic and automatic interventions separately. These findings have implications for developing adaptive collision avoidance systems considering user preference and driving performance.

Modeling driver steering behavior in restricted-preview boundary-avoidance tasks

In the design of human-like steering support systems, driver models are essential for matching the supporting automation's behavior to that of the human driver. However, current driver models are very limited in capturing the driver's adaptation to key task variables such as road width and visibility (i.e., ‘preview’ of the road ahead). This paper uses a recently proposed, novel control-theoretical model for centerline tracking to investigate driver steering in lane-keeping tasks with restricted and unrestricted preview, in an attempt to substantially extend this model's validity. Using data from a tailored driving simulator experiment, three driver control loops (feedforward, heading and position feedback) are separately quantified using system identification techniques. The results show that when preview is restricted, drivers use all of the remaining preview to anticipate the curves of the road ahead, and are no longer able to ‘smooth’ tight curves in the road trajectory (i.e., corner cutting). When sufficient preview and lane width are available, the time to line crossing increases, and steering behavior is less aggressive and more intermittent, or more ‘satisficing’. The novel driver steering model captures these adaptations very well (over 95% of the steering actions) and can thereby be instrumental in realizing human-like steering automation and support systems.

Data-Driven Feedforward Compensation Tuning in Torque Vectoring Control

In automotive control, torque vectoring enhances a vehicle dynamical characteristic by independently allocating wheel torques. Torque vectoring algorithms are often based on some form of closed-loop yaw rate tracking, whose reference is generated according to the driver's steering input. Thus, unless the vehicle is equipped with steer-by-wire, from the torque vectoring point of view, the driver's input simultaneously acts as a reference and as a disturbance. This paper presents a method to explicitly consider this aspect in the torque vectoring design by means of a feedforward compensation. At first, we identify the plant model highlighting the effects of the driver input steer on the system. Then, we design the compensation term with H∞ techniques based on the identified model. This solution is then refined using a Data-Driven approach based on a Bayesian optimization algorithm. Results show that the compensation term can reduce the tracking error by 40% and the control effort by 35%.

Fault-tolerant human-machine shared scheme for collision avoidance of intelligent vehicles considering driver error and actuator fault

This paper presents a fault-tolerant human-machine shared scheme for collision avoidance considering driver error and actuator fault. A fault-tolerant method of trajectory planning and decision-making is developed for dealing with the actuator fault. We calculate the remainder trajectory tracking capability of the faulted actuator and utilise the motion redundancy in the system to generate feasible fault-tolerant trajectories for collision avoidance. In addition, an assessment criterion of driver error is conducted to supervise the driver's steering response to the potential collision and actuator fault. The shared system dynamically adjusts the driving authority based on the driver error. A model predictive control (MPC) algorithm is employed for tracking the fault-tolerant trajectory. Simulation and hardware-in-the-loop (HIL) experiment results demonstrate the effectiveness of the presented approach, reducing traffic accidents due to driver error and actuator fault.

Driver-automation shared steering control considering driver neuromuscular delay characteristics based on stackelberg game

To promote the intelligent vehicle safety and reduce the driver steering workload, stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteristics into account. First, a shared steering control framework with adjustable driving weight is proposed, and a coupling interaction model considering the driver neuromuscular delay characteristics is constructed by using the stackelberg game theory. Moreover, the driver-automation optimal control strategy is deduced theoretically when the game equilibrium is reached. Finally, simulation and virtual driving tests are carried out to verify the superiority of the proposed method. The results illustrate that the raised method can enhance the vehicle safety with low driving weight intervention, and it can achieve better auxiliary effect with less control cost. In addition, the driver-in-the-loop test results show that the proposed strategy can achieve better performance in assisting drivers with low driving skills.

Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle

The plug-in hybrid electric vehicle (PHEV) stands out among hybrid vehicles due to the ability to recharge the battery from an external source and improve vehicle performance and efficiency by increasing fuel economy and reducing emissions. Due to the use of two power sources, a power management strategy must be determined to ensure the operation of both systems achieves higher global efficiency. Therefore, this paper presents three fuzzy controllers. Two controllers act hierarchically to perform the power management, and the third controller is responsible for performing the gear shift. A multi-objective optimization based on the particle swarm algorithm is proposed to reduce the driver's action on the steering angle, minimize the mass of electrical components (electric motors and battery), the equivalent fuel consumption, and exhaust emissions. To do this, the electric motor torque curve, battery capacity and voltage, fuzzy controller parameters are optimized, and the best gearbox and mechanical differential configuration are determined. The best trade-off solution, when compared to the conventional vehicle, was able to improve the handling performance with a 71.9% reduction in the driver steering action, decrease fuel consumption by 18.44%, and reduce pollutant emissions by 69.34% of CO, 20.33% of HC, and 22.11% of NOx.

A Mixed L1/H2 Robust Observer With An Application To Driver Steering Torque Estimation for Autopilot-Human Shared Steering

Abstract Driver's steering torque is an essential signal for automated and assistance driving systems to interpret the human driver's intention. In a steer-by-wire (SBW) apparatus, due to the inevitable couplings between the driver steering, electrical motor, and viscous frictional torques, such a vital signal cannot be directly measured via a torque transducer. Instead, model-based estimators are considered a more practicable route. Toward this end, this paper introduces a novel mixed L1/H2 observer to achieve a robust estimation respecting the human driver's steering torque in human-alone and human-automation shared driving. On the one hand, the L1 induced norm from the process disturbance to the estimation error is attenuated. On the other hand, the H2 synthesis seeks to suppress the effects of measurement noises from the steering angle encoder and the motor's current sensor. Driving simulator human subject experiments are effectuated to justify the proposed strategy and manifest its superiorities over a benchmark method.

Algebraic Driver Steering Model Parameter Identification

AbstractModeling driver steering behavior plays an ever-important role in nowadays automotive dynamics and control applications. Especially, understanding individuals' steering characteristics enables the advanced driver assistance systems (ADAS) to adapt to particular drivers, which provides enhanced protection while mitigating human–machine conflict. Driver-adaptive ADAS requires identifying the parameters inside a driver steering model in real-time to account for driving characteristics variations caused by weather, lighting, road, or driver physiological conditions. Usually, recursive least squares (RLS) and Kalman filter are employed to update the driver steering model parameters online. However, because of their asymptotical nature, the convergence speed of the identified parameters could be slow. In contrast, this paper adopts a purely algebraic perspective to identify parameters of a driver steering model, which can achieve parameter identification within a short period. To verify the proposed method, we first apply synthetic driver steering data to show its superior performance over an RLS identifier in identifying constant model parameters, i.e., feedback steering gain, feedforward steering gain, preview time, and first-order neuromuscular lag. Then, we utilize real measurement data from human subject driving simulator experiments to illustrate how the time-varying feedback and feedforward steering gains can be updated online via the algebraic method.

Research on lateral control of autonomous vehicle based on driver steering model

Path tracking is an important issue in autonomous driving research. In this paper, based on the vehicle two-degree-of-freedom linear dynamics model for path tracking, the study of vehicle path tracking is carried out by applying model predictive control. A linear state space model of the vehicle is established based on the linear two-degree-of-freedom dynamics model, which provides the basis for the path tracking research. Based on the established state space model and the model predictive control method, the path tracking control algorithm is established using the preview point of driver steering model instead of the prediction part in the traditional model predictive control algorithm, which reduces the complexity of the algorithm and greatly improves the real-time performance. Finally, the effectiveness of the algorithm is verified by the joint software simulation of CarSim and Simulink.

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi Task Time Series Transformer

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi Task Time Series Transformer

Robust LPV control synthesis for learning-aided driver assistance systems

This paper proposes a control design framework with guarantees for systems, which contains learning-based control elements. The framework is based on a supervisory control structure, which contains a supervisor, a robust Linear Parameter Varying (LPV) controller and the learning-based control elements. This paper presents the design of a lateral path following control for driver assistance systems, which is aided with a learning-based agent. In the paper the formulation of the supervisor, the design method of the robust LPV controller are provided, while the learning-based agent via an imitation learning process is considered to be given. The effectiveness of the method on driver-in-the-loop simulation scenario is demonstrated. It is shown that the proposed control system is able to provide guarantee on the limitation of the path following error, and to guarantee transition between the driver steering actuation and the automated control intervention.