Driver Steering Research Articles

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.

Indirect Shared Control for Cooperative Driving Between Driver and Automation in Steer-by-Wire Vehicles

It is widely acknowledged that drivers should remain in the control loop before automated vehicles completely meet real-world operational conditions. This paper presents an “indirect shared control” framework for steer-by-wire vehicles, which allows the control authority to be continuously shared between the driver and automation through an weighted-input-summation method. A “best-response” driver steering model based on model predictive control (MPC) for indirect shared control is proposed. Unlike any conventional driver model for manual driving, this model assumes that drivers can learn and incorporate the controller strategy into their internal model for predictive path following. The analytic solution to the driver model is provided to enable off-line simulations. A driving-simulator experiment was conducted to demonstrate the advantages of the indirect shared control system in a highway lane-keeping task. The result showed that the proposed indirect shared control method was effective to improve the subjects’ lane-keeping performance and reduce steering control effort. The proposed driver steering model was also validated by the experiment data, which produced a smaller prediction error than the conventional MPC driver model.

A robust H∞-based steering assistance system for the wheeled tractor.

Agricultural machine automatic navigation poses great challenge to the precise agricultural technology system nowadays. To this end, this paper proposes a novel steering assistance system (SAS) to assist drivers in the path-tracking. First, the driver steering model is investigated through the driver simulator tests. Combining the wheeled tractor kinematics model, a driver-vehicle model is developed. Then, a polytopic linear parameter-varying (LPV) system is adopted to describe the uncertainties, including time-varying driver model parameters and velocity, in the model, based on which an output-feedback robust controller is developed to ensure robust stability within the polytope space. Moreover, a regional pole placement method is adopted to improve the transient performance of the system. Finally, driver-in-the-loop and field tests conducted to value the controller. The results show the effectiveness of the proposed method to improve the path-tracking performance for the agricultural machine navigation, while reducing the physical and mental workload of drivers. This control method is expected to be a paradigm for the precise navigation system of the agricultural machinery.

Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle

The four-wheel independent-drive electric vehicle (FWID-EV) stands out among the electrically-propelled vehicles due to its higher efficiency. This EV configuration faces an energy management challenge, needing a controller to define the best power split among the motors, decreasing the energy consumption while keeping the EV performance. Aiming to overcome this challenge, this paper presents a fuzzy logic control (FLC) strategy to perform the power split among the electric motors to improve vehicle energy efficiency and dynamic performance under real driving conditions. The implemented controller is developed hierarchically, ensuring the speed follows a driving cycle and acting as an electronic differential to correct the vehicle trajectory along the path. Multi-objective optimization is presented using a particle swarm algorithm (MOPSO) to minimize the mass of the electrical components (motors and battery) and extend the driving range throu gh the maximization of the battery state of charge (SoC). Moreover, the optimization process also aims to enhance vehicle handling by minimizing the driver steering input during the cycle. The best trade-off solution was able to reach a 124.2 km drive range with a 235.8 kg battery (387.8 V and 91.2 Ah), presenting improved handling performance with 78.5% decrease in the driver steering action.

Robust fault-tolerant control for four-wheel individually actuated electric vehicle considering driver steering characteristics

A robust fault-tolerant control scheme for distributed actuated electric vehicles is proposed to maintain vehicle stability suffering actuator faults while considering the driver personality differences. The proposed scheme integrates the cooperative game and terminal sliding mode control into the framework of the feedback linearization method (FLM). Firstly, the nonlinearities of the driver-vehicle system are treated by the knowledge of Lie derivative, and then a set of controllable virtual subsystems is obtained through diffeomorphism. To achieve multi-objective cooperation, the interaction framework of virtual subsystems is modeled based on cooperative game theory, which provides a basic feedback control scheme (BFCS). Finally, a terminal sliding mode technology-based active compensation control scheme is integrated into BFCS to handle the systemic disturbances caused by actuator faults. An implementation of hardware-in-the-loop verifies that the stability of the vehicle under the control of the developed approach can be guaranteed for different drivers and different fault types.

