Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon

Motivated by the advancement in connected and autonomous vehicle technologies, this paper develops a novel car-following control scheme for a platoon of connected and autonomous vehicles on a straight highway. The platoon is modeled as an interconnected multi-agent dynamical system subject to physical and safety constraints, and it uses the global information structure such that each vehicle shares information with all the other vehicles. A constrained optimization based control scheme is proposed to ensure an entire platoon’s transient traffic smoothness and asymptotic dynamic performance. By exploiting the solution properties of the underlying optimization problem and using primal-dual formulation, this paper develops dual based distributed algorithms to compute optimal solutions with proven convergence. Furthermore, the asymptotic stability of the unconstrained linear closed-loop system is established. These stability analysis results provide a principle to select penalty weights in the underlying optimization problem to achieve the desired closed-loop performance for both the transient and the asymptotic dynamics. Extensive numerical simulations are conducted to validate the efficiency of the proposed algorithms.     

Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are emerging components of intelligent transport systems (ITS) based on which vehicles can drive in a cooperative way and, hence, significantly improve traffic flow efficiency. However, due to the high vehicle mobility, the unreliable vehicular communications such as packet loss and transmission delay can impair the performance of the cooperative driving system (CDS). In addition, the downstream traffic information collected by roadside sensors in the V2I communication may introduce measurement errors, which also affect the performance of the CDS. The goal of this paper is to bridge the gap between traffic flow modelling and communication approaches in order to build up better cooperative traffic systems. To this end, we aim to develop an enhanced cooperative microscopic (car-following) traffic model considering V2V and V2I communication (or V2X for short), and investigate how vehicular communications affect the vehicle cooperative driving, especially in traffic disturbance scenarios. For these purposes, we design a novel consensus-based vehicle control algorithm for the CDS, in which not only the local traffic flow stability is guaranteed, but also the shock waves are supposed to be smoothed. The IEEE 802.11p, the defacto vehicular networking standard, is selected as the communication protocols, and the roadside sensors are deployed to collect the average speed in the targeted area as the downstream traffic reference. Specifically, the imperfections of vehicular communication as well as the measured information noise are taken into account. Numerical results show the efficiency of the proposed scheme. This paper attempts to theoretically investigate the relationship between vehicular communications and cooperative driving, which is needed for the future deployment of both connected vehicles and infrastructure (i.e. V2X).     

Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation

This paper examines the impact of having cooperative adaptive cruise control (CACC) embedded vehicles on traffic flow characteristics of a multilane highway system. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce traffic congestion resulting from the acceleration/deceleration of the operating vehicles. An agent-based microscopic traffic simulation model (Flexible Agent-based Simulator of Traffic) is designed specifically to examine the impact of these intelligent vehicles on traffic flow. The flow rate of cars, the travel time spent, and other metrics indicating the evolution of traffic congestion throughout the lifecycle of the model are analyzed. Different CACC penetration levels are studied. The results indicate a better traffic flow performance and higher capacity in the case of CACC penetration compared to the scenario without CACC-embedded vehicles.     

Platoon based cooperative driving model with consideration of realistic inter-vehicle communication

Recent developments of information and communication technologies (ICT) have enabled vehicles to timely communicate with each other through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. Nevertheless, unreliable vehicular communications also introduce packet loss and transmission delay when vehicular kinetic information or control commands are disseminated among vehicles, which brings more challenges in the system modeling and optimization. Currently, no data has been yet available for the calibration and validation of a model for ITS, and most research has been only conducted for a theoretical point of view. Along this line, this paper focuses on the (theoretical) development of a more general (microscopic) traffic model which enables the cooperative driving behavior via a so-called inter-vehicle communication (IVC). To this end, we design a consensus-based controller for the cooperative driving system (CDS) considering (intelligent) traffic flow that consists of many platoons moving together. More specifically, the IEEE 802.11p, the de facto vehicular networking standard required to support ITS applications, is selected as the IVC protocols of the CDS, in order to investigate how the vehicular communications affect the features of intelligent traffic flow. This study essentially explores the relationship between IVC and cooperative driving, which can be exploited as the reference for the CDS optimization and design.     

