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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.
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.
As autonomous vehicles enter public roads, they should be capable of using all of the vehicle's performance capability, if necessary, to avoid collisions. This dissertation focuses on facilitating collision avoidance for autonomous vehicles by enabling safe vehicle operation up to the handling limits. The new control approaches first rely on a standard paradigm for autonomous vehicles that divides vehicle control into trajectory generation and trajectory tracking. A trajectory generation approach calculates emergency lane change trajectories, defined in terms of path curvature, that allows an autonomous vehicle to perform emergency lane changes up to its handling limits. Analysis also provides insights into when and to what extent a vehicle should brake and turn during an emergency lane change to maximize the number of situations in which a collision can be avoided. However, experimental results also highlight vehicle stabilization challenges associated with tracking paths defined by high rates of curvature change, which are desirable for emergency maneuvers. A link is forged between path curvature and vehicle performance, which inspires two trajectory tracking control designs. A four-wheel steering controller adds rear steering actuation to improve tracking and stabilization performance, while a two-wheel steering predictive controller incorporates future path information into current control actions. Experimental results demonstrate the advantages of each approach. However, separating vehicle control into trajectory generation and tracking is not always conducive to emergency maneuvers up to the vehicle's handling limits, where these aspects of vehicle control become tightly coupled with each other and with vehicle stabilization. An alternative paradigm is suggested that is more adept at controlling the vehicle in such scenarios. This approach integrates trajectory generation, trajectory tracking, and vehicle stabilization into one controller capable of mediating among the sometimes conflicting demands imposed by collision avoidance and stabilization. The controller can prioritize collision avoidance, above even stabilization, to minimize potential collisions. Experimental emergency lane changes and a mid-corner obstacle avoidance scenario highlight the advantages of this integrated approach to vehicle control.
The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
Without a driver to fall back on, a fully self-driving car needs to be able to handle any situation it can encounter. With the perspective of future safety systems, this research studies autonomous maneuvering at the tire-road friction limit. In these situations, the dynamics is highly nonlinear, and the tire-road parameters are uncertain. To gain insights into the optimal behavior of autonomous safety-critical maneuvers, they are analyzed using optimal control. Since analytical solutions of the studied optimal control problems are intractable, they are solved numerically. An optimization formulation reveals how the optimal behavior is influenced by the total amount of braking. By studying how the optimal trajectory relates to the attainable forces throughout a maneuver, it is found that maximizing the force in a certain direction is important. This is like the analytical solutions obtained for friction-limited particle models in earlier research, and it is shown to result in vehicle behavior close to the optimal also for a more complex model. Based on the insights gained from the optimal behavior, controllers for autonomous safety maneuvers are developed. These controllers are based on using acceleration-vector references obtained from friction-limited particle models. Exploiting that the individual tire forces tend to be close to their friction limits, the desired tire slip angles are determined for a given acceleration-vector reference. This results in controllers capable of operating at the limit of friction at a low computational cost and reduces the number of vehicle parameters used. For straight-line braking, ABS can intervene to reduce the braking distance without prior information about the road friction. Inspired by this, a controller that uses the available actuation according to the least friction necessary to avoid a collision is developed, resulting in autonomous collision avoidance without any estimation of the tire–road friction. Investigating time-optimal lane changes, it is found that a simple friction-limited particle model is insufficient to determine the desired acceleration vector, but including a jerk limit to account for the yaw dynamics is sufficient. To enable a tradeoff between braking and avoidance with a more general obstacle representation, the acceleration-vector reference is computed in a receding-horizon framework. The controllers developed in this thesis show great promise with low computational cost and performance not far from that obtained offline by using numerical optimization when evaluated in high-fidelity simulation.
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".
The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.
The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.
The trend of more advanced driver-assistance features and the development toward autonomous vehicles enable new possibilities in the area of active safety. With more information available in the vehicle about the surrounding traffic and the road ahead, there is the possibility of improved active-safety systems that make use of this information for stability control in safety-critical maneuvers. Such a system could adaptively make a trade-off between controlling the longitudinal, lateral, and rotational dynamics of the vehicle in such a way that the risk of collision is minimized. To support this development, the main aim of this licentiate thesis is to provide new insights into the optimal behavior for autonomous vehicles in safety-critical situations. The knowledge gained have the potential to be used in future vehicle control systems, which can perform maneuvers at-the-limit of vehicle capabilities. Stability control of a vehicle in autonomous safety-critical at-the-limit maneuvers is analyzed by the use of optimal control. Since analytical solutions of the studied optimal control problems are intractable, they are discretized and solved numerically. A formulation of an optimization criterion depending on a single interpolation parameter is introduced, which results in a continuous family of optimal coordinated steering and braking patterns. This formulation provides several new insights into the relation between different braking patterns for vehicles in at-the-limit maneuvers. The braking patterns bridge the gap between optimal lane-keeping control and optimal yaw control, and have the potential to be used for future active-safety systems that can adapt the level of braking to the situation at hand. A new illustration named attainable force volumes is introduced, which effectively shows how the trajectory of a vehicle maneuver relates to the attainable forces over the duration of the maneuver. It is shown that the optimal behavior develops on the boundary surface of the attainable force volume. Applied to lane-keeping control, this indicates a set of control principles similar to those analytically obtained for friction-limited particle models in earlier research, but is shown to result in vehicle behavior close to the globally optimal solution also for more complex models and scenarios.