A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks

Cooperative Perception for Social Driving in Connected Vehicle Traffic

The development of autonomous vehicle technology has moved to the center of automotive research in recent decades. In the foreseeable future, road vehicles at all levels of automation and connectivity will be required to operate safely in a hybrid traffic where human operated vehicles (HOVs) and fully and semi-autonomous vehicles (AVs) coexist. Having an accurate and reliable perception of the road is an important requirement for achieving this objective. This dissertation addresses some of the associated challenges via developing a human-like social driver model and devising a decentralized cooperative perception framework. A human-like driver model can aid the development of AVs by building an understanding of interactions among human drivers and AVs in a hybrid traffic, therefore facilitating an efficient and safe integration. The presented social driver model categorizes and defines the driver\u27s psychological decision factors in mathematical representations (target force, object force, and lane force). A model predictive control (MPC) is then employed for the motion planning by evaluating the prevailing social forces and considering the kinematics of the controlled vehicle as well as other operating constraints to ensure a safe maneuver in a way that mimics the predictive nature of the human driver\u27s decision making process. A hierarchical model predictive control structure is also proposed, where an additional upper level controller aggregates the social forces over a longer prediction horizon upon the availability of an extended perception of the upcoming traffic via vehicular networking. Based on the prediction of the upper level controller, a sequence of reference lanes is passed to a lower level controller to track while avoiding local obstacles. This hierarchical scheme helps reduce unnecessary lane changes resulting in smoother maneuvers. The dynamic vehicular communication environment requires a robust framework that must consistently evaluate and exploit the set of communicated information for the purpose of improving the perception of a participating vehicle beyond the limitations. This dissertation presents a decentralized cooperative perception framework that considers uncertainties in traffic measurements and allows scalability (for various settings of traffic density, participation rate, etc.). The framework utilizes a Bhattacharyya distance filter (BDF) for data association and a fast covariance intersection fusion scheme (FCI) for the data fusion processes. The conservatism of the covariance intersection fusion scheme is investigated in comparison to the traditional Kalman filter (KF), and two different fusion architectures: sensor-to-sensor and sensor-to-system track fusion are evaluated. The performance of the overall proposed framework is demonstrated via Monte Carlo simulations with a set of empirical communications models and traffic microsimulations where each connected vehicle asynchronously broadcasts its local perception consisting of estimates of the motion states of self and neighboring vehicles along with the corresponding uncertainty measures of the estimates. The evaluated framework includes a vehicle-to-vehicle (V2V) communication model that considers intermittent communications as well as a model that takes into account dynamic changes in an individual vehicle’s sensors’ FoV in accordance with the prevailing traffic conditions. The results show the presence of optimality in participation rate, where increasing participation rate beyond a certain level adversely affects the delay in packet delivery and the computational complexity in data association and fusion processes increase without a significant improvement in the achieved accuracy via the cooperative perception. In a highly dense traffic environment, the vehicular network can often be congested leading to limited bandwidth availability at high participation rates of the connected vehicles in the cooperative perception scheme. To alleviate the bandwidth utilization issues, an information-value discriminating networking scheme is proposed, where each sender broadcasts selectively chosen perception data based on the novelty-value of information. The potential benefits of these approaches include, but are not limited to, the reduction of bandwidth bottle-necking and the minimization of the computational cost of data association and fusion post processing of the shared perception data at receiving nodes. It is argued that the proposed information-value discriminating communication scheme can alleviate these adverse effects without sacrificing the fidelity of the perception

Periodic Control of Automotive Vehicles to Improve Fuel Economy

This research studies the intersection of two technologies to improve fuel economy, i.e., pulse-and-glide (PnG) and cooperative adaptive cruise control (CACC). By exploiting the characteristics of internal combustion engines (ICEs), PnG periodically turns on and off the engine to save fuel. On the other hand, CACC facilitates the vehicle platooning via vehicle-to-vehicle (V2V) communication. CACC is promising to both increase the traffic throughput and reduce the fuel consumption. This research explores the possibilities for more fuel saving potential by introducing PnG into CACC. It also addresses the speed oscillation problem resulting from PnG operations, which is a challenge to vehicle platooning in terms of both string stability and ride comfort. To address these challenges, first the PnG operation of a hybrid electric vehicle (HEV) in the car-following scenario is studied with ride comfort considerations. The proposed control consists of two minimum-time control problems, one for the pulsing phase and another for the gliding phase. These two problems are solved using model-predictive control (MPC). After a series of simplification, convexification, and sparsity optimization, the two minimum-time control problems are reformulated as quadratic programming (QP) problems using the pseudo-spectral (PS) method to be solved on-line efficiently. This proposed control establishes a framework that can effectively leverage PnG for fuel savings, while satisfying the ride comfort and safety constraints. For the problem of platooning heterogeneous PnG vehicles, the concept of PnG synchronization is proposed as a solution. A control approach is developed based on the Kuramoto oscillator model to realize this concept. More specifically, individual vehicles in the platoon maintain their own virtual oscillators. With the synchronization mechanism provided by the Kuramoto model, the virtual oscillators are synchronized via only local communications. By tracking the target trajectories given by the virtual oscillators, PnG synchronization is achieved. A range-keeping approach via V2V communication is also developed. This proposed method of PnG synchronization is able to maintain the fuel saving potentials of individual PnG vehicles while keeping the platoon compact, which is ideal for achieving high throughput. The naturalistic driving data from the Safety Pilot project are utilized to analyze the levels of acceleration that people experience in everyday driving. Also, a PnG experiment is conducted using an automated Lincoln MKZ. The results from this experiment validate the fuel saving ability of the proposed PnG technique, especially at lower speeds, and offer a better knowledge about the influence of PnG operations on ride comfort.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169892/1/syshieh_1.pd