Compendium in Vehicle Motion Engineering

This compendium is written for the course “MMF062 Vehicle Motion Engineering” at Chalmers University of Technology. The compendium covers more than included in that course; both in terms of subsystem designs and in terms of some teasers for more advanced studies of vehicle dynamics. Therefore, it is also useful for the more advanced course “TME102 Vehicle Modelling and Control”.The overall objective of the compendium is to educate vehicle dynamists, i.e., engineers that understand and can contribute to development of good motion and energy functionality of vehicles. The compendium focuses on road vehicles, primarily passenger cars and commercial vehicles. Smaller road vehicles, such as bicycles and single-person cars, are only very briefly addressed. It should be mentioned that there exist a lot of ground-vehicle types not covered at all, such as: off-road/construction vehicles, tracked vehicles, horse wagons, hovercrafts, or railway vehicles.Functions are needed for requirement setting, design and verification. The overall order within the compendium is that models/methods/tools needed to understand each function are placed before the functions. Chapters 3-5 describes (complete vehicle) “functions”, organised after vehicle motion directions:\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 3:\ua0Longitudinal\ua0dynamics\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 4:\ua0Lateral\ua0dynamics\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 5:\ua0Vertical\ua0dynamicsChapter 1 introduces automotive industry and the overall way of working there and defines required pre-knowledge from “product-generic” engineering, e.g. modelling of dynamic systems.Chapter 2 also describes the subsystems relevant for vehicle dynamics:• Wheels and Tyre\ua0• Suspension\ua0• Propulsion\ua0• Braking System\ua0• Steering System\ua0• Environment Sensing Syste

Compendium in Vehicle Motion Engineering

This compendium is written for the course “MMF062 Vehicle Motion Engineering” at Chalmers University of Technology. The compendium covers more than included in that course; both in terms of subsystem designs and in terms of some teasers for more advanced studies of vehicle dynamics. Therefore, it is also useful for the more advanced courses, such as “TME102 Vehicle Modelling and Control”.The overall objective of the compendium is to educate engineers that understand and can contribute to development of good motion and energy functionality of vehicles. The compendium focuses on road vehicles, primarily passenger cars and commercial vehicles. Smaller road vehicles, such as bicycles and single-person cars, are only very briefly addressed. It can be mentioned that there exist a lot of ground-vehicle types not covered at all, such as: off-road/construction vehicles, tracked vehicles, horse wagons, hovercrafts, and railway vehicles.Functions are needed for requirement setting, design and verification. The overall order within the compendium is that models/methods/tools needed to understand each function are placed before the functions. Chapters 3-5 describes (complete vehicle) “functions”, organised after vehicle motion directions:\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 3:\ua0Longitudinal\ua0dynamics\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 4:\ua0Lateral\ua0dynamics\ub7\ua0\ua0\ua0\ua0\ua0\ua0\ua0\ua0 Chapter 5:\ua0Vertical\ua0dynamicsChapter 1 introduces automotive industry and the overall way of working there and defines required pre-knowledge from “product-generic” engineering, e.g. modelling of dynamic systems.Chapter 2 also describes the subsystems relevant for vehicle dynamics:• Wheels and Tyre\ua0• Suspension\ua0• Propulsion\ua0• Braking System\ua0• Steering System\ua0• Environment Sensing SystemThe compendium is released in a new version each year, around October, which is the version your read now. A "latest draft" is more frequently updated and often includes some more, sometimes unfinished, material: https://chalmersuniversity.box.com/s/6igaen1ugcjzuhjziuon08axxiy817f

Modeling and track planning for the automation of BMW model car

In recent years, autonomous driving technologies have become a topic of growing interest due to the promise of safer and more convenient mode of transportation. An essential element in every autonomous driving system is the control algorithm. Classical control schemes, like PID, are not able to manage Multiple Inputs-Multiple Outputs, complex, non-linear systems. A more recent control strategy is Model predictive control (MPC), a modern control method that has shown promising results in systems with complex dynamics. In MPC, a sequence of optimal control inputs are predicted within a short time horizon based on the car dynamics, and soft or hard restriction of the system. In this work, three different nonlinear-MPC (NMPC) controllers were formulated based on a kinematic, and two dynamic models (double-track and single-track). The steering system’s dynamics were additionally identified using experimental data. Each MPC was solved applying direct methods, by transforming the optimal control problem to a Nonlinear programming (NLP) problem using the Multiple shooting scheme with a Runge-Kutta 4 integrator. The NLPs were solved using the state-of-the-art optimization solver IpOpt. Before the real-time implementation, all the NMPC controllers were simulated in different scenarios and multiple configurations. The results allowed to select the most suitable controllers to be implemented in a 1:5 scale robotic car. Finally, two NMPC controllers based on the kinematic, and the single-track dynamic model were implemented in the robotic car. The algorithms were tested in two different scenarios at the maximum possible speed. The obtained results from the tests were very promising, and provide compelling evidence that MPC could be implemented as the core of future autonomous driving algorithms, since it computes the optimal control inputs, taking in consideration the restrictions inherent to the system.Tesi

