Preview Driver Research Articles

Robust path-tracking control for intelligent vehicles considering human–vehicle multi-factors

Human–machine co-driving refers to a technological architecture in which human driver and automated system control vehicle sharing and cooperate to accomplish driving tasks. The architecture could solve the safety and ethical dilemmas of autonomous vehicles, which is an innovative method to improve vehicle intelligence. To realize high-precision and safe path-tracking control, this paper proposes a parallel steering sharing human–machine co-driving framework based on constraint-following controller and driving weight allocation module. First, a parallel human–machine co-driving control framework is established, which uses a two-point preview driver model and constraint-following controller to represent the human driver and automated system, respectively. Second, the underactuated characteristics of front steering vehicles are analyzed. A multiple-constraint path-tracking robust controller is designed based on constraint-following control approach, which considers the multi-source uncertainty and multiple constraints of real road conditions. Third, a multi-factor driving weight allocation module is constructed, which considers human–machine characteristics and cooperation performance. Finally, the effectiveness of the proposed method is verified by CarSim–MATLAB/Simulink joint simulation under three different case studies. This paper creatively synthesizes the vehicle uncertainty, multiple path-tracking constraints, and human–machine cooperation performance, and provides a new idea for human–machine co-driving vehicles to realize robust and safe path-tracking control.

Three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicle

This article proposes a three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicles (AHVs). Although AHVs have extremely high road transportation efficiency, they have poor driving stability and high accident risk during high-speed transportation due to their large weight, high center of mass and multi-body characteristics. However, there are few studies on intelligent driving control of AHVs. In order to improve the active safety performance of AHVs, a three-point adaptive preview driver model is designed in this article. Based on the driver model, a sliding mode steady-state compensation controller is added. The three-point adaptive preview driver is different from other driver models. Its characteristic is that the preview distance can be automatically adjusted according to the vehicle speed and road curvature. A driver model with three-point preview weighted deviation, preview time, current vehicle speed and vehicle state as input and front wheel angle as output is established. Considering the trailer state, the front wheel angle is compensated and corrected by sliding mode steady-state compensation controller. The simulation results show that the model can reasonably judge the relationship between the target path and the vehicle position, adapt to the dynamic adjustment mechanism of the preview distance, and has good tracking accuracy. The front wheel compensation angle provided by the sliding mode controller under high speed and extreme conditions can ensure that the AHVs can track the target trajectory while maintaining driving stability.

A preview SMC driver modeling and simulation for the B-train doubles

B-train doubles have existed a greater risk of causing accidents. The research on the driver model for combination vehicles is crucial to the active safety control of B-train doubles. The direction control of the driver model forms an invaluable part of the closed-loop simulation of the human-vehicle-road system. Meanwhile, studies of driver models for long combination vehicles (LCVs) are limited. Driver modeling for the articulated heavy vehicle (AHV), a preview driver model for B-train doubles, has been proposed, on the basis of Sliding Mode Control (SMC), considering the low-speed path-tracking and high-speed stability, with the constant speed approach rate and optimal approach rate of the SMC, respectively. The simulation proved that, the proposed driver model can significantly eliminate the chattering phenomena of the front wheel angle, enhance the lateral stability of the B-train doubles, and decrease in peak magnitude of lateral acceleration and yaw angular velocity, and achieve better path following ability.

A Reliable Robust Control Method for Vehicle Lateral Dynamics With Preview Driver Model

A Reliable Robust Control Method for Vehicle Lateral Dynamics With Preview Driver Model

Dual predictive model adaptive switching control for directional control of tractor semitrailer combinations

In order to ensure that safety and reliability of tractor semitrailer combinations (TSCs) on the road, the human drivers’ steering decisions need to comprehensively consider the trajectories and states of the tractor and semitrailer. For this purpose, a dual predictive model adaptive switching control decision for directional control is proposed. Firstly, a multi-point preview algorithm and a general regression neural network are designed to percept the current local target paths for the tractor and semitrailer. Then, a kinematic predictive model control algorithm for low-speed path tracking control and a dynamic predictive model control algorithm for high-speed path following and lateral stability control are established respectively. In addition, an S-type switching function is introduced to realize smooth switching between the two control algorithms. Finally, the directional control decision in this study is validated by the numerical simulations under different conditions and compared with single-point preview driver and the MPC driver without considering semitrailer. The results show that the proposed approach can accurately track the target path and effectively improve the high-speed lateral stability.

