Application of an Optimal Preview Control for Simulation of Closed-Loop Automobile Driving

An optimal preview control method is applied to the automobile path following problem. The technique is first used to examine the straight-line regulatory driving task and results compared with similar experimental measurements. The method is further demonstrated by closed-loop-simulation of an automobile driver/vehicle system during transient lane-change maneuvers. The computer simulation results are compared with equivalent vehicle test measurements.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65010/1/MacAdam 1981 IEEE driver model.pd

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

The Implementation of Driver Model Based on the Attention Transfer Process

To describe the characteristics of driver’s attention changing with driving environment, establish the relation between driver model parameter and driver’s attention, seek for mapping relation between driver’s behavior and vehicle’s running status data, and provide individualized driver simulation model for unmanned car controller or for driver’s mental state inversion based on vehicle’s running status data, the paper established a driver model based on driver’s attention and deduced the relation between attention intensity and continuous driving time according to the process of driver’s attention change from concentration to distraction and the distribution characteristics of their durations. The relationship between driver’s mental state and manual closed-loop driving model parameters is established according to the transfer rule of attention in the driving course, and it is applied to driver model based on dynamical regulation neural network. Finally the paper researched dynamics evolution characteristics of vehicle running caused by fatigue driving in the environment of double lane change and large curvature, with test result verifying the effectiveness and accuracy of the driver model based on the attention transfer process

A Review of Near-Collision Driver Behavior Models

Objective: This article provides a review of recent models of driver behavior in on-road collision situations. Background: In efforts to improve traffic safety, computer simulation of accident situations holds promise as a valuable tool, for both academia and industry. However, to ensure the validity of simulations, models are needed that accurately capture near-crash driver behavior, as observed in real traffic or driving experiments.<p> Method: Scientific articles were identified by a systematic approach, including extensive database searches. Criteria for inclusion were defined and applied, including the requirement that models should have been previously applied to simulate on-road collision avoidance behavior. Several selected models were implemented and tested in selected scenarios.<p> Results: The reviewed articles were grouped according to a rough taxonomy based on main emphasis, namely avoidance by braking, avoidance by steering, avoidance by a combination of braking and steering, effects of driver states and characteristics on avoidance, and simulation platforms.<p> Conclusion: A large number of near-collision driver behavior models have been proposed. Validation using human driving data has often been limited, but exceptions exist. The research field appears fragmented, but simulation-based comparison indicates that there may be more similarity between models than what is apparent from the model equations. Further comparison of models is recommended.<p> Application: This review provides traffic safety researchers with an overview of the field of driver models for collision situations. Specifically, researchers aiming to develop simulations of on-road collision accident situations can use this review to find suitable starting points for their work

Occupant–vehicle dynamics and the role of the internal model

With the increasing need to reduce time and cost of vehicle development there is increasing advantage in simulating mathematically the dynamic interaction of a vehicle and its occu- pant. The larger design space arising from the introduction of automated vehicles further increases the potential advantage. The aim of the paper is to outline the role of the internal model hypothesis in understanding and modelling occupant-vehicle dynamics, specifically the dynamics associated with direction and speed control of the vehicle. The internal model is the driver’s or passenger’s understanding of the vehicle dynamics and is thought to be employed in the perception, cognition and action processes of the brain. The internal model aids the estimation of the states of the vehicle from noisy sensory measurements. It can also be used to optimise cognitive control action by predicting the consequence of the action; thus model predictive control theory (MPC) provides a foundation for modelling the cognition process. The stretch reflex of the neuromuscular system also makes use of the prediction of the internal model. Extensions to the MPC approach are described which account for: interaction with an automated vehicle; robust control; intermittent control; and cognitive workload. Further work to extend understanding of occupant-vehicle dynamic interaction is outlined. This paper is based on a keynote presentation given by the author to the 13th International Symposium on Advanced Vehicle Control (AVEC) conference held in Munich, September 2016