Development of a Control System for an Urban Electric Vehicle Compatible with Autonomous Driving Features

As cities continue to grow, the roads only become more congested, leading drivers to spend more time commuting, while vehicle accidents continue to rise. The Mechatronics Vehicle Systems Laboratory has developed its own urban electric vehicle in an attempt to combat congestion, as well as pollution concerns in urban areas. The focus of these thesis is a fault tolerant control system to improve the robustness of the vehicle, as well as allow for future autonomous vehicle functions to be tested on it. The development of the control system began with deriving the requirements from the vehicle’s hardware, the goal of the project and foreseeable changes in the future. From there, the processors were selected, electronic control units were ordered, the vehicle’s necessarily electrical connections were completed, and the control software was deigned. Once every part of the project was completed, the control system was integrated, and testing was completed at the university. The goal of the control software was to allow the vehicle to be drivable if a motor, motor controller or steering actuator failed due to an electrical, mechanical or feedback fault. Due to the vehicle’s modular design it allowed other components to compensate for failures, while maintaining vehicle controllability and predictable vehicle dynamics. Furthermore, the control system allows for autonomous vehicle functions to be added and tested on the vehicle. This was tested through the addition of a lane following models, along with the control system’s path following function. The developed control system was shown to improve vehicle controllability when faults occurred, as well as allowed lane following to work effectively with the system structure and communication channels chosen. Overall, validating the design as well as allowing future mechanical designs or autonomous functions to be tested on the vehicle

Ground Vehicle Platooning Control and Sensing in an Adversarial Environment

The highways of the world are growing more congested. People are inherently bad drivers from a safety and system reliability perspective. Self-driving cars are one solution to this problem, as automation can remove human error and react consistently to unexpected events. Automated vehicles have been touted as a potential solution to improving highway utilization and increasing the safety of people on the roads. Automated vehicles have proven to be capable of interacting safely with human drivers, but the technology is still new. This means that there are points of failure that have not been discovered yet. The focus of this work is to provide a platform to evaluate the security and reliability of automated ground vehicles in an adversarial environment. An existing system was already in place, but it was limited to longitudinal control, relying on a steel cable to keep the vehicle on track. The upgraded platform was developed with computer vision to drive the vehicle around a track in order to facilitate an extended attack. Sensing and control methods for the platform are proposed to provide a baseline for the experimental platform. Vehicle control depends on extensive sensor systems to determine the vehicle position relative to its surroundings. A potential attack on a vehicle could be performed by jamming the sensors necessary to reliably control the vehicle. A method to extend the sensing utility of a camera is proposed as a countermeasure against a sensor jamming attack. A monocular camera can be used to determine the bearing to a target, and this work extends the sensor capabilities to estimate the distance to the target. This provides a redundant sensor if the standard distance sensor of a vehicle is compromised by a malicious agent. For a 320×200 pixel camera, the distance estimation is accurate between 0.5 and 3 m. One previously discovered vulnerability of automated highway systems is that vehicles can coordinate an attack to induce traffic jams and collisions. The effects of this attack on a vehicle system with mixed human and automated vehicles are analyzed. The insertion of human drivers into the system stabilizes the traffic jam at the cost of highway utilization

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

Ground Vehicle Platooning Control and Sensing in an Adversarial Environment

The highways of the world are growing more congested. People are inherently bad drivers from a safety and system reliability perspective. Self-driving cars are one solution to this problem, as automation can remove human error and react consistently to unexpected events. Automated vehicles have been touted as a potential solution to improving highway utilization and increasing the safety of people on the roads. Automated vehicles have proven to be capable of interacting safely with human drivers, but the technology is still new. This means that there are points of failure that have not been discovered yet. The focus of this work is to provide a platform to evaluate the security and reliability of automated ground vehicles in an adversarial environment. An existing system was already in place, but it was limited to longitudinal control, relying on a steel cable to keep the vehicle on track. The upgraded platform was developed with computer vision to drive the vehicle around a track in order to facilitate an extended attack. Sensing and control methods for the platform are proposed to provide a baseline for the experimental platform. Vehicle control depends on extensive sensor systems to determine the vehicle position relative to its surroundings. A potential attack on a vehicle could be performed by jamming the sensors necessary to reliably control the vehicle. A method to extend the sensing utility of a camera is proposed as a countermeasure against a sensor jamming attack. A monocular camera can be used to determine the bearing to a target, and this work extends the sensor capabilities to estimate the distance to the target. This provides a redundant sensor if the standard distance sensor of a vehicle is compromised by a malicious agent. For a 320×200 pixel camera, the distance estimation is accurate between 0.5 and 3 m. One previously discovered vulnerability of automated highway systems is that vehicles can coordinate an attack to induce traffic jams and collisions. The effects of this attack on a vehicle system with mixed human and automated vehicles are analyzed. The insertion of human drivers into the system stabilizes the traffic jam at the cost of highway utilization