Coordination Control Strategy for Human-Machine Cooperative Steering of Intelligent Vehicles: A Reinforcement Learning Approach

This paper proposes a novel coordination control strategy through reinforcement learning approach for human-machine cooperative steering of intelligent vehicles, so as to realize a more flexible and efficient way for the human driver and the automated driving system to jointly complete the path-following. Firstly, the human-machine cooperative steering is modeled by the optimal preview driver model with adaptive preview time and the MPC lateral tracking model with adaptive prediction horizon and step, as well as the steering system with a realistic dual-motor steering-by-wire structure is also built into the human-machine cooperative steering system. Then, the human-machine coordination control strategy is designed and trained based on two reinforcement learning agents to further optimize the allocation of cooperative steering weights. Finally, the strategy is fully verified in lane departure simulation scenarios and also in real-world Steering-in-the-Loop experiments with real-time controller, which shows that both agents can effectively keep the vehicle with the small lateral and yaw errors in the simulation scenarios, and also can be successfully applied in the realistic human-machine cooperative system to maintain the lateral and yaw errors within a safe range during path-following.

A preview driver model based on sliding-mode and fuzzy control for articulated heavy vehicle

A preview driver model based on sliding-mode and fuzzy control for articulated heavy vehicle

Path Planning on Large Curvature Roads Using Driver-Vehicle-Road System Based on the Kinematic Vehicle Model

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability.

Novel dual‐layer‐oriented strategy for fully automated vehicles’ lane‐keeping system

A novel concept of a dual-layer-oriented control strategy for fully automated vehicles’ lane-keeping system is proposed which consists of an inner controller with a modified preview driver model and an outer cooperative copilot controller. With designed weighting function of displacement error based on a hyperbolic tangent and adjustable preview horizon according to road geometry, the inner controller is supposed to track the lane centreline precisely and efficiently through optimal control framework. The outer controller is specially designed for situations where the vehicle may run out of the lane and cause a collision. Only when the vehicle is at high risk of lane-crossing, the outer controller is activated to guide the vehicle back to lane centreline by exerting proper steering command, which is calculated by solving a model predictive control-based constrained optimisation problem with a designed quadratic cost function. Finally, simulation tests based on CarSim-Matlab joint platform are carried out to verify the proposed strategy. Results demonstrate that the modified preview driver model is able to improve path following performance and the dual-layer-oriented control strategy with less computational burden can effectively prevent the vehicle from crossing lane boundaries.

Trajectory Tracking Control for Four-Wheel Independent Drive Intelligent Vehicle Based on Model Predictive Control

For four-wheel independent drive intelligent vehicle, the Model Predictive Control (MPC) is adopted to design the trajectory tracking controller through active steering and four-wheel independent drive/brake. The control objective is to follow a desired trajectory while taking into account the control actuator constraints and vehicle dynamic stability constraints. To reduce the computational complexity, a linear time-varying model predictive controller is designed to linearize the nonlinear vehicle model locally at each sampling point. The co-simulation of CarSim/Simulink shows that the designed controller has high tracking accuracy on the basis of ensuring vehicle stability and strong robustness to vehicle velocity and road adhesion coefficient. The trajectory tracking accuracy based on MPC is better than that of the preview driver model (PDM).

A Unified Lateral Preview Driver Model for Road Vehicles

This paper presents a unified lateral preview driver model for closed-loop dynamic simulations of road vehicles. Numerous driver models have been proposed for Single-Unit Vehicles (SUVs). Some SUV-based driver models have been applied to closed-loop simulations of Multi-Trailer Articulated Heavy Vehicles (MTAHVs). However, the dynamics of MTAHVs is significantly different from that of SUVs, and drivers of Multi-Unit Vehicles (MUVs) have different driving performance and skills. Very few driver models have been proposed for closed-loop simulations of MUVs. This paper designs the unified driver model, considering the dynamic features of both SUVs and MUVs. The driver model is derived using a sliding mode control (SMC) technique, and it distinguishes itself from conventional driver models with a number of features. Simulations demonstrate the applicability and effectiveness of the proposed driver model.

A direct multiple shooting method to improve vehicle handling and stability for four hub-wheel-drive electric vehicle during regenerative braking

Regenerative braking is an important technology to improve fuel economy for electric vehicles. Apart from improving energy recovery efficiency and vehicle stability, the arithmetic speed of the algorithm is also essential for an automotive-qualified micro control units. This paper presents a direct multiple shooting method–based algorithm to achieve multiple objectives for four hub-wheel-drive electric vehicle during mild braking situations. Mathematical models of the system are generated for numerical simulations in MATLAB, including a vehicle dynamics model, a modified tire model, a single-point preview driver model, and a regenerative braking motor efficiency map. With the limitation of hard constraint and minimization of adjustment rate in cost function, optimization tends to be accomplished by distribution of braking torque in front and rear wheels. Furthermore, the control strategy has been realized using a direct multiple shooting method to convert the nonlinear optimal control problem to a nonlinear programming problem, which will be settled by adopting a sequential quadratic programming method in each subintervals. The effectiveness and adaptation of the control strategy for four hub-wheel-drive electric vehicle has been evaluated by conducting many simulations during mild braking situations, and the simulation results also demonstrated that the direct multiple shooting–based strategy exhibits a better performance than that of proportional-integral-based or nonlinear model predictive control–based controller.

