Ecological Control and Coordination of Connected and Automated PHEVs at Roundabouts under Uncertainty

During the last decade, comprehensive research efforts were concentrated on autonomous driving. Annually, many car accidents happen as a result of human faults. Extreme traffic congestion prolongs commute time, increase air pollution and cause other transportation inefficiencies. Consequently, using advanced technologies to make vehicles less dependent on human drivers enable more efficient use of time for passengers and decrease car accidents. Connectivity between vehicles and automation provides a spectacular opportunity to improve traffic flow, safety, and efficiency. There are different main active research subjects under the broad domain of autonomous driving, one of them is intersection control for connected and automated vehicles (CAVs) which can be categorized into centralized and decentralized approaches. The environmental and strict regulatory demands require automotive companies to reduce Carbon Dioxide emissions by investing more in Electric Vehicles (EVs) and Plug-in Hybrid Electric vehicles (PHEVs). A PHEV equipped with connectivity and automation looks more interesting to automobile consumers since they can have advantages of both fewer emissions and enhanced abilities. Since the powertrain of PHEVs consists of different sources of power, advanced control techniques such as Model Predictive Control (MPC) is needed. Coordination of vehicles at roundabouts is a demanding problem especially by knowing that the chance of both lateral and longitudinal collision exists. To this end, first, we proposed a centralized nonlinear MPC-based controller to adhere to calculated priorities for connected and automated PHEVs (CA-PHEVs). We further continued this research by proposing an approach for solving nonlinear multi-objective optimal control problem of decentralized coordination of CA-PHEVs at roundabouts with consideration of fuel economy. It was found that the proposed controller can calculate priority based on a navigation function and provide a safe gap between vehicles. A novel priority calculation logic based on optimal control is proposed as well and its performance is compared with the navigation function approach. In addition to the decentralized control approach, we considered a more realistic robust tube-based nonlinear MPC decentralized approach to solve this problem in the presence of uncertainties. We used simulations to test the controller and a Toyota Prius PHEV high-fidelity model is used in this thesis for simulations. Simulation results show that the addition of robustness, and energy economy to performance index can improve the fuel consumption of the vehicle. One of the major concerns in designing a controller for automotive applications is real-time implementation. The results of hardware-in-the-loop experiments show the real-time implementation of the controllers

Optimal speed trajectory and energy management control for connected and automated vehicles

Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle). The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles. In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation. The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces

Short-Term Traffic Participants Behaviour Prediction for Automated Vehicles

Due to the rapid commencement of autonomous vehicles and the promising potential benefits, it has made it critical for said vehicles to be able to interpret their environment and compensate for the absence of driver predictions from visual cues. This study presents a novel intermediate component to improve the performance of autonomous vehicle controllers, by providing them with real-time microscopic predictions of traffic participants' behaviour, given the environmental conditions. This strategy is especially aimed towards direct combination with model-predictive controllers (MPCs) and other controllers that can utilize dynamic state predictions. This task is undertaken in three stages for three different scenarios. Scenario I considers V2X communications and predicts the velocity of an arbitrary vehicle in longitudinal direction. Using a recurrent neural network (RNN) and considering a complementary variable the strategy can predict the speed profile of said vehicle for arbitrary horizons. Results of this scenario exhibit >0.95 correlation if trained with enough data. Scenario II moves on to a more sophisticated approach for prediction of vehicles on US-101, using real data provided by the U.S. Federal Highway Administration (FHWA) under NGSIM. Utilizing a marriage of dynamic Bayesian network (DBN) and RNN, the method can make predictions on speed profiles of all present vehicles within a range, for arbitrary horizons, as well as prediction on whether the vehicle on the main lanes would yield to the merging vehicles on the ramp. Due to digital nature of the DBN stream, a Kalman filter (KF) was introduced as post processing smoothing method. Results of this scenario exhibit >0.95 correlation and <1.6 mph mean absolute error. Scenario III tackles a much more complex driving environment, intersection driving. Because in intersection driving, the priority relationships of highway driving are no longer existent, the training must be broadened to encompass vehicle pairs which is exponentially more difficult than training for single vehicles. The data for this phase was generated by SUMO. Results of this scenario exhibit <1.1 mph mean absolute error. Scenario IV focuses on the problem of roundabout driving. In roundabout driving, the general driving situation is more similar to highway merging, however due to the rapid move toward replacing intersections with roundabouts, especially in developing cities, definitely an important scenario to look at. In this scenario SUMO was used for data generation, a new DBN topology was developed and the results yielded exhibit >0.89 correlation. To evaluate the performance and the accuracy of the proposed method, it was compared with a collection of sequence prediction techniques, including LSTM and GRU. It was concluded that the DBN-RNN has the best accuracy and performance among these methods. Validation of the strategy was planned to be done on the scaled autonomous vehicle test platform developed in Smart Hybrid and Electric Vehicles Systems (SHEVS) lab, where driver-in-the-loop hardware was incorporated and the equipment were prepared but due to COVID-19 closures was not realized

