Adaptive Cooperative Highway Platooning and Merging

As low-cost reliable sensors are introduced to market, research efforts in autonomous driving are increasing. Traffic congestion is a major problem for nearly all metropolis'. Assistive driving technologies like cruise control and adaptive cruise control are widely available today. While these control systems ease the task of driving, the driver still needs to be fully alert at all times. While these existing structures are helpful in alleviating the stress of driving to a certain extent, they are not enough to improve traffic flow. Two main causes of congestion are slow response of drivers to their surroundings, and situations like highway ramp merges or lane closures. This thesis will address both of these issues. A modified version of the widely available adaptive cruise control systems, known as cooperative adaptive cruise control, can work at all speeds with additional wireless communication that improves stability of the controller. These structures can tolerate much smaller desired spacing and can safely work in stop and go traffic. This thesis proposes a new control structure that combines conventional cooperative adaptive cruise control with rear end collision check. This approach is capable of avoiding rear end collisions with the following car, as long as it can still maintain the safe distance with the preceding vehicle. This control structure is mainly intended for use with partially automated highways, where there is a risk of being rear-ended while following a car with adaptive cruise control. Simulation results also shows that use of bidirectional cooperative adaptive cruise control also helps to strengthen the string stability of the platoon. Two different control structures are used to accomplish this task: MPC and PD based switching controller. Model predictive control (MPC) structure works well for the purpose of bidirectional platoon control. This control structure can adapt to the changes in the plant with the use of a parameter estimator. Constraints are set to make sure that the controller outputs are always within the boundaries of the plant. Also these constraints assures that a certain gap will always be kept with the preceding vehicle. PD based switching controller offers an alternative to the MPC structure. Main advantage of this control structure is that it is designed to be robust to certain level of sensor noise. Both these control structures gave good simulation results. The thesis makes use of the control structures developed in the earlier chapters to continue developing structures to alleviate traffic congestions. Two merging schemes are proposed to find a solution to un-signaled merging and lane closures. First problem deals with situations where necessary levels of communication is not present to inform surrounding drivers of merging intention. Second structure proposes a merging protocol for cases where two platoons are approaching a lane closure. This structure makes use of the modified cooperative adaptive cruise control structures proposed earlier in the thesis

Trajectory Convergence From Coordinate-Wise Decrease of Quadratic Energy Functions, and Applications to Platoons

We consider trajectories where the sign of the derivative of each entry is opposite to that of the corresponding entry in the gradient of an energy function. We show that this condition guarantees convergence when the energy function is quadratic and positive definite and partly extend that result to some classes of positive semi-definite quadratic functions including those defined using a graph Laplacian. We show how this condition allows establishing the convergence of a platoon application in which it naturally appears, due to deadzones in the control laws designed to avoid instabilities caused by inconsistent measurements of the same distance by different agents

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Cooperative Autonomous Vehicle Speed Optimization near Signalized Intersections

Road congestion in urban environments, especially near signalized intersections, has been a major cause of significant fuel and time waste. Various solutions have been proposed to solve the problem of increasing idling times and number of stops of vehicles at signalized intersections, ranging from infrastructure-based techniques, such as dynamic traffic light control systems, to vehicle-based techniques that rely on optimal speed computation. However, all of the vehicle-based solutions introduced to solve the problem have approached the problem from a single vehicle point of view. Speed optimization for vehicles approaching a traffic light is an individual decision-making process governed by the actions/decisions of the other vehicles sharing the same traffic light. Since the optimization of other vehicles’ speed decisions is not taken into consideration, vehicles selfishly compete over the available green light; as a result, some of them experience unnecessary delay which may lead to increasing congestion. In addition, the integration of dynamic traffic light control system with vehicle speed optimization such that coordination and cooperation between the traffic light and vehicles themselves has not yet been addressed. As a step toward technological solutions to popularize the use of autonomous vehicles, this thesis introduces a game theoretic-based cooperative speed optimization framework to minimize the idling times and number of stops of vehicles at signalized intersections. This framework consists of three modules to cover issues of autonomous vehicle individual speed optimization, information acquisition and conflict recognition, and cooperative speed decision making. It relies on a linear programming optimization technique and game theory to allow autonomous vehicles heading toward a traffic light cooperate and agree on certain speed actions such that the average idling times and number of stops are minimized. In addition, the concept of bargaining in game theory is introduced to allow autonomous vehicles trade their right of passing the traffic light with less or without any stops. Furthermore, a dynamic traffic light control system is introduced to allow the cooperative autonomous vehicles cooperate and coordinate with the traffic light to further minimize their idling times and number of stops. Simulation has been conducted in MATLAB to test and validate the proposed framework under various traffic conditions and results are reported showing significant reductions of average idling times and number of stops for vehicles using the proposed framework as compared to a non-cooperative speed optimization algorithm. Moreover, a platoon-based autonomous vehicle speed optimization scheme is posed to minimize the average idling times and number of stops for autonomous vehicles connected in platoons. This platoon-based scheme consists of a linear programming optimization technique and intelligent vehicle decision-making algorithm to allow vehicles connected in a platoon and approaching a signalized intersection decide in a decentralized manner whether it is efficient to be part of the platoon or not. Simulation has been conducted in MATLAB to investigate the performance of this platoon-based scheme under various traffic conditions and results are reported, showing that vehicles using the proposed scheme achieve lower average values of idling times and number of stops as compared to two other platoon scenarios