Development of a neuromuscular driver model with an estimation of steering torque feedback in vehicle steer-by-wire systems

In this paper, a neuromuscular driver model for sensing torque feedback or haptic interaction between the vehicle equipped with steer-by-wire (SBW) system and the driver has been developed. The proposed driver model consists of a preview model and a neuromuscular model. The preview driver model calculates the desired angle of the steering-wheel to follow the path, and the neuromuscular driver model, with the ability of perceiving real-time torque feedback, determines the real angle of the steering-wheel angle according to muscular system transfer functions to follow the desired steering-wheel angle. In order to calculate torques on the steering-wheel, the lateral tyre-road forces are estimated by Kalman filter designed using a linear 2-DOF vehicle model. So, the design of the neuromuscular driver model combined with torque feedback estimation is the main contribution of this paper. The simulation results from TruckSim and Simulink software indicate that the novel designed driver model with torque feedback estimation has an important role in the controlling and steering vehicle to follow the desired paths.

Model predictive driver model considering the steering characteristics of the skilled drivers

First, the experienced drivers with good driving skills are used as objects of learning and road steering test data of skilled drivers are collected in this article. To better simulate human drivers, skilled drivers’ steering characteristics are analyzed under different steering conditions. Vehicle trajectories of skilled drivers are fitted by general regression neural network, and the ideal path trajectory is obtained. Second, the model predictive control algorithm is used to build the driver model. According to the requirements of quickly and steadily tracking the track of skilled drivers, vehicle kinematics model is established. The objective function and the corresponding constraint conditions of the driver model based on model predictive control were determined. Finally, numerical simulations results demonstrate that the driver model based on model predictive control can accurately track the reference trajectory of skilled drivers under the four typical steering conditions, and the tracking effect is better than the traditional single-point preview driver model and path tracking method based on a β-spline curve.

A Fractional Derivative-Based Lateral Preview Driver Model for Autonomous Automobile Path Tracking

The concept of focus point preview is proposed, and fractional calculus is introduced to driver model to build focus point preview driver model. A formula for calculating lateral error is given, where the weight coefficients of fractional calculus are designed to imitate the driver’s focus preview property. The relationship between the speed and the order of fractional calculus is studied. A driver-vehicle-road simulation system is set up to illustrate the performances of the proposed preview model. The S-type road is used to test the model in the case of continuous small curvature turns and the Shanghai F1 track model is used as the case that automobile passing large curvature curve. The performances are evaluated from two aspects: path tracking effect and vehicle dynamic responses. It is concluded that, as the speed of the vehicle increases, the optimal order of fractional integral increases, so that the order is seen as the degree of driver’s attention. What is more, in the case of large curvature, the path tracking performance is improved by increasing the corresponding fractional order. Simulations results also show that, compared with the single-point preview model, the performances of the focus point preview model are better. On the one hand, the proposed driver model can be used to control the vehicle steering and path tracking. On the other hand, fractional calculus is used to reveal the driver preview property and the order is given a certain physical meaning, which is conducive to the development of fractional calculus applications.

Investigation on three-directional dynamic interaction between a heavy-duty vehicle and a curved bridge

Considering the nonlinear property of suspension damping and tire stiffness, a full-vehicle model is built for a heavy-duty truck. A modified preview driver model with nonlinear time delay is inserted into the vehicle model to compute the suitable steering angle of the front wheel and to make the vehicle follow the required route. Next, the finite element model of a five-span continuous curved highway bridge is established, and the bridge’s inherent frequencies and modes are obtained. The curved bridge and the vehicle are coupled by three-directional tire forces, and a three-directional driver–vehicle–bridge interaction model is presented. The presented vehicle model and bridge model are verified by comparing with the published works. The dynamic impact factors of vertical, lateral, and torsional displacements of the bridge are calculated when a vehicle is traversing through the bridge, and the impact factors’ distributions along the bridge are analyzed. The effects of vehicle driving conditions on impact factors are also researched. It is found that the impact factor calculated from the present specification for a straight bridge is smaller than that from the three-directional driver–vehicle–bridge interaction model, and the vertical and torsional impact effects at the third span midpoint are greater than the lateral impact effect.