Fast Nonlinear Model Predictive Control of Quadrotors: Design and Experiments

Quadrotor (or quadcopter) is a type of Unmanned Aerial Vehicles (UAVs). Due to the quadrotors simple and inexpensive design, they have become popular platforms. This thesis proposes a computationally fast scheme for implementing Nonlinear Model Predictive Control (NMPC) as a high-level controller to solve the path following problem for unmanned quadrotors. After discussing the background and reviewing the literature, it is noted that this problem referred widely in the literature as a necessary step toward the autonomous flight of quadrotor UAVs. The previous studies usually used simplified models which are computationally uncomplicated and straightforward in terms of control developments and stability investigations. Moreover, some articles are presented showing the importance of accurate state observation on the performance of feedback-based control approaches. The NMPC-based controller is designed using a more realistic highly nonlinear control-oriented model which requires heavy computations for practical real-time implementations. To deal with this issue, the Newton generalized minimal residual (Newton/GMRES) method is applied to solve the NMPC’s real-time optimizations rapidly during the control process. This technique uses the Hamiltonian method to derive a set of equations with multiple variables. To solve these in a real-time application, the Newton/GMRES method applies forward-difference generalized minimal residual (fdgmres) algorithm. The simulation and experimental result using a commercial drone, called AR.Drone 2.0, in our laboratory instrumented by a Vicon Vantage motion capture system, demonstrate that our feedback-based control method’s performance highly depends on the reliability of the state vector feedback signals. As a result, a Kalman filter and Luenberger observer algorithms are used for estimating unknown states. The NMPC-based controller operation is simulated, and the result reveals the similar efficiency of observers. Moreover, the NMPC control approach is compared with a proportional controller which shows great improvements in the response of the quadrotor. The experiment showed that our control method is sufficiently fast for practical implementations, and it can solve the trajectory tracking problem properly even for complex paths. This thesis is concluded by stating a summary of contributions and some potential future works

Safe and Secure Control of Connected and Automated Vehicles

Evolution of Connected and Automated Vehicles (CAV), as an important class of Cyber-Physical Systems (CPS), plays a crucial role in providing innovative services in transport and traffic management. Vehicle platoons, as a set of CAV, forming a string of connected vehicles, have offered significant enhancements in traffic management, energy consumption, and safety in intelligent transportation systems. However, due to the existence of the cyber layer in these systems, subtle security related issues have been underlined and need to be taken into account with sufficient attention. In fact, despite the benefits brought by the platoons, they potentially suffer from insecure networks which provide the connectivity among the vehicles participating in the platoon which makes these systems prone to be under the risk of cyber attacks. One (or more) external intelligent intruder(s) might attack one (or more) of the vehicles participating in a platoon. In this respect, the need for a safe and secure driving experience is highly sensible and crucial. Hence, we will concentrate on improving the safety and security of CAVs in different scenarios by taking advantage of security related approaches and CAV control systems. In this thesis, we are going to focus on two main levels of platoon control, namely I) High level secure platoon control, and II) Low level secure platoon control. In particular, in the high level part, we consider platoons with arbitrary inter-vehicular communication topoloy whereby the vehicles are able to exchange their driving data with each other through DSRC-based environment. The whole platoon is modeled using graph-theoretic notions by denoting the vehicles as the nodes and the inter-vehicular communication quality as the edge weights. We study the security of the vehicle platoon exposed to cyber attacks using a novel game-theoretic approach. The platoon topologies under investigation are directed (called predecessor following) or undirected (bidirectional) weighted graphs. The attacker-detector game is defined as follows. The attacker targets some vehicles in the platoon to attack and the detector deploys monitoring sensors on the vehicles. The attacker's objective is to be as stealthy to the sensors as possible while the detector tries to place the monitoring sensors to detect the attack impact as much as he can. The existence of equilibrium strategies for this game is investigated based on which the detector can choose specific vehicles to put his sensors on and increase the security level of the system. Moreover, we study the effect of adding (or removing) communication links between vehicles on the game value. We then address the same problem while investigating the optimal actuator placement strategy needed by the defender to mitigate the effects of the attack. In this respect, the energy needed by the attacker to steer the consensus follower-leader dynamics of the system towards his desired direction is used as the game payoff. Simulation and experimental results conducted on a vehicle platoon setup using Robotic Operating System (ROS) demonstrate the effectiveness of our analyses. In the low level platoon control, we exploit novel secure model predictive controller algorithms to provide suitable countermeasure against a prevalent data availability attack, namely Denial-of-Service (DoS) attack. A DoS intruder can endanger the security of platoon by jamming the communication network among the vehicles which is responsible to transmit inter-vehicular data throughout the platoon. In other words, he may cause a failure in the network by jamming it or injecting a huge amount of delay, which in essence makes the outdated transferred data useless. This can potentially result in huge performance degradation or even hazardous collisions. We propose novel secure distributed nonlinear model predictive control algorithms for both static and dynamic nonlinear heterogeneous platoons which are capable of handling DoS attack performed on a platoon equipped by different communication topologies and at the same time they guarantee the desired formation control performance. Notably, in the dynamic case, our proposed method is capable of providing safe and secure control of the platoon in which arbitrary vehicles might perform cut-in and/or cut-out maneuvers. Convergence time analysis of the system are also investigated. Simulation results on a sample heterogeneous attacked platoon exploiting two-predecessor follower communication environment demonstrates the fruitfulness of the method