Real-time Autonomous Cruise Control of Connected Plug-in Hybrid Electric Vehicles Under Uncertainty

Advances in embedded digital computing and communication networks have enabled the development of automated driving systems. Autonomous cruise control (ACC) and cooperative ACC (CACC) systems are two popular types of these technologies, which can be implemented to enhance safety, traffic flow, driving comfort and energy economy. This PhD thesis develops robust and adaptive controllers for plug-in hybrid electric vehicles (PHEVs), with the Toyota Plug-in Prius as the baseline vehicle, in order to enable them to perform safe and robust car-following and platooning with improved vehicle performance. Three controllers are designed here to achieve three main goals. The first goal of this thesis is the development of a real-time Ecological ACC (Eco-ACC) system for PHEVs, that is robust to uncertainties. A novel adaptive tube-based nonlinear model predictive control (AT-NMPC) approach to the design of Eco-ACC systems is proposed. Through utilizing two separate models to define the constrained optimal control problem, this method takes into account uncertainties, modeling errors and delayed data in the design of the controller and guaranties robust constraint handling for the assumed uncertainty bounds. {In addition, it adapts to changes in order to improve the control performance when possible.} Furthermore, a Newton/GMRES fast solver is employed to implement the designed AT-NMPC in real-time. The second goal is the development of a real-time Ecological CACC (Eco-CACC) system that can simultaneously satisfy the frequency-domain and time-domain platooning criteria. A novel distributed reference governor (RG) approach to the constraint handling of vehicle platoons equipped with CACC is presented. RG sits behind the controlled string stable system and keeps the output inside the defined constraints. Furthermore, to improve the platoon's energy economy, a controller is presented for the leader's control using NMPC method, assuming it is a PHEV. The third objective of this thesis is the control of heterogeneous platoons using an adaptive control approach. A direct model reference adaptive controller (MRAC) is designed that enforces a string stable behavior on the vehicle platoon despite different dynamical models of the platoon members and the external disturbances acting on the systems. The proposed method estimates the controller coefficients on-line to adapt to the disturbances such as wind, changing road grade and also to different vehicle dynamic behaviors. The main purpose of all three controllers is to maintain the driving safety of connected vehicles in car-following and platooning while being real-time implementable. In addition, when there is a possibility for performance enhancement without sacrificing safety, ecological improvement is also considered. For each designed controller, Model-in-the-Loop (MIL) simulations and Hardware-in-the-Loop (HIL) experiments are performed using high-fidelity vehicle models in order to validate controllers' performance and ensure their real-time implementation capability

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

Flight Simulation-Driven Research into Simplified Vehicle Operations for Urban Air Mobility

692M152140004The goal of this project was to study the Simplified Vehicle Operations (SVO) paradigm for vertical takeoff and landing (VTOL) urban air mobility (UAM) aircraft through piloted simulations in two fixed-base flight simulators located at the Vehicle Systems, Dynamics, and Design Laboratory (VSDDL), part of the Department of Aerospace Engineering at Auburn University. A lift-plus-cruise (LPC) aircraft flight simulation model was deployed to two flight simulators, with identical cockpit display elements and core flight control system (FCS) architecture, which was based on the Total Energy Control System (TECS). The simulator setups differed in the design of the pilot inceptors and in the inceptor-to-command mappings

Aeronautical engineering, a continuing bibliography with indexes

This bibliography lists 823 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1984