Design of an improved predictive LTR for rollover warning systems

Rollover warning system predicts whether the vehicle has a risk of rollover in a period of time based on the current driving state of the vehicle. Most previous studies on rollover evaluation index have only used vehicle’s current states and often ignored the judgment of the overall tendency of the motion. In the calculation of the rollover early warning algorithm, it is assumed that the vehicle input was known. Based on the above reasons, this paper introduces the derivative of LTR into the rollover index, using an improved predictive LTR (IPLTR) as the rollover index, and realized the real-time calculation through an 8-DOF nonlinear vehicle model. The vehicle rollover prediction system is achieved through the preview driver model and the rollover prediction index calculation model. To achieve a proper preview time for the driver model, a comprehensive evaluation formula that considers the trajectory deviation, the steering wheel rotation speed and lateral acceleration is used. Finally, an IPLTR is developed, which utilizes a driver model to predict the steering angle and several other sensor signals available from the electronic stability control system as inputs. The new IPLTR index can provide a time-advanced measure of rollover propensity and its effectiveness is verified by virtual tests and experimental test data. Simulation results by double lane change (DLC) and “Highway Merge” road maneuvers show that the proposed rollover warning algorithm is rather satisfying. Experimental test by pylon course slalom maneuver also proves its effectiveness and has more advantages of such a system in rollover prevention.

Design of a Preview Driver Model Based on Optical Flow

Automated driving systems have been released in recent years to improve the active safety. In this research field, a preview/predictive driver model has been widely used as a steering controller and for driver behavior analysis. This driver model has been implemented by many researchers and, through the vehicle simulations and experiments, has been shown to reproduce driver behavior, such as the driver's steering and handle torque. On the other hand, optical flow is a part of visual information, and in our previous works we focused on it as the index used in human locomotor behavior. Humans are known to perceive the direction of their self-motion from optical flow and utilize it during driving. In experimental applications of the optical flow model to an in-vehicle system, the system was shown to be effective in practical use. In particular, the system can express the same driver behavior as the preview driver model. In this study, the preview driver model was analyzed from the viewpoint of optical flow to understand the preview driver model's interpretation of human behavior. In addition, in this paper a new preview driver model that utilizes our knowledge of optical flow is proposed. This model shows a better control performance and ride quality than the conventional preview driver model. As a result, this approach serves as a foundation of the preview driver model.

Driver steering model for closed-loop steering function analysis

In this paper, a two level preview driver steering control model for the use in numerical vehicle dynamics simulation is introduced. The proposed model is composed of cascaded control loops: The outer loop is the path following layer based on potential field framework. The inner loop tries to capture the driver's physical behaviour. The proposed driver model allows easy implementation of different driving situations to simulate a wide range of different driver types, moods and vehicle types. The expediency of the proposed driver model is shown with the help of developed driver steering assist (DSA) function integrated with a conventional series production (Electric Power steering System with rack assist servo unit) system. With the help of the DSA assist function, the driver is prevented from over saturating the front tyre forces and loss of stability and controllability during cornering. The simulation results show different driver reactions caused by the change in the parameters or properties of the proposed driver model if the DSA assist function is activated. Thus, the proposed driver model is useful for the advanced driver steering and vehicle stability assist function evaluation in the early stage of vehicle dynamics handling and stability evaluation.

Driver Steering Control and Full Vehicle Dynamics Study Based on a Nonlinear Three-Directional Coupled Heavy-Duty Vehicle Model

Under complicated driving situations, such as cornering brake, lane change, or barrier avoidance, the vertical, lateral, and longitudinal dynamics of a vehicle are coupled and interacted obviously. This work aims to propose the suitable vehicle and driver models for researching full vehicle dynamics in complicated conditions. A nonlinear three-directional coupled lumped parameters (TCLP) model of a heavy-duty vehicle considering the nonlinearity of suspension damping and tire stiffness is built firstly. Then a modified preview driver model with nonlinear time delay is proposed and connected to the TCLP model to form a driver-vehicle closed-loop system. The presented driver-vehicle closed-loop system is evaluated during a double-lane change and compared with test data, traditional handling stability vehicle model, linear full vehicle model, and other driver models. The results show that the new driver model has better lane keeping performances than the other two driver models. In addition, the effects of driver model parameters on lane keeping performances, handling stability, ride comfort, and roll stability are discussed. The models and results of this paper are useful to enhance understanding the effects of driver behaviour on full vehicle dynamics.