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In this thesis, a modified second-order continuum model is used to describe the traffic behaviour along highways. The model is identified and verified using several sets of traffic measurements collected from a major highway in metropolitan Toronto, Canada. A robust nonlinear sliding mode observer is developed to generate estimates of average velocity and density for a segment of a highway within a corridor, given loop detector measurements at the end-points of the segment. The sliding mode approach has several advantages over other estimation techniques such as the Kalman Filtering including proof of estimate convergence and simplified computations. However, the primary advantage is the robustness of the observer with respect to unmodelled dynamics and disturbances. Unmodelled dynamics are associated with the traffic factors whose effects cannot be captured (properly) in the traffic flow models, e.g., road geometry and weather conditions. On the other hand, model disturbances such as unavailable (not measured) traffic flow at a ramp or measurements provided by a faulty detector can also create unpredictable traffic states. Based on the presented traffic model, a systematic design procedure is developed to make the observer robust with respect to the modelling uncertainties and unavailable traffic states. Simulation and experimental results show the effectiveness of the proposed observer in estimating the states of a highway traffic system. Moreover, a new decentralized state feedback linearizing controller for ramp metering using variable structure control is presented. The main aim is to develop a robust controller to locally stabilize freeway traffic despite the presence of disturbances and modelling errors. Simulation results show that the proposed controller provides improved performance in achieving the design objectives over other existing ramp control strategies such as neural network and linear feedback controllers.
Vehicles' active safety systems use different sensors, vehicle states, and actuators, along with an advanced control algorithm, to assist drivers and to maintain the dynamics of a vehicle within a desired safe range in case of instability in vehicle motion. Therefore, recent developments in such vehicle stability control and autonomous driving systems have led to substantial interest in reliable road angle and vehicle states (tire forces and vehicle velocities) estimation. Advances in applications of sensor technologies, sensor fusion, and cooperative estimation in intelligent transportation systems facilitate reliable and robust estimation of vehicle states and road angles. In this direction, developing a flexible and reliable estimation structure at a reasonable cost to operate the available sensor data for the proper functioning of active safety systems in current vehicles is a preeminent objective of the car manufacturers in dealing with the technological changes in the automotive industry. This thesis presents a novel generic integrated tire force and velocity estimation system at each corner to monitor tire capacities and slip condition individually and to address road uncertainty issues in the current model-based vehicle state estimators. Tire force estimators are developed using computationally efficient nonlinear and Kalman-based observers and common measurements in production vehicles. The stability and performance of the time-varying estimators are explored and it is shown that the developed integrated structure is robust to model uncertainties including tire properties, inflation pressure, and effective rolling radius, does not need tire parameters and road friction information, and can transfer from one car to another. The main challenges for velocity estimation are the lack of knowledge of road friction in the model-based methods and accumulated error in kinematic-based approaches. To tackle these issues, the lumped LuGre tire model is integrated with the vehicle kinematics in this research. It is shown that the proposed generic corner-based estimator reduces the number of required tire parameters significantly and does not require knowledge of the road friction. The stability and performance of the time-varying velocity estimators are studied and the sensitivity of the observers' stability to the model parameter changes is discussed. The proposed velocity estimators are validated in simulations and road experiments with two vehicles in several maneuvers with various driveline configurations on roads with different friction conditions. The simulation and experimental results substantiate the accuracy and robustness of the state estimators for even harsh maneuvers on surfaces with varying friction. A corner-based lateral state estimation is also developed for conventional cars application independent of the wheel torques. This approach utilizes variable weighted axles' estimates and high slip detection modules to deal with uncertainties associated with longitudinal forces in large steering. Therefore, the output of the lateral estimator is not altered by the longitudinal force effect and its performance is not compromised. A method for road classification is also investigated utilizing the vehicle lateral response in diverse maneuvers. Moreover, the designed estimation structure is shown to work with various driveline configurations such as front, rear, or all-wheel drive and can be easily reconfigured to operate with different vehicles and control systems' actuator configurations such as differential braking, torque vectoring, or their combinations on the front or rear axles. This research has resulted in two US pending patents on vehicle speed estimation and sensor fault diagnosis and successful transfer of these patents to industry.
Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. - Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring - Uses methods based on video and time series data for traffic modeling and forecasting - Includes case studies, key processes guidance and comparisons of different methodologies
A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.
The AVEC symposium is a leading international conference in the fields of vehicle dynamics and advanced vehicle control, bringing together scientists and engineers from academia and automotive industry. The first symposium was held in 1992 in Yokohama, Japan. Since then, biennial AVEC symposia have been established internationally and have considerably contributed to the progress of technology in automotive research and development. In 2016 the 13th International Symposium on Advanced Vehicle Control (AVEC’16) was held in Munich, Germany, from 13th to 16th of September 2016. The symposium was hosted by the Munich University of Applied Sciences. AVEC’16 puts a special focus on automatic driving, autonomous driving functions and driver assist systems, integrated control of interacting control systems, controlled suspension systems, active wheel torque distribution, and vehicle state and parameter estimation. 132 papers were presented at the symposium and are published in these proceedings as full paper contributions. The papers review the latest research developments and practical applications in highly relevant areas of vehicle control, and may serve as a reference for researchers and engineers.