Two Nash-equilibrium-based steering control models for representing a driver’s interaction with vehicle automated steering

ABSTRACT Automated steering technology offers significant benefits to the safety of vehicles, but desire to keep the human driver in the loop requires a better understanding of the interaction between driver and vehicle. An existing noncooperative–game–theoretic framework for modelling such interaction is revisited, leading to the development of two driver steering control models. Both bear Nash–equilibrium properties, but involve different assumptions about driver steering behaviour. A simulation study is performed to demonstrate the difference between the two driver models. An experiment using a fixed–base driving simulator is conducted to measure six test subjects' steering angles in response to the lane–change manoeuvres generated by an automated steering controller. The two driver models' capabilities for representing driver steering behaviour are investigated through fitting them to measured driver steering angles. Key model parameters are identified using a system identification procedure. It is found that the two driver models have equivalent capability in capturing the trend of the six test subjects' measured steering angles, but less good at reproducing the overshoot and oscillation involved in two subjects' steering angles. It is found that the inclusion of an arm neuromuscular system model can improve the performance of the proposed driver models.

Extraction of optimal fatigue‐driving steering indicators considering individual differences

Extracting effective steering indicators is critical for fatigue-identification based on steering behaviour. However, considering individual differences, the best calculation parameters of the individual driver's steering indicators are different. The indicator's performance decreases by using unified parameters rather than the individual driver's best parameters. The authors propose a model extracting individual driver's optimal fatigue steering indicators calculated by the individual driver's best parameters. Individual driver's naturalistic driving data are analysed by the model. First, indicators in sober and fatigue state are examined by the Wilcoxon test, and |Z| represents the indicator's fatigue-identification performance. And the function reflecting correspondences between calculation parameters and indicator's fatigue-identification performance is constructed. Then the function is optimized using particle swarm optimization to obtain individual driver's best calculation parameters maximizing |Z|. Finally, effective indicators calculated by the individual driver's best parameters constitute the individual driver's optimal fatigue indicator set. Indicators calculated by the individual driver's best parameters and unified parameters are used to establish the individual fatigue-identification model. The average identification accuracy of models using individual driver's best parameters and unified parameters is 87.4% and 79.1%, which indicates using individual driver's optimal fatigue indicators can improve identification accuracy. This study can provide references for individualized fatigue-driving identification

Hybrid-Learning-Based Driver Steering Intention Prediction Using Neuromuscular Dynamics

The emerging automated driving technology poses a new challenge to driver-automation collaboration, which requires a mutual understanding between humans and machines through their intention identifications. In this article, oriented by human–machine mutual understanding, a driver steering intention prediction method is proposed to better understand human driver's expectation during driver–vehicle interaction. The steering intention is predicted based on a novel hybrid-learning-based time-series model with deep learning networks. Two different driving modes, namely, both hands and single right-hand driving modes, are studied. Different electromyography signals from the upper limb muscles are collected and used for the steering intention prediction. The relationship between the neuromuscular dynamics and the steering torque is analyzed first. Then, the hybrid-learning-based model is developed to predict both the continuous and discrete steering intentions. The two intention prediction networks share the same temporal pattern exaction layer, which is built with the bidirectional recurrent neural network and long short-term memory cells. The model prediction performance is evaluated with a varied history and prediction horizon to exploit the model capability further. The experimental data are collected from 21 participants of varied ages and driving experience. The results show that the proposed method can achieve a prediction accuracy of around 95% steering under the two driving modes.

A method for fatigue detection based on Driver's steering wheel grip

We propose a simple method to measure a driver's fatigue state by detecting the driver's grip force on the steering wheel while driving. We tested the grip force of 36 drivers on the steering wheel in conscious states (Alert) and fatigue states under actual road driving conditions. Using the Stanford sleepiness scale (SSS), we divided drivers into Alert Group A, fatigue Group A, and fatigue Group B. During 20-min real-road driving trials, we measured the steering wheel grip force, electroencephalogram index (R = (α + θ)/β), and blink frequency of each driver synchronously. We found that ΔF, the difference between the maximum/minimum grip force and the standard deviation of the grip force, σF, for each driver, strongly correlated with the driver's fatigue state. In the fatigue state, both ΔF and σF increased significantly. We examined these force indices using analysis of variance (ANOVA) and validated them against the R-value, blink frequency, and the driver's self-reported fatigue state. Using the grip force in fatigue detection, our method can achieve an overall recognition rate of 86.6% and an individual recognition rate of 88.3%. These results indicate that this method can effectively detect a driver's fatigue state during actual road driving. This new method has several advantages, such as a high signal-to-noise ratio, simple data collection, and no influence on daily driving. Thus, our proposed method may provide a theoretical foundation for the development of fatigue-detecting steering wheels

Roll Stability and Path Tracking Control Strategy Considering Driver in the Loop

A study pointed out that the delay time of the driver's nervous system has a significant effect on the roll stability of the vehicle. However, the existing researches on vehicle rollover prevention control rarely consider the influence of driver factors on vehicle roll stability. Aiming at this problem, a vehicle roll stability and path tracking control strategy considering driver in the loop is proposed to assist different types of drivers. It includes the supervisory decision layer and execution layer. The supervisory decision layer selects the corresponding control mode according to the driver's steering wheel angle change rate, path tracking deviation and vehicle roll stability information. The execution layer includes three modes: human-machine shared steering, active braking and integrated chassis control. The human-machine shared steering and active braking modes assist the driver to improve the roll stability and path tracking accuracy. The integrated chassis control mode is used for the automatic driving of vehicles under emergency conditions. Simulation results show that the proposed control strategy can effectively improve the vehicle roll stability and path tracking accuracy, and reduce the driver's operating burden.

Stability of stand-up type PMV when reproducing dynamic behavior of a driver steering with intention

Stability of stand-up type PMV when reproducing dynamic behavior of a driver steering with intention

Low-cost Electric Bus Stability Enhancement Scheme Based on Fuzzy Torque Vectoring Differentials: Design and Hardware-in-the-loop Test

Abstract In this paper, a fuzzy torque vectoring differential controller is proposed to improve the lateral stability for the two-motor-wheel drive electric buses with low sensor cost and computational cost. In order to know the unmeasured sideslip angle, unscented Kalman filter algorithm is used to accurately estimate the sideslip angle based on a nonlinear electric bus model and the measured yaw rate and lateral acceleration signals. Given the advantages of high computational efficiency and human’s heuristic knowledge, fuzzy rules are newly designed to control the torque vectoring differentials, and thereby to provide an extra yaw moment on the electric bus body to compensate the driver steering aiming at stability enhancement. To validate the effectiveness and efficiency of the proposed fuzzy torque vectoring differential controller, hardware-in-the-loop tests were carried out to evaluate the controller performance in real-time under different driving maneuvers and road conditions. The test results indicate that the fuzzy torque vectoring differential controller can significantly improve the handling and path-following accuracy for the two-motor-wheel drive electric buses.

Effect of Inner and Outer Wheels Driving Force Control on Small Electric Vehicle

In this study, in order to improve steering stability during turning, we devised an inner and outer wheel driving force control system that is based on the steering angle and steering angular velocity, and verified its effectiveness via running tests. In the driving force control system based on steering angle, the inner wheel driving force is weakened in proportion to the steering angle during a turn, and the difference in driving force is applied to the inner and outer wheels by strengthening the outer wheel driving force. In the driving force control (based on steering angular velocity), the value obtained by multiplying the driving force constant and the steering angular velocity, that differentiates the driver steering input during turning output as the driving force of the inner and outer wheels. By controlling the driving force of the inner and outer wheels, it reduces the maximum steering angle by 40 deg and it became possible to improve the cornering marginal performance and improve the steering stability at the J-turn. In the pylon slalom it reduces the maximum steering angle by 45 deg and it became possible to improve the responsiveness of the vehicle. Control by steering angle is effective during steady turning, while control by steering angular velocity is effective during sharp turning. The inner and outer wheel driving force control are expected to further improve steering stability.

