A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

Traffic management system for smart road networks reserved for self-driving cars

A model of a smart road network consisting of unsignalised intersections and smart roads connecting them is considered in this work with the aim of presenting a traffic management system for self-driving cars (or, more generally, autonomous vehicles) which travel the network. The proposed system repeatedly solves a set of mathematical programming problems (each of them relative to a single intersection or to a single road stretch of the network) within a decentralised control scheme in which each local intersection controller and each local road controller communicates with the fully autonomous vehicles in order to receive travel data from vehicles and to provide speed profiles to them once determined the optimal solution of the problem. In order to reduce the computational effort required to provide the optimal solution, a discrete-time approach is adopted so that, in each time interval, a limited number of vehicles are taken into consideration; in this way, solutions can be determined in a very short time thus making the proposed model compatible with a practical application to real traffic systems. The proposed model is general enough, and can be adapted to different scenarios of smart road networks reserved for self-driving cars

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

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Distributed, decentralised and compensational mechanisms for platoon formation

Verkehrsprobleme nehmen mit der weltweiten Urbanisierung und der Zunahme der Anzahl der Fahrzeuge pro Kopf zu. Platoons, eine Formation von eng hintereinander fahrenden Fahrzeugen, stellen sich als mögliche Lösung dar, da bestehende Forschungen darauf hinweisen, dass sie zu einer besseren Straßenauslastung beitragen, den Kraftstoffverbrauch und die Emissionen reduzieren und Engpässe schneller entlasten können. Rund um das Thema Platooning gibt es viele Aspekte zu erforschen: Sicherheit, Stabilität, Kommunikation, Steuerung und Betrieb, die allesamt notwendig sind, um den Einsatz von Platooning im Alltagsverkehr näher zu bringen. Während in allen genannten Bereichen bereits umfangreiche Forschungen durchgeführt wurden, gibt es bisher nur wenige Arbeiten, die sich mit der logischen Gruppierung von Fahrzeugen in Platoons beschäftigen. Daher befasst sich diese Arbeit mit dem noch wenig erforschten Problem der Platoonbildung, wobei sich die vorhandenen Beispiele mit auf Autobahnen fahrenden Lastkraftwagen beschäftigen. Diese Fälle befinden sich auf der strategischen und taktischen Ebene der Planung, da sie von einem großen Zeithorizont profitieren und die Gruppierung entsprechend optimiert werden kann. Die hier vorgestellten Ansätze befinden sich hingegen auf der operativen Ebene, indem Fahrzeuge aufgrund der verteilten und dezentralen Natur dieser Ansätze spontan und organisch gruppiert und gesteuert werden. Dadurch entstehen sogenannte opportunistische Platoons, die aufgrund ihrer Flexibilität eine vielversprechende Voraussetzung für alle Netzwerkarte bieten könnten. Insofern werden in dieser Arbeit zwei neuartige Algorithmen zur Bildung von Platoons vorgestellt: ein verteilter Ansatz, der von klassischen Routing-Problemen abgeleitet wurde, und ein ergänzender dezentraler kompensatorischer Ansatz. Letzteres nutzt automatisierte Verhandlungen, um es den Fahrzeugen zu erleichtern, sich auf der Basis eines monetären Austausches in einem Platoon zu organisieren. In Anbetracht der Tatsache, dass alle Verkehrsteilnehmer über eine Reihe von Präferenzen, Einschränkungen und Zielen verfügen, muss das vorgeschlagene System sicherstellen, dass jede angebotene Lösung für die einzelnen Fahrzeuge akzeptabel und vorteilhaft ist und den möglichen Aufwand, die Kosten und die Opfer überwiegt. Dies wird erreicht, indem den Platooning-Fahrzeugen eine Form von Anreiz geboten wird, im Sinne von entweder Kostensenkung oder Ampelpriorisierung. Um die vorgeschlagenen Algorithmen zu testen, wurde eine Verkehrssimulation unter Verwendung realer Netzwerke mit realistischer Verkehrsnachfrage entwickelt. Die Verkehrsteilnehmer wurden in Agenten umgewandelt und mit der notwendigen Funktionalität ausgestattet, um Platoons zu bilden und innerhalb dieser zu operieren. Die Anwendbarkeit und Eignung beider Ansätze wurde zusammen mit verschiedenen anderen Aspekten untersucht, die den Betrieb von Platoons betreffen, wie Größe, Verkehrszustand, Netzwerkpositionierung und Anreizmethoden. Die Ergebnisse zeigen, dass die vorgeschlagenen Mechanismen die Bildung von spontanen Platoons ermöglichen. Darüber hinaus profitierten die teilnehmenden Fahrzeuge mit dem auf