A traffic congestion detection and information dissemination scheme for urban expressways using vehicular networks

The cooperative vehicle-infrastructure technologies have enabled vehicles to collect and exchange traffic information in real time. Therefore, it is possible to use Vehicular Ad-hoc NETworks (VANETs) for detecting traffic congestion on urban expressways. However, because of the special topology of urban expressways (consisting of both major and auxiliary roadways), the existing traffic congestion detection methods using VANETs do not work very well. In addition, the existing dissemination methods of congestion information lack the necessary control mechanism, so the information may be disseminated to irrelevant geographical areas. This paper proposes a congestion detection and notification scheme using VANETs for urban expressways. The scheme adopts a simplified Doppler frequency shift method to estimate and differentiate traffic conditions for major and auxiliary roadways. Vehicular cooperation and human cognition are introduced to improve the estimation accuracy and to describe the overall traffic conditions. Additionally, the scheme develops a spatial–temporal effectiveness model based on the potential energy theory to control the dissemination area and survival time of the congestion information. Meanwhile, the proposed scheme uses several broadcast control mechanisms to alleviate vehicular network congestion. Simulations through TransModeler indicate that our scheme ensures the accuracy of the estimation of congestion degree. Consequently, the scheme can provide effective references for driving decision-making and path-planning.     

Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing

This paper presents a thorough microscopic simulation investigation of a recently proposed methodology for highway traffic estimation with mixed traffic, i.e., traffic comprising both connected and conventional vehicles, which employs only speed measurements stemming from connected vehicles and a limited number (sufficient to guarantee observability) of flow measurements from spot sensors. The estimation scheme is tested using the commercial traffic simulator Aimsun under various penetration rates of connected vehicles, employing a traffic scenario that features congested as well as free-flow conditions. The case of mixed traffic comprising conventional and connected vehicles equipped with adaptive cruise control, which feature a systematically different car-following behavior than regular vehicles, is also considered. In both cases, it is demonstrated that the estimation results are satisfactory, even for low penetration rates.     

Influence of connected and autonomous vehicles on traffic flow stability and throughput

The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.     

Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections

Connected vehicles will change the modes of future transportation management and organization, especially at intersections. In this paper, we propose a distributed conflict-free cooperation method for multiple connected vehicles at unsignalized intersections. We firstly project the approaching vehicles from different traffic movements into a virtual lane and introduce a conflict-free geometry topology considering the conflict relationship of involved vehicles, thus constructing a virtual platoon. Then we present the modeling of communication topology to describe two modes of information transmission between vehicles. Finally, a distributed controller is designed to stabilize the virtual platoon for conflict-free cooperation at intersections. Numerical simulations validate the effectiveness of this method.     

A hybrid controller for autonomous vehicles driving on automated highways

This paper addresses the problem of the hybrid control of autonomous vehicles driving on automated highways. Vehicles are autonomous, so they do not communicate with each other nor with the infrastructure. Two problems have to be dealt with: a vehicle driving in a single-lane highway must never collide with its leading vehicle; and a vehicle entering the highway at a designated entry junction must be able to merge from the merging lane to the main lane, again without any collision. To solve these problems, we equip each vehicle with a hybrid controller, consisting of several continuous control laws embedded inside a finite state automaton. The automaton specifies when a given vehicle must enter the highway, merge into the main lane, yield to other vehicles, exit from the highway, and so on. The continuous control laws specify what acceleration the vehicle must have in order to avoid collisions with nearby vehicles. By carefully designing these control laws and the conditions guarding the automaton transitions, we are able to demonstrate three important results. First, we state the initial conditions guaranteeing that a following vehicle never collides with its leading vehicle. Second, we extend this first result to a lane of autonomous vehicles. Third, we prove that if all the vehicles are equipped with our hybrid controller, then no collision can ever occur, and all vehicles either merge successfully or are forced to drop out when they reach the end of their merging lane. Finally, we show the outcome of a highway microsimulation modelled after the Katy Corridor near Houston, Texas: our single-lane highway can accommodate 4000 vehicles per hour with neither drop-outs nor traffic congestion. It is entirely programmed in SHIFT, a hybrid systems simulation language developed at UC Berkeley by the PATH group. This shows that SHIFT is a well suited language for designing safe control laws for autonomous highway systems, among others.     