Control and optimization methods for problems in intelligent transportation systems

This thesis aims to address three research topics in intelligent transportation systems which include multi-intersection traffic light control based on stochastic flow models with delays and blocking, optimization of mobility-on-demand systems using event-driven receding horizon control and the optimal control of lane change maneuvers in highways for connected and automated vehicles. First, for the traffic light control work, we extend Stochastic Flow Models (SFMs), used for a large class of discrete event and hybrid systems, by including the delays which typically arise in flow movements, as well as blocking effects due to space constraints. We apply this framework to the multi-intersection traffic light control problem by including transit delays for vehicles moving from one intersection to the next and possible blocking between two intersections. Using Infinitesimal Perturbation Analysis (IPA) for this SFM with delays and possible blocking, we derive new on-line gradient estimates of several congestion cost metrics with respect to the controllable green and red cycle lengths. The IPA estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. The second problem relates to developing an event-driven Receding Horizon Control (RHC) scheme for a Mobility-on-Demand System (MoDS) in a transportation network where vehicles may be shared to pick up and drop off passengers so as to minimize a weighted sum of passenger waiting and traveling times. Viewed as a discrete event system, the event-driven nature of the controller significantly reduces the complexity of the vehicle assignment problem, thus enabling its real-time implementation. Finally, optimal control policies are derived for a Connected Automated Vehicle (CAV) cooperating with neighboring CAVs in order to implement a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. For the first phase, the maneuver time subject to safety constraints and subsequently the associated energy consumption of all cooperating vehicles in this maneuver are optimized. For the second phase, time and energy are jointly optimized based on three different solution methods including a real-time approach based on Control Barrier Functions (CBFs). Structural properties of the optimal policies which simplify the solution derivations are provided in the case of the longitudinal maneuver, leading to analytical optimal control expressions. The solutions, when they exist, are guaranteed to satisfy safety constraints for all vehicles involved in the maneuver

Trajectory optimization based on recursive B-spline approximation for automated longitudinal control of a battery electric vehicle

Diese Arbeit beschreibt ein neuartiges Verfahren zur linearen und nichtlinearen gewichteten Kleinste-Quadrate-Approximation einer unbeschränkten Anzahl von Datenpunkten mit einer B-Spline-Funktion. Das entwickelte Verfahren basiert auf iterativen Algorithmen zur Zustandsschätzung und sein Rechenaufwand nimmt linear mit der Anzahl der Datenpunkte zu. Das Verfahren ermöglicht eine Verschiebung des beschränkten Definitionsbereichs einer B-Spline-Funktion zur Laufzeit, sodass der aktuell betrachtete Datenpunkt ungeachtet des anfangs gewählten Definitionsbereichs bei der Approximation berücksichtigt werden kann. Zudem ermöglicht die Verschiebeoperation die Reduktion der Größen der Matrizen in den Zustandsschätzern zur Senkung des Rechenaufwands sowohl in Offline-Anwendungen, in denen alle Datenpunkte gleichzeitig zur Verarbeitung vorliegen, als auch in Online-Anwendungen, in denen in jedem Zeitschritt weitere Datenpunkte beobachtet werden. Das Trajektorienoptimierungsproblem wird so formuliert, dass das Approximationsverfahren mit Datenpunkten aus Kartendaten eine B-Spline-Funktion berechnet, die die gewünschte Geschwindigkeitstrajektorie bezüglich der Zeit repräsentiert. Der Rechenaufwand des resultierenden direkten Trajektorienoptimierungsverfahrens steigt lediglich linear mit der unbeschränkten zeitlichen Trajektorienlänge an. Die Kombination mit einem adaptiven Modell des Antriebsstrangs eines batterie-elektrischen Fahrzeugs mit festem Getriebeübersetzungsverhältnis ermöglicht die Optimierung von Geschwindigkeitstrajektorien hinsichtlich Fahrzeit, Komfort und Energieverbrauch. Das Trajektorienoptimierungsverfahren wird zu einem Fahrerassistenzsystem für die automatisierte Fahrzeuglängsführung erweitert, das simulativ und in realen Erprobungsfahrten getestet wird. Simulierte Fahrten auf der gewählten Referenzstrecke benötigten bis zu 3,4 % weniger Energie mit der automatisierten Längsführung als mit einem menschlichen Fahrer bei derselben Durchschnittsgeschwindigkeit. Für denselben Energieverbrauch erzielt die automatisierte Längsführung eine 2,6 % höhere Durchschnittsgeschwindigkeit als ein menschlicher Fahrer