verteilter Optimierung basierenden Ansatz und unter Verwendung kostensenkender Anreize unabhängig von der Platoon-Größe, dem Verkehrszustand und der Positionierung, mit Nutzenverbesserungen von 20% bis über 50% im Vergleich zur untersuchten Baseline. Bei zeitbasierten Anreizen waren die Ergebnisse uneinheitlich, wobei sich der Nutzen einiger Fahrzeuge verbesserte, bei einigen keine Veränderung eintrat und bei anderen eine Verschlechterung zu verzeichnen war. Daher wird die Verwendung solcher Anreize aufgrund ihrer mangelnden Pareto-Effizienz nicht empfohlen. Der kompensatorische und vollständig dezentralisierte Ansatz weißt einige Vorteile auf, aber die daraus resultierende Verbesserung war insgesamt vernachlässigbar. Die vorgestellten Mechanismen stellen einen neuartigen Ansatz zur Bildung von Platoons dar und geben einen aussagekräftigen Einblick in die Mechanik und Anwendbarkeit von Platoons. Dies schafft die Voraussetzungen für zukünftige Erweiterungen in der Planung, Konzeption und Implementierung effektiverer Infrastrukturen und Verkehrssysteme.Traffic problems have been on the rise corresponding with the increase in worldwide urbanisation and the number of vehicles per capita. Platoons, which are a formation of vehicles travelling close together, present themselves as a possible solution, as existing research indicates that they can contribute to better road usage, reduce fuel consumption and emissions and decongest bottlenecks faster. There are many aspects to be explored pertaining to the topic of platooning: safety, stability, communication, controllers and operations, all of which are necessary to bring platoons closer to use in everyday traffic. While extensive research has already made substantial strides in all the aforementioned fields, there is so far little work on the logical grouping of vehicles in platoons. Therefore, this work addresses the platoon formation problem, which has not been heavily researched, with existing examples being focused on large, freight vehicles travelling on highways. These cases find themselves on the strategic and tactical level of planning since they benefit from a large time horizon and the grouping can be optimised accordingly. The approaches presented here, however, are on the operational level, grouping and routing vehicles spontaneously and organically thanks to their distributed and decentralised nature. This creates so-called opportunistic platoons which could provide a promising premise for all networks given their flexibility. To this extent, this thesis presents two novel platoon forming algorithms: a distributed approach derived from classical routing problems, and a supplementary decentralised compensational approach. The latter uses automated negotiation to facilitate vehicles organising themselves in a platoon based on monetary exchanges. Considering that all traffic participants have a set of preferences, limitations and goals, the proposed system must ensure that any solution provided is acceptable and beneficial for the individual vehicles, outweighing any potential effort, cost and sacrifices. This is achieved by offering platooning vehicles some form of incentivisation, either cost reductions or traffic light prioritisation. To test the proposed algorithms, a traffic simulation was developed using real networks with realistic traffic demand. The traffic participants were transformed into agents and given the necessary functionality to build platoons and operate within them. The applicability and suitability of both approaches were investigated along with several other aspects pertaining to platoon operations such as size, traffic state, network positioning and incentivisation methods. The results indicate that the mechanisms proposed allow for spontaneous platoons to be created. Moreover, with the distributed optimisation-based approach and using cost-reducing incentives, participating vehicles benefited regardless of the platoon size, traffic state and positioning, with utility improvements ranging from 20% to over 50% compared to the studied baseline. For time-based incentives the results were mixed, with the utility of some vehicles improving, some seeing no change and for others, deteriorating. Therefore, the usage of such incentives would not be recommended due to their lack of Pareto-efficiency. The compensational and completely decentralised approach shows some benefits, but the resulting improvement was overall negligible. The presented mechanisms are a novel approach to platoon formation and provide meaningful insight into the mechanics and applicability of platoons. This sets the stage for future expansions into planning, designing and implementing more effective infrastructures and traffic systems