Theoretical and experimental investigation of driver noncooperative-game steering control behavior

This paper investigates two noncooperative-game strategies which may be used to represent a human driver's steering control behavior in response to vehicle automated steering intervention. The first strategy, namely the Nash strategy is derived based on the assumption that a Nash equilibrium is reached in a noncooperative game of vehicle path-following control involving a driver and a vehicle automated steering controller. The second one, namely the Stackelberg strategy is derived based on the assumption that a Stackelberg equilibrium is reached in a similar context. A simulation study is performed to study the differences between the two proposed noncooperative-game strategies. An experiment using a fixed-base driving simulator is carried out to measure six test driver's steering behavior in response to vehicle automated steering intervention. The Nash strategy is then fitted to measured driver steering wheel angles following a model identification procedure. Control weight parameters involved in the Nash strategy are identified. It is found that the proposed Nash strategy with the identified control weights is capable of representing the trend of measured driver steering behavior and vehicle lateral responses. It is also found that the proposed Nash strategy is superior to the classic driver steering control strategy which has widely been used for modeling driver steering control over the past. A discussion on improving automated steering control using the gained knowledge of driver noncooperative-game steering control behavior was made.

Driver steering assistance for collision avoidance and turning performance optimization by constrained MPC

Driver steering assistance for collision avoidance and turning performance optimization by constrained MPC

A Probabilistic Model for Driving-Style-Recognition-Enabled Driver Steering Behaviors

This article presents a framework to determine driving style and design a driver steering model considering driver characteristics. First, principal component analysis (PCA) and <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-means clustering are utilized to classify 30 participants into cautious, moderate, and aggressive drivers. Subsequently, a generic steering model is established based on the model predictive control method. Thereafter, the maximum lateral acceleration is extracted as a crucial indicator to represent driver characteristics, and it is calibrated through probabilistic models using the dataset, which consists of the classified drivers. Besides, point estimation model and interval estimation model are leveraged to determine driving style and adjust constraints in the stochastic programming-based steering model. Finally, simulation experiments present the variations of actual output trajectories between the aggressive drivers and the cautious drivers.

Driver conflict response during supervised automation: Do hands on wheel matter?

Securing appropriate driver responses to conflicts is essential in automation that is not perfect (because the driver is needed as a fall-back for system limitations and failures). However, this is recognized as a major challenge in the human factors literature. Moreover, in-depth knowledge is lacking regarding mechanisms affecting the driver response process. The first aim of this study was to investigate how driver conflict response while using highly reliable (but not perfect) supervised automation differ for drivers that (a) crash or avoid a conflict object and (b) report high trust or low trust in automation to avoid the conflict object. The second aim was to understand the influence on the driver conflict response of two specific factors: a hands-on-wheel requirement (with vs. without), and the conflict object type (garbage bag vs. stationary vehicle). Seventy-six participants drove with highly reliable but supervised automation for 30 min on a test track. Thereafter they needed to avoid a static object that was revealed by a lead-vehicle cut-out. The driver conflict response was assessed through the response process: timepoints for driver surprise reaction, hands-on-wheel, driver steering, and driver braking. Crashers generally responded later in all actions of the response process compared to non-crashers. In fact, some crashers collided with the conflict object without even putting their hands on the wheel. Driver conflict response was independent of the hands-on-wheel requirement. High-trust drivers generally responded later than the low-trust drivers or not at all, and only high trust drivers crashed. The larger stationary vehicle triggered an earlier surprise reaction compared to the garbage bag, while hands-on-wheel and steering response were similar for the two conflict object types. To conclude, crashing is associated with a delay in all actions of the response process. In addition, driver conflict response does not change with a hands-on-wheel requirement but changes with trust-level and conflict object type. Simply holding the hands on the wheel is not sufficient to prevent collisions or elicit earlier responses. High trust in automation is associated with late response and crashing, whereas low trust is associated with appropriate driver response. A larger conflict object trigger earlier surprise reactions.