A control theoretic formulation of green driving strategies based on inter-vehicle communications

Various green driving strategies have been proposed to smooth traffic flow and lower pollutant emissions and fuel consumption in stop-and-go traffic. In this paper, we present a control theoretic formulation of distributed, cooperative green driving strategies based on inter-vehicle communications (IVCs). The control variable is the advisory speed limit, which is designed to smooth a following vehicle’s speed profile without changing its average speed. We theoretically analyze the performance of a constant independent and three simple cooperative green driving strategies and present three rules for effective and robust strategies. We then develop a distributed cooperative green driving strategy, in which the advisory speed limit is first independently calculated by each individual vehicle and then averaged among green driving vehicles through IVC. By simulations with Newell’s car-following model and the Comprehensive Modal Emissions Model (CMEM), we demonstrate that such a strategy is effective and robust independently as well as cooperatively for different market penetration rates of IVC-equipped vehicles and communication delays. In particular, even when 5% of the vehicles implement the green driving strategy and the IVC communication delay is 60 s, the fuel consumption can be reduced by up to 15%. Finally we discuss some future extensions.     

Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment

Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.     

Energy saving potentials of connected and automated vehicles

Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles.     

A data fusion approach for real-time traffic state estimation in urban signalized links

Real-time estimation of the traffic state in urban signalized links is valuable information for modern traffic control and management. In recent years, with the development of in-vehicle and communication technologies, connected vehicle data has been increasingly used in literature and practice. In this work, a novel data fusion approach is proposed for the high-resolution (second-by-second) estimation of queue length, vehicle accumulation, and outflow in urban signalized links. Required data includes input flow from a fixed detector at the upstream end of the link as well as location and speed of the connected vehicles. A probability-based approach is derived to compensate the error associated with low penetration rates while estimating the queue tail location, which renders the proposed methodology more robust to varying penetration rates of connected vehicles. A well-defined nonlinear function based on traffic flow theory is developed to attain the number of vehicles inside the queue based on queue tail location and average speed of connected vehicles. The overall scheme is thoroughly tested and demonstrated in a realistic microscopic simulation environment for three types of links with different penetration rates of connected vehicles. In order to test the efficiency of the proposed methodology in case that data are available at higher sampling times, the estimation procedure is also demonstrated for different time resolutions. The results demonstrate the efficiency and accuracy of the approach for high-resolution estimation, even in the presence of measurement noise.     

Experimental validation of connected automated vehicle design among human-driven vehicles

In this paper, we present results regarding the experimental validation of connected automated vehicle design. In order for a connected automated vehicle to integrate well with human-dominated traffic, we propose a class of connected cruise control algorithms with feedback structure originated from human driving behavior. We test the connected cruise controllers using real vehicles under several driving scenarios while utilizing beyond-line-of-sight motion information obtained from neighboring human-driven vehicles via vehicle-to-everything (V2X) communication. We experimentally show that the design is robust against variations in human behavior as well as changes in the topology of the communication network. We demonstrate that both safety and energy efficiency can be significantly improved for the connected automated vehicle as well as for the neighboring human-driven vehicles and that the connected automated vehicle may bring additional societal benefits by mitigating traffic waves.     

