Joint Time and Energy-Optimal Control of Connected Automated Vehicles at Signal-Free Intersections with Speed-Dependent Safety Guarantees

We extend earlier work establishing a framework for optimally controlling Connected Automated Vehicles (CAVs) crossing a signal free intersection by jointly optimizing energy and travel time. We derive explicit optimal control solutions in a decentralized manner that guarantee both a speed-dependent rear-end safety constraint and a time-dependent lateral collision constraint, in addition to lower/upper bounds on speed and acceleration. Extensive simulation examples are included to illustrate this framework.Comment: 11 pages. This article draws from arXiv:1903.04600 and is submitted to CDC1

Methods in intelligent transportation systems exploiting vehicle connectivity, autonomy and roadway data

Intelligent transportation systems involve a variety of information and control systems methodologies, from cooperative systems which aim at traffic flow optimization by means of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, to information fusion from multiple traffic sensing modalities. This thesis aims to address three problems in intelligent transportation systems, one in optimal control of connected automated vehicles, one in discrete-event and hybrid traffic simulation model, and one in sensing and classifying roadway obstacles in smart cities. The first set of problems addressed relates to optimally controlling connected automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling. A decentralized optimal control framework is established whereby, under proper coordination among CAVs, each CAV can jointly minimize its energy consumption and travel time subject to hard safety constraints. A closed-form analytical solution is derived while taking speed, control, and safety constraints into consideration. The analytical solution of each such problem, when it exists, yields the optimal CAV acceleration/deceleration. The framework is capable of accommodating for turns and ensures the absence of collisions. In the meantime, a measurement of passenger comfort is taken into account while the vehicles make turns. In addition to the first-in-first-out (FIFO) ordering structure, the concept of dynamic resequencing is introduced which aims at further increasing the traffic throughput. This thesis also studies the impact of CAVs and shows the benefit that can be achieved by incorporating CAVs to conventional traffic. To validate the effectiveness of the proposed solution, a discrete-event and hybrid simulation framework based on SimEvents is proposed, which facilitates safety and performance evaluation of an intelligent transportation system. The traffic simulation model enables traffic study at the microscopic level, including new control algorithms for CAVs under different traffic scenarios, the event-driven aspects of transportation systems, and the effects of communication delays. The framework spans multiple toolboxes including MATLAB, Simulink, and SimEvents. In another direction, an unsupervised anomaly detection system is developed based on data collected through the Street Bump smartphone application. The system, which is built based on signal processing techniques and the concept of information entropy, is capable of generating a prioritized list of roadway obstacles, such that the higher-ranked entries are most likely to be actionable bumps (e.g., potholes) requiring immediate attention, while those lower-ranked are most likely to be nonactionable bumps(e.g., flat castings, cobblestone streets, speed bumps) for which no immediate action is needed. This system enables the City to efficiently prioritize repairs. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of the system in practice

Decentralized Optimal Merging Control for Connected and Automated Vehicles

This paper addresses the optimal control of Connected and Automated Vehicles (CAVs) arriving from two roads at a merging point where the objective is to jointly minimize the travel time and energy consumption of each CAV. The solution guarantees that a speed-dependent safety constraint is always satisfied, both at the merging point and everywhere within a control zone which precedes it. We first analyze the case of no active constraints and prove that under certain conditions the safety constraint remains inactive, thus significantly simplifying the determination of an explicit decentralized solution. When these conditions do not apply, an explicit solution is still obtained that includes intervals over which the safety constraint is active. Our analysis allows us to study the tradeoff between the two objective function components (travel time and energy within the control zone). Simulation examples are included to compare the performance of the optimal controller to a baseline with human-driven vehicles with results showing improvements in both metrics.Comment: 16 pages, 2nd version, 20 figure

Safety-critical optimal control in autonomous traffic systems

Traffic congestion is a central problem in transportation systems, especially in urban areas. The rapid development of Connected and Automated Vehicles (CAVs) and new traffic infrastructure technologies provides a promising solution to solve this problem. This work focuses on the safety-critical optimal control of CAVs in autonomous traffic systems. The dissertation starts with the roundabout problem of controlling CAVs travelling through a roundabout so as to jointly minimize their travel time, energy consumption, and centrifugal discomfort while providing speed-dependent safety guarantees. A systematic approach is developed to determine the safety constraints for each CAV dynamically. The joint optimal control and control barrier function (OCBF) controller is applied, where the unconstrained optimal control solution is derived which is subsequently optimally tracked by a real-time controller while guaranteeing the satisfaction of all safety constraints. Secondly, the dissertation deals with the feasibility problem of OCBF. The feasibility problem arises when the control bounds conflict with the Control Barrier Function (CBF) constraints and is solved by adding a single feasibility constraint to the Quadratic Problem (QP) in the OCBF controller to derive the feasibility guaranteed OCBF. The feasibility guaranteed OCBF is applied in the merging control problem which provably guarantees the feasibility of all QPs derived from the OCBF controller. Thirdly, the dissertation deals with the performance loss of OCBF due to the improperly selected reference trajectory which deviates largely from the complete optimal solution especially when the vehicle limitations are tight. A neural network is used to learn the control policy from data retrieved by offline calculation from the complete optimal solutions. Tracking the learnt reference trajectory with CBF outperforms OCBF in simulation experiments. Finally, a hierarchical framework of modular control zones (CZ) is proposed to extend the safety-critical optimal control of CAV from a single CZ to a traffic network. The hierarchical modular CZ framework is developed consisting of a lower-level OCBF controller and a higher-level feedback flow controller to coordinate adjacent CZs which outperforms a direct extension of the OCBF framework to multiple CZs without any flow control in simulation