Influence of Tire Inflation Pressure on Vehicle Dynamics and Compensation Control on FWID Electric Vehicles

AbstractThis paper presents a comprehensive study revealing the influences of tire pressure variations and their distribution among four tires on vehicle dynamics performance and driver's steering workload for a four wheel independent drive electric vehicle (FWID EV). Then a direct yaw-moment control (DYC) strategy employing an unbiased adaptive model predictive control (MPC) algorithm is proposed to compensate for the influences. An unbiased estimation strategy is developed to update the time-variant prediction model for the adaptive MPC system. An extended magic formula (MF) tire model and a modified Unitire model involving tire inflation pressure are employed to describe the tire longitudinal and lateral forces, respectively. Various tire pressure variations are simulated in CarSim to exhibit the influences of tire inflation pressure, including all four tires at same and different pressures. The unbiased estimation results and compensation effects of the DYC strategy are also verified through simulations. A vehicle dynamics and lateral motion stability index, and driver's steering workload are proposed to quantify the influence of tire pressure variations and distributions. Analyses on the simulation results indicate that: pressure reduction on a front tire and rear tire induces a large steering angle and a large vehicle sideslip angle, respectively; all-tire inflation pressure decrease will increase driver's steering workload. The proposed DYC strategy can achieve satisfactory compensation effects through significantly decreasing the vehicle sideslip angle, steering wheel angle and reducing driver's steering workload by up to 90%.

Rear-Wheel Steering Control for Enhanced Steady-State and Transient Vehicle Handling Characteristics

This paper presents a rear-wheel steering (RWS) control algorithm to enhance the vehicle handling performance without prior knowledge of tire characteristics. A RWS system is a chassis control module that can effectively improve vehicle maneuverability and stability. Since the tire-road friction coefficient is difficult to obtain in real world application, the proposed RWS control algorithm is designed so that it can be implemented without any tire-road information. The proposed RWS control algorithm consists of steady-state and transient control inputs. The steady-state control input is proportional to the driver's steering input for achieving the desired yaw rate gain. The desired yaw rate gain is obtained through an offline optimization that is aimed to minimize the vehicle lateral velocity. The transient control input consists of feedforward and feedback control inputs. The feedforward input is designed to improve transient responses of the yaw rate. Computer simulation studies have shown that a trade-off relationship between overshoot and response time exists when the RWS control input is a sum of the steady-state and feedforward inputs. To compromise this conflict, a feedback input has been designed. The overshoot can be significantly reduced while the response time is slightly changed via the feedback input. The proposed algorithm has been investigated via computer simulations. The simulation has been conducted for step steer and sine with dwell scenarios under various road friction conditions. The performance of a RWS vehicle was evaluated using objective indices. Simulation results show that the proposed algorithm enhances vehicle handling performance.

A comparison of test manoeuvres for determining rearward amplification of articulated heavy vehicles

Rearward amplification (RA) is an effective indicator of lateral stability for multi-trailer articulated heavy vehicles (MTAHVs). The International Organization for Standardization released the test manoeuvres, ISO-14791, for determining the indicator for MTAHVs. ISO-14791 recommends three methods, including two time-domain and one frequency-domain, to derive the RA measures. It was reported that the results from the three methods were not in good agreement. To explore this inconsistency among these methods, a multiple cycle sine-wave steering input (MCSSI) manoeuvre was simulated to obtain steady-state responses of MTAHVs. Furthermore, an automated frequency response method (AFRM) was used to derive the measures in the frequency-domain. This paper presents simulation results based on an A-Train Double. Results demonstrate that the steady-state RA measures under a MCSSI manoeuvre are in excellent agreement with those from frequency-domain methods. It is revealed that driver's steering behaviours impose a non-negligible impact on the transient RA measures of MTAHVs.