Control of vehicle lateral dynamics based on longitudinal wheel forces

Trends show that on board vehicle technology is becoming increasingly complex and that this will continue to be the case. This complexity has enabled both driver assistance systems and fully automatic systems to be introduced. Driver assistance systems include anti-lock braking and yaw rate control, and these differ from fully automatic systems which include collision avoidance systems, where control of the car may be taken away from the driver. With this distinct difference in mind, this work will focus on driver assist based systems, where emerging technology has created an opportunity to try and improve upon the systems which are currently available. This work investigates the ability to simultaneously control a set of two lateral dynamics using primarily the longitudinal wheels forces. This approach will then be integrated with front wheel steering control to assess if any benefits can be obtained. To aid this work, three different vehicle models are available. A linear model is derived for the controller design stage, and a highly nonlinear validated model from an industrial partner is available for simulation and evaluation purposes. A third model, which is also nonlinear, is used to integrate the control structures with a human interface test rig in a Hardware in the Loop (HiL) environment, which operates in real-time. Frequency based analysis and design techniques are used for the feedback controller design, and a feedforward based approach is used to apply a steering angle to the vehicle model. Computer simulations are initially used to evaluate the controllers, followed by evaluation via a HiL setup using a test rig. Using a visualisation environment in Matlab, this interface device allows driver interaction with the controllers to be analysed. It also enables driver reaction without any controllers present to be compared directly with the controller performance whilst completing the test manoeuvres. Results show that during certain manoeuvres, large variations in vehicle velocity are required to complete the control objective. However, it can be concluded from both the computer simulation and HiL results that simultaneous control of the lateral dynamics, based on the longitudinal wheel forces can be achieved using linear control methods

New slip control system considering actuator dynamics

A new control strategy for wheel slip control, considering the complete dynamics of the electro-hydraulic brake (EHB) system, is developed and experimentally validated in Cranfield University's HiL system. The control system is based on closed loop shaping Youla-parameterization method. The plant model is linearized about the nominal operating point, a Youla parameter is defined for all stabilizing feedback controller and control performance is achieved by employing closed loop shaping technique. The stability and performance of the controller are investigated in frequency and time domain, and verified by experiments using real EHB smart actuator fitted into the HiL system with driver in the loop

Identifying Modes of Intent from Driver Behaviors in Dynamic Environments

In light of growing attention of intelligent vehicle systems, we propose developing a driver model that uses a hybrid system formulation to capture the intent of the driver. This model hopes to capture human driving behavior in a way that can be utilized by semi- and fully autonomous systems in heterogeneous environments. We consider a discrete set of high level goals or intent modes, that is designed to encompass the decision making process of the human. A driver model is derived using a dataset of lane changes collected in a realistic driving simulator, in which the driver actively labels data to give us insight into her intent. By building the labeled dataset, we are able to utilize classification tools to build the driver model using features of based on her perception of the environment, and achieve high accuracy in identifying driver intent. Multiple algorithms are presented and compared on the dataset, and a comparison of the varying behaviors between drivers is drawn. Using this modeling methodology, we present a model that can be used to assess driver behaviors and to develop human-inspired safety metrics that can be utilized in intelligent vehicular systems.Comment: Submitted to ITSC 201

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Man-in-the-control-loop simulation of manipulators

A method to achieve man-in-the-control-loop simulation is presented. Emerging real-time dynamics simulation suggests a potential for creating an interactive design workstation with a human operator in the control loop. The recursive formulation for multibody dynamics simulation is studied to determine requirements for man-in-the-control-loop simulation. High speed computer graphics techniques provides realistic visual cues for the simulator. Backhoe and robot arm simulations are implemented to demonstrate the capability of man-in-the-control-loop simulation

Fuzzy logic control for energy saving in autonomous electric vehicles

Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic