Steering disturbance rejection using a physics-based neuromusculoskeletal driver model

In this paper, a path tracking control for active rear steering vehicles considering driver steering characteristics is proposed to help drivers track the desired vehicle trajectory in a tailored way. Firstly, the driver model is established, and based on the driver steering data, the driver parameters are identified by the least square method. Based on driver model parameter identification, driver and vehicle models are formed for the MPC controller design. In MPC controller design, global objective function, considering the task performance, vehicle stability, driver's physical workloads, mental workloads and actuator performance, is designed to optimize the overall performance of the driver-vehicle system. On the constraint, the driver operating limit, actuator physical limit and vehicle stability performance limit are also considered to make the controller feasible to in the real practice. Simulation results under different conditions show that the controller can significantly improve the system performance and reduce the driver's workloads.

With the increase in driver assistance systems, driver models are becoming more important to vehicle control, driving safety, and performance. To make these driver assistance systems better cooperate with human drivers, the driver models need to be able to predict human driving behaviors and distinguish among different drivers. In this paper, a combined driver model consisting of a compensatory transfer function and an anticipatory component based on road geometry is integrated with the design of the individual driver’s desired path. The proposed driver model parameters are obtained from human subject test data collected in a driving simulator. It has been shown that the proposed combined driver model is able to replicate each driver’s steering wheel angle signals for a variety of maneuvers at different vehicle speeds. The driver model is then validated by first using a polynomial to interpolate the driver model and desired path parameters for an intermediate speed. It is also validated by comparing two different drivers’ model parameter sets to show that each driver has a unique set of parameters. The final validation is to show that the proposed individual driver’s desired path offers more accurate steering wheel fits than the previous geometric centerline.

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.

Realistic driver models can play an important role in developing new driver assistance technologies. A realistic driver model can reduce the time-consuming trial-and-error process of designing and testing products, and thereby reduce the vehicle's development time and cost. A realistic model should provide both driver path planning and arm motions that are physiologically possible. The interaction forces between a driver's hand and steering wheel can influence control performance and steering feel. The aim of this work is to develop a comprehensive yet practical model of the driver and vehicle. Consequently, a neuromuscular driver model in conjunction with a high-fidelity vehicle model is developed to learn and understand more about the driver's performance and preferences, and their effect on vehicle control and stability. This driver model can provide insights into task performance and energy consumption of the driver, including fatigue and cocontraction dynamics of a steering task. In addition, this driver model in conjunction with a high-fidelity steering model can be used to develop new steering technologies such as electric power steering.

The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple dynamic Bayesian sensory-motor models according to the Bayesian Programming (BP) approach: Bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.

Individual Driver Modeling via Optimal Selection of Steering Primitives

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

This paper proposes a novel method of verifying comprehensive driver model used for the evaluation of driving safety systems, which is achieved by coupling the traffic simulation and the driving simulator (DS). The method consists of three-step procedure. In the first step, an actual driver operates a DS vehicle in the traffic flow controlled by the traffic simulation. Then in the next step, the actual driver is replaced by a driver model and the surrounding vehicle maneuvers are replayed using the recorded data from the first step. Then, the maneuver by the driver model is compared directly with the actual driver's maneuver along the simulation time steps. For example, while turning right (in front of the oncoming traffic), the proposed verification method was applied to our driver-agent model of traffic simulation system ASSTREET (The Advanced Safety System & Traffic REaltime Evaluation Tool) to demonstrate that the proposed method provides a straightforward way of verifying the driver-agent model capability to reproduce the driving behavior including the subtle driving behaviors, such as safety confirmation, that can have significant impact on driving safety. The scheme of interactive connection of ASSTREET and a driving simulator is also described. Language: en

Driver steering assistance to avoid unintended lane departure by lane keeping and steering suggestions

