Measurement and mathematical model of a driver's intermittent compensatory steering control

The compensatory (feedback) component of a human driver's steering control is examined. In particular the effect of the cognitive process is studied. Model predictive control theory is used to implement models of intermittency in cognitive processing. Experiments using a fixed-base driving simulator with periodic occlusion of the visual display are used to reveal the nature of the driver's steering behaviour. An intermittent serial-ballistic control strategy is found to match the measured behaviour better than intermittent zero-order hold or continuous control. The findings may enable some insight to driver-vehicle interaction and vehicle handling qualities.This work was supported by the Engineering and Physical Sciences Research Council (EP/P505445/1); the Qualcomm European Research Studentship Fund in Technology; and the Lotus F1 Team (RG61664).This is the final version of the article. It first appeared from Taylor & Francis via http://dx.doi.org/10.1080/00423114.2015.110074

Stability Control of Triple Trailer Vehicles

While vehicle stability control is a well-established technology in the passenger car realm, it is still an area of active research for commercial vehicles as indicated by the recent notice of proposed rulemaking on commercial vehicle stability by the National Highway Traffic Safety Administration (NHTSA, 2012). The reasons that commercial vehicle electronic stability control (ESC) development has lagged passenger vehicle ESC include the fact that the industry is generally slow to adopt new technologies and that commercial vehicles are far more complex requiring adaptation of existing technology. From the controller theory perspective, current commercial vehicle stability systems are generally passenger car based ESC systems that have been modified to manage additional brakes (axles). They do not monitor the entire vehicle nor do they manage the entire vehicle as a system

Real-Time Vehicle Parameter Estimation and Adaptive Stability Control

This dissertation presents a novel Electronic Stability Control (ESC) strategy that is capable of adapting to changing vehicle mass, tire condition and road surface conditions. The benefits of ESC are well understood with regard to assisting drivers to maintain vehicle control during extreme handling maneuvers or when extreme road conditions such as ice are encountered. However state of the art ESC strategies rely on known and invariable vehicle parameters such as vehicle mass, yaw moment of inertia and tire cornering stiffness coefficients. Such vehicle parameters may change over time, especially in the case of heavy trucks which encounter widely varying load conditions. The objective of this research is to develop an ESC control strategy capable of identifying changes in these critical parameters and adapting the control strategy accordingly. An ESC strategy that is capable of identifying and adapting to changes in vehicle parameters is presented. The ESC system utilizes the same sensors and actuators used on commercially-available ESC systems. A nonlinear reduced-order observer is used to estimate vehicle sideslip and tire slip angles. In addition, lateral forces are estimated providing a real-time estimate of lateral force capability of the tires with respect to slip angle. A recursive least squares estimation algorithm is used to automatically identify tire cornering stiffness coefficients, which in turn provides a real-time indication of axle lateral force saturation and estimation of road/tire coefficient of friction. In addition, the recursive least squares estimation is shown to identify changes in yaw moment of inertia that may occur due to changes in vehicle loading conditions. An algorithm calculates the reduction in yaw moment due to axle saturation and determines an equivalent moment to be generated by differential braking on the opposite axle. A second algorithm uses the slip angle estimates and vehicle states to predict a Time to Saturation (TTS) value of the rear axle and takes appropriate action to prevent vehicle loss of control. Simulation results using a high fidelity vehicle modeled in CarSim show that the ESC strategy provides improved vehicle performance with regard to handling stability and is capable of adapting to the identified changes in vehicle parameters

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

Development of a novel model of driver-vehicle steering control incorporating sensory dynamics

This is the author accepted manuscript. The final version is available from CRC Press via http://dx.doi.org/10.1201/b21185-8A novel model of driver steering control is proposed, incorporating models of the driver’s sensory dynamics and limitations. The model is based on the hypothesis that the driver’s steering strategy minimises an internal cost function optimally based on the noisy, delayed information received from the sensory systems. Published results from experiments carried out on pilots were used to identify parameter values for the new model, and to assess the validity of the new modelling approach. The new model was found to fit the results very well, with variance accounted for (VAF) values greater than 90% for all but one trial. The model was found to fit the results almost as well with a single fixed set of parameter values as with separate parameter values for each trial, indicating that a fixed-parameter model is able to predict variations in control behaviour under different conditions.EPSR

Modelling of a Human Driver’s Interaction with Vehicle Automated Steering using Cooperative Game Theory

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver’s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver’s and the automated steering system’s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver’s and the automated system’s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver’s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver’s steering angle control, compared to the driver’s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter

Model Predictive Control System Design of a passenger car for Valet Parking Scenario

A recent expansion of passenger cars’ automated functions has led to increasingly challenging design problems for the engineers. Among this the development of Automated Valet Parking is the latest addition. The system represents the next evolution of automated system giving the vehicle greater autonomy: the efforts of most automotive OEMs go towards achieving market deployment of such automated function. To this end the focus of each OEM is on taking part to this competitive endeavor and succeed by developing a proprietary solution with the support of hardware and software suppliers. Within this framework the present work aims at developing an effective control strategy for the considered scenarios. In order to reach this goal a Model Predictive Control approach is employed taking advantage of previous works within the automotive OEM in the automated driving field. The control algorithm is developed in a Simulink® simulation according to the requirements of the application and tested; results show the control strategy successfully drives the vehicle on the predefined path

Sensor Fusion and Non-linear MPC controller development studies for Intelligent Autonomous vehicular systems

The demand for safety and fuel efficiency on ground vehicles and advancement in embedded systems created the opportunity to develop Autonomous controller. The present thesis work is three fold and it encompasses all elements that are required to prototype the autonomous intelligent system including simulation, state handling and real time implementation. The Autonomous vehicle operation is mainly dependent upon accurate state estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The initial work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. The previous studies covers the handling of sensor noise data for vehicle yaw estimations and the same approach can be applied for additional sensors used in the work. However, it is important to develop simulations to analyze the autonomous navigation for various road, obstacles and grade conditions. These simulations serve base platform for real time implementation and provide the opportunity to implement it on real road vehicular application and leads to prototype the controller. Therefore, the next section deals with simulations that focuses on developing Non-linear Model Predictive controller for high speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in MATLAB environment. As mentioned, the above two sections provide base platform for real time implementation and the final section uses these techniques for developing intelligent autonomous vehicular system that would track the given path and avoid static obstacles by rejecting the considerable environmental disturbance in the given path. The Linear Quadratic Gaussian (LQG) is developed for the present application, The model developed in the LQG controller is a kinematic bicycle model, that mimics 1/5th scale truck and cubic spline has been used to connect and generate the continuous target path

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Vehicle Crash Avoidance Modelling and Simulation Using Artificial Neural Network Approach

The objectives of this project are to study kinematics of vehicles in crash avoidance maneuvers, to model and simulate vehicles in crash avoidance scenarios in Matlab Simulink environment, and also to develop crash avoidance algorithm utilizing artificial neural network approach. The problem that leads to the development of this project is that accidents happened mostly caused by human error, in which traffic delays and congestion can eventually take place. The project involves a preliminary study on the simulation of changinglane and also merging into highwaytraffic. This project consists of two main components, which represents the method. The first component is whereby studying of vehicle kinematics in crash avoidance maneuvers is done. The second component is the process of modeling and simulation of crash avoidance scenarios in Matlab Simulink environment. Based on the project that is to be done, the accidents caused by lane changing and merging can be avoided through the design of intelligent vehicle and intelligent highway. As a conclusion, the results ofthis paper could be used to investigate on how to improve the safety of lane changingmaneuversand to provide warnings or take evasive actions to avoid collision when combined with appropriate hardware on board vehicles