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Autonomous vehicles have the capability to revolutionize human mobility and vehicle safety. To prove safe, they must be capable of navigating their environment as well as or better than the best human drivers. The best human drivers can leverage the limits of a vehicle's capabilities to avoid collisions and stabilize the vehicle while sliding on pavement, ice, and snow. Automated vehicles should similarly be capable of navigating safety-critical scenarios when friction is limited, and one large advantage they hold over human drivers is the amount of data they can generate. With self-driving vehicles in the San Francisco Bay Area collecting almost two human lifetimes worth of data just during 2020, this abundance of data holds the key to improving vehicle safety. This dissertation examines how data generated by self-driving vehicles can be used to learn control policies and models to improve vehicle control near the limits of handling. As data collection and vehicle operation near the limits can be expensive, this work uses skilled humans as an inspiration for learning policies because of their incredible data efficiency. This ability is clearly demonstrated in racing where skilled human drivers act to improve their performance after each lap by shifting their braking point to maximize corner entry speed and minimize lap time. Starting from a benchmark feedforward and feedback control architecture already comparable to skilled human drivers, this work directly learns feedforward policies to improve vehicle performance over time. By using an approximate physics-based model of the vehicle, recorded lap data, and the gradient of lap time, this approach improves lap time by almost seven tenths of a second on a nineteen second lap over an initial optimization-based approach for racing. Additionally, this approach generalizes to low-friction driving. While model-based policy search shows improvement over a solely optimization-based approach, model-based policy search is ultimately limited by the vehicle model used. Physics-based models are useful for interpretability and understanding, but fail to make use of the abundance of data self-driving vehicles generate and often do not capture high-order or complex-to-model effects. Additionally, to operate at a vehicle's true limits, precise identification of the vehicle's road-tire friction coefficient is required which is a very difficult task. To overcome the drawbacks of physics-based models, this thesis next examines the ability of neural networks to use vehicle data to learn vehicle dynamics models. These models are capable of not only modeling higher-order and complex effects, but also vehicle motion on high- and low-friction surfaces. Furthermore, these models do so while retaining comparable control performance near the limits to a benchmark physics-based feedforward and feedback control architecture. Though this control approach shows promise in operating near the limits, feedforward and feedback control is ultimately limited in its ability to trade of small errors in the short term to prevent larger errors in the future. Additionally, actuator and road boundary constraints play an increasingly important role in safety as the vehicle nears the limits. To deal with these limitations, this work presents neural network model predictive control for automated driving near the limits of friction. Neural network model predictive control not only leverages the neural network model's ability to predict dynamics on high- and low-friction test tracks, but also retains comparable or better performance to MPC using a well-tuned physics model optimized to the corresponding high- or low-friction test track. While neural network MPC shows improved performance over physics-based MPC when operating near the limits, MPC leverages its dynamics model with complete certainty. These effects can lead to MPC overleveraging its dynamics model, which in the presence of model mismatch can lead to poor controller performance. Additionally, when using neural network models in MPC, the network predicts vehicle motion with complete certainty regardless of the presence or absence of training data in the corresponding modeled region. To mitigate this issue, this work presents an approach which leverages a neural network model to learn the uncertainty in the underlying dynamics model used in MPC. By learning the uncertainty in MPC's dynamics model, the vehicle can take actions to avoid highly uncertain regions of operation while still attempting to optimize the original MPC cost function. The insights from this work can be used to design automated vehicles capable of leveraging vehicle data to more effectively operate near the limits of handling.