Stimulating Swallowing: Essential Central and Peripheral Nervous System Targets

If the driving behavior of an autonomous vehicle is similar to that of a skilled driver, the human driver can extricate himself from fatigue operation and the comfort of passengers can also be guaranteed. Therefore, this paper studies the human‐like lane‐changing model of an autonomous vehicle. The lane‐changing characteristic data of skilled drivers are collected and analyzed through a real vehicle test. Then, comparing the MPC‐based driver model with the steering wheel angle of human drivers, we found that the MPC‐based model could hardly reflect the maneuvering characteristics of human drivers, so we proposed a driver model with steering wheel angle continuity for human drivers. This paper uses four neural network models to compare the prediction on the test set, then uses different input types to compare the prediction accuracy of the model, and finally verifies the generalization ability of the model on the verification set. These three test results show that the prediction results of the human‐like lane‐changing driving model based on Bi‐LSTM are closest to the real steering wheel angle sequence of skilled drivers. The test results demonstrate that the Bi‐LSTM‐based human‐like lane‐changing driving model achieves 9.8% RMSE and 6.8% MAE, which improves 10.8% RMSE and 10.3% MAE over LSTM. The model can generate the steering wheel angle sequence in the process of lane changing like a human, so as to realize the human simulation control of an autonomous vehicle for lane‐changing conditions.

SUMMARY This investigation is based on a complex 4-wheel vehicle model of a passenger car that includes steering system and drive train. The tyre properties are described for all possible combined longitudinal and lateral slip values and for arbitrary friction conditions. The active part is an additional steering system of all 4 wheels, additionally to the driver's steering wheel angle input. Three control levels are used for the driver model that thereby can follow a given trajectory or avoid an obstacle. The feedback control of the additional 4 wheel steering is based on an observer which can also have adaptive characteristics. Moreover a virtual vehicle model in a feedforward scheme can provide desired steering characteristics. To get information for critical situations a cornering manoeuvre with sudden u-split conditions is simulated. Further a similar manoeuvre is used to evaluate the reentry in a high friction area from low friction conditions. And finally the performance of the controller is shown in a severe lane change manoeuvre.

In order to provide the dual-motor drive electric tracked vehicle with the capacity of steering when braking, analyzed steering dynamics and mainly researched the characteristic of the turning radius, proposed a torque control strategy for steering in braking process. The motor's brake torque of each side is controlled according to inputs signals from driver's steering wheel and braking footplate. Simulation in circumstance of Matlab/Simulink is made and the results show that the strategy can realize the driver's steering intention well, the radius is stable and can be controlled validly by the driver.

<div class="section abstract"><div class="htmlview paragraph">A two-passenger all-wheel-drive urban electric vehicle (AUTO21EV) with four in-wheel motors and an active steering system has been designed and developed at the University of Waterloo. In order to evaluate the handling and performance of such a vehicle in the design stage and analyze the effectiveness of different chassis control systems before implementing them in the real vehicle, the simulation of a large number of different open-loop and closed-loop test maneuvers is necessary. Thus, in the simulation environment, not only is a mathematical vehicle model needed for every test maneuver, but a driver model must also be designed to simulate the closed-loop test maneuvers. The role of the driver model is to calculate the control inputs required to successfully follow a predefined path. Such a driver model can be implemented as an inverse dynamics problem or by a representation of a driver that can look ahead, preview the path, and change the steering wheel angle and acceleration or brake pedal positions accordingly. In this regard, a path-following driver model is developed in this work with an advanced path previewing technique. In addition, a gain scheduling speed control driver model is developed for the AUTO21EV, which adjusts the drive torques of the wheels to minimize the deviation between the desired and actual vehicle speeds.</div></div>

In this paper we address the problem of combining automatic lane keeping and driver's steering for either obstacle avoidance or lane change maneuvers for passing purpose or any other desired maneuvers, through a closed loop control strategy. The automatic lane keeping control loop is never opened, and no on/off switching strategy is used. During the driver's maneuver, the vehicle lateral dynamics is controlled by the driver himself through the vehicle steering system. When there is no driver's steering action, the vehicle center of gravity tracks the center of the travelling lane thanks to the automatic lane keeping system. At the beginning (end) of the maneuver, the lane keeping task is released (resumed) safely and smoothly. Experimental results obtained with the proposed two DOF structure along italian highways are presented.