A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers

In the study of vehicle dynamics and controls, modeling ultra-high performance maneuvers (i.e., minimum-time vehicle maneuvering) is a fascinating problem that explores the boundaries of capabilities for a human controlling a machine. Professional human drivers are still considered the benchmark for controlling a vehicle during these limit handling maneuvers. Different drivers possess unique driving styles, i.e. preferences and tendencies in their local decisions and corresponding inputs to the vehicle. These differences in the driving style among professional drivers or sets of drivers are duly considered in the vehicle development process for component selection and system tuning to push the limits of achievable lap times. This work aims to provide a mathematical framework for modeling driving styles of professional drivers that can then be embedded in the vehicle design and development process. This research is conducted in three separate phases. The first part of this work introduces a cascaded optimization structure that is capable of modeling driving style. Model Predictive Control (MPC) provides a natural framework for modeling the human decision process. In this work, the inner loop of the cascaded structure uses an MPC receding horizon control strategy which is tasked with finding the optimal control inputs (steering, brake, throttle, etc.) over each horizon while minimizing a local cost function. Therein, we extend the typical fixed-cost function to be a blended cost capable of optimizing different objectives. Then, an outer loop finds the objective weights used in each MPC control horizon. It is shown that by varying the driver\u27s objective between key horizons, some of the sub-optimality inherent to the MPC process can be alleviated. In the second phase of this work, we explore existing onboard measurements of professional drivers to compare different driving styles. We outline a novel racing line reconstruction technique rooted in optimal control theory to reconstruct the driving lines for different drivers from a limited set of measurements. It is demonstrated that different drivers can achieve nearly identical lap times while adopting different racing lines. In the final phase of this work, we use our racing line technique and our cascaded optimization framework to fit computable models for different drivers. For this, the outer loop of the cascaded optimization finds the set of objective weights used in each local MPC horizon that best matches simulation to onboard measurements. These driver models will then be used to optimize vehicle design parameters to suit each driving style. It will be shown that different driving styles will yield different parameters that optimize the driver/vehicle system

Identification and validation of a driver steering control model incorporating human sensory dynamics

Most existing models of driver steering control do not consider the driver's sensory dynamics, despite many aspects of human sensory perception having been researched extensively. The authors recently reported development of a driver model that incorporates sensory transfer functions, noise and delays. The present paper reports the experimental identification and validation of this model. An experiment was carried out with five test subjects in a driving simulator, aiming to replicate a real-world driving scenario with no motion scaling. The results of this experiment are used to identify parameter values for the driver model, and the model is found to describe the results of the experiment well. Predicted steering angles match the linear component of measured results with an average `variance accounted for' of 98% using separate parameter sets for each trial, and 93% with a single fixed parameter set. The identified parameter values are compared with results from the literature and are found to be physically plausible, supporting the hypothesis that driver steering control can be predicted using models of human perception and control mechanisms

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Abstract Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends

Understanding and Modeling the Human Driver

This paper examines the role of the human driver as the primary control element within the traditional driver-vehicle system. Lateral and longitudinal control tasks such as path-following, obstacle avoidance, and headway control are examples of steering and braking activities performed by the human driver. Physical limitations as well as various attributes that make the human driver unique and help to characterize human control behavior are described. Example driver models containing such traits and that are commonly used to predict the performance of the combined driver-vehicle system in lateral and longitudinal control tasks are identified.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65021/1/MacAdam_2003 VSD Understanding and Modelling the Driver.pd

Optimal path-tracking of virtual race-cars using gain-scheduled preview control

In the search for a more capable minimum-lap-time-prediction program, the presence of an alternative solution has been introduced, which requires the development of a high-quality path-tracking controller. Preview Discrete Linear Quadratic Regulator (DLQR) theory has been used to generate optimal tracking control gains for a given car model. The calculation of such gains are performed off-line, reducing the computational burden during simulated tracking trials. A simple car model was used to develop limit-tracking control strategies, first for an understeering and then for an oversteering car, travelling at a constant forward speed. Adaptation in the controller, with respect to front-/rear-lateral-slip ratio, facilitated superior tracking performance over the non-adaptive counterpart in a number of challenging tracking manoeuvres. Once complete, development work was focused on the control of a complex car model. Such a model required an extension to the preview DLQR theory, to allow variable speed, two-channel (x,y) optimal path tracking. Significant benefits were observed when using an adaptive control strategy, firstly scheduling with respect to forward ground speed and then including adaptation with respect to mean front-lateral-slip ratio. A variable weighting strategy was used to suppress oscillations in the tracking controller when operating near the limit of the car. Such a strategy places a higher cost on control effort expenditure, relative to tracking error, as the car approaches the limit of the front axle. Further oscillatory behaviour, due to the presence of lightly-damped eigenmodes, was suppressed by increasing the car’s suspension stiffness and damping parameters. The tracking controller, that has resulted from the work documented by this thesis, has demonstrated high-quality tracking when operating in a number of different scenarios, including lateral limit tracking. Variable speed limit tracking is suggested as the next development step, which will then allow the controller to be implemented in initial learning trials. Successful development of the speed and path optimisers in such trials will complete the development of a novel solution to the minimum lap-time problem