Many road accidents are caused by the inability of drivers to control a vehicle at its friction limits, yet racecar drivers routinely operate a vehicle at the limits of handling without losing control. If autonomous vehicles or driver assistance systems had capabilities similar to those of racecar drivers, many fatal accidents could be avoided. To advance this goal, an autonomous racing controller was designed and tested to understand how to track a path at the friction limits. The controller structure was inspired by how racecar drivers break down their task into (i) finding a desired path, and (ii) tracking this desired path at the limits. Separating the problem in this way instead of integrating both path planning and path tracking into one problem results in an intuitive structure that is easy to analyze. Assuming that a desired path is known, the racing controller in this dissertation focuses on tracking this path at the friction limits. The controller is separated into steering and longitudinal modules, each module consisting of feedforward and feedback controller submodules. From the desired path, the longitudinal feedforward submodule uses the path geometry and friction information derived from a g-g diagram to execute trail-braking and throttle-on-exit driving techniques. These techniques maximize tire forces during cornering by using a combination of steering and brake/throttle inputs. To calculate the steering input, the feedforward steering submodule employs a nonlinear bicycle model. These feedforward submodules can adjust their commands in real-time to respond to any changes in the environment, such as changes in friction due to rain or changes in the desired path to avoid an obstacle. To add path tracking ability and stability to the system, a fixed-gain full-state steering feedback submodule was combined with a longitudinal feedback submodule that regulates vehicle speed and minimizes tire slip through a slip circle feedback controller. For consistency in the steering controller, the same reference point at the center of percussion (COP) was used for both feedforward and feedback steering submodules. The COP was chosen because it simplifies the feedforward design process by eliminating the nonlinear and changing rear axle force from the lateral dynamics equation. Using the set of steering gains derived from lanekeeping steering with yaw damping feedback, the system was proven to be Lyapunov stable even when the rear tires are highly saturated. The simulation and experimental results on various surfaces on oval tracks demonstrate that the submodules work collectively to robustly track a desired path at the friction limits. The experimental results highlight the challenges of trail-braking during a corner-entry phase, where a correct corner entry-speed and accurate model of longitudinal weight transfer are required. Thus, longitudinal weight transfer was incorporated into the feedforward longitudinal submodule to minimize oversteer caused by reduction in the rear normal load. In addition to performing well on oval tracks, the racing controller also showed its ability to operate in a challenging environment by driving 12.4 miles up Pikes Peak autonomously, where the path consists of both dirt and paved surfaces with significant bank and grade. The complex path at Pikes Peak also demonstrated the controller's ability to plan the vehicle speed several corners in advance. The racing controller's ability to drive a vehicle at the friction limits can be applied to drive an autonomous vehicle while ensuring stability and tracking ability even in extreme conditions, such as driving on icy road. Alternatively, the submodules in the racing controller can be adapted to create driver assistance systems that work in conjunction with the driver, assisting the driver during emergency maneuvers.
This LNCS volume constitutes the proceedings of 12th International Conference, GALA 2023, in Dublin, Ireland, held during November/December 2023. The 36 full papers and 13 short papers were carefully reviewed and selected from 88 submissions. The papers contained in this book have been organized into six categories, reflecting the variety of theoretical approaches and application domains of research into serious games: 1. The Serious Games and Game Design 2. User experience, User Evaluation and User Analysis in Serious Games 3. Serious Games for Instruction 4. Serious Games for Health, Wellbeing and Social Change 5. Evaluating and Assessing Serious Games Elements 6. Posters
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
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.
This book is the ninth volume of a sub-series on Road Vehicle Automation, published as part of the Lecture Notes in Mobility. It gathers contributions to the Automated Road Transportation Symposium (ARTS), held on July 12-15, 2021, as a fully virtual event, and as a continuation of TRB's annual summer symposia on automated vehicle systems. Written by researchers, engineers and analysts from around the globe, this book offers a multidisciplinary perspectives on the opportunities and challenges associated with automating road transportation. It highlights innovative strategies, including public policies, infrastructure planning and automated technologies, which are expected to foster sustainable and automated mobility in the near future, thus addressing industry, government and research communities alike.
The number of people in South Asia's cities rose by 130 million between 2000 and 2011--more than the entire population of Japan. This was linked to an improvement in productivity and a reduction in the incidence of extreme poverty. But the region's cities have struggled to cope with the pressure of population growth on land, housing, infrastructure, basic services, and the environment. As a result, urbanization in South Asia remains underleveraged in its ability to deliver widespread improvements in both prosperity and livability. Leveraging Urbanization in South Asia is about the state of South Asia's urbanization and the market and policy failures that have taken the region’s urban areas to where they are today--and the hard policy actions needed if the region’s cities are to leverage urbanization better. This publication provides original empirical and diagnostic analysis of urbanization and related economic trends in the region. It also discusses in detail the key policy areas, the most fundamental being urban governance and finance, where actions must be taken to make cities more prosperous and livable.
This book brings together important new contributions covering electric vehicle smart charging (EVSC) from a multidisciplinary group of global experts, providing a comprehensive look at EVSC and its role in meeting long-term goals for decarbonization of electricity generation and transportation. This multidisciplinary reference presents practical aspects and approaches to the technology, along with evidence from its applications to real-world energy systems. Electric Vehicle Integration via Smart Charging is suitable for practitioners and industry stakeholders working on EVSC, as well as researchers and developers from different branches of engineering, energy, transportation, economic, and operation research fields.
This report presents a framework for measuring safety in automated vehicles (AVs): how to define safety for AVs, how to measure safety for AVs, and how to communicate what is learned or understood about AVs.
This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".