Orchestration of driving simulator scenarios based on dynamic actor preparation and automated action planning

In driving simulation, a scenario includes definitions of the road environment, the traffic situation, simulated vehicles’ interactions with the participant’s vehicle and measurements that need to be collected. The scenarios need to be designed in such a way that the research questions to be studied can be answered, which commonly imply exposing the participant for a couple of predefined specific situations that has to be both realistic and repeatable. This article presents an integrated algorithm based on Dynamic Actor Preparation and Automated Action Planning to control autonomous simulated vehicles in the simulation in order to generate predefined situations. This algorithm is thus able to plan driving actions for autonomous vehicles based on specific tasks with relevant contextual information as well as handling longitudinal transportation of simulated vehicles based on the contextual information in an automated manner. The conducted experiment shows that the algorithm is able to guarantee repeatability under autonomous traffic flow. The presented algorithm can benefit not only the driving simulation community, but also relevant areas, such as autonomous vehicle and in-vehicle device design by providing them with an algorithm for target pursue and driving task accomplishment, which can be used to design a human-vehicle cooperation system in the coming era of autonomous driving.     

Definition of a merging assistant strategy using intelligent vehicles

In the area of active traffic management, new technologies provide opportunities to improve the use of current infrastructure. Vehicles equipped with in-car communication systems are capable of exchanging messages with the infrastructure and other vehicles. This new capability offers many opportunities for traffic management. This paper presents a novel merging assistant strategy that exploits the communication capabilities of intelligent vehicles. The proposed control requires the cooperation of equipped vehicles on the main carriageway in order to create merging gaps for on-ramp vehicles released by a traffic light. The aim is to reduce disruptions to the traffic flow created by the merging vehicles. This paper focuses on the analytical formulation of the control algorithm, and the traffic flow theories used to define the strategy. The dynamics of the gap formation derived from theoretical considerations are validated using a microscopic simulation. The validation indicates that the control strategy mostly developed from macroscopic theory well approximates microscopic traffic behaviour. The results present encouraging capabilities of the system. The size and frequency of the gaps created on the main carriageway, and the space and time required for their creation are compatible with a real deployment of the system. Finally, we summarise the results of a previous study showing that the proposed merging strategy reduces the occurrence of congestion and the number of late-merging vehicles. This innovative control strategy shows the potential of using intelligent vehicles for facilitating the merging manoeuvre through use of emerging communications technologies.     

The impacts of connected vehicle technology on network-wide traffic operation and fuel consumption under various incident scenarios

ABSTRACT

Incidents are a major source of traffic congestion and can lead to long and unpredictable delays, deteriorating traffic operations and adverse environmental impacts. The emergence of connected vehicles and communication technologies has enabled travelers to use real-time traffic information. The ability to exchange traffic information among vehicles has tremendous potential impacts on network performance especially in the case of non-recurrent congestion. To this end, this paper utilizes a microscopic simulation model of traffic in El Paso, Texas to investigate the impacts of incidents on traffic operation and fuel consumption at different market penetration rates (MPR) of connected vehicles. Several scenarios are implemented and tested to determine the impacts of incidents on network performance in an urban area. The scenarios are defined by changing the duration of incidents and the number of lanes closed. This study also shows how communication technology affects network performance in response to congestion. The results of the study demonstrate the potential effectiveness of connected vehicle technology in improving network performance. For an incident with a duration of 900?s and MPR of 80%, total fuel consumption and total travel time decreased by approximately 20%; 26% was observed in network-wide travel time and fuel consumption at 100% MPR.     

Rolling horizon control framework for driver assistance systems. Part II: Cooperative sensing and cooperative control

This contribution furthers the control framework for driver assistance systems in Part I to cooperative systems, where equipped vehicles can exchange relevant information via vehicle-to-vehicle communication to improve the awareness of the ambient situation (cooperative sensing) and to manoeuvre together under a common goal (cooperative control). To operationalize the cooperative sensing strategy, the framework is applied to the development of a multi-anticipative controller, where an equipped vehicle uses information from its direct predecessor to predict the behaviour of its pre-predecessor. To operationalize the cooperative control strategy, we design cooperative controllers for sequential equipped vehicles in a platoon, where they collaborate to optimise a joint objective. The cooperative control strategy is not restricted to cooperation between equipped vehicles. When followed by a human-driven vehicle, equipped vehicles can still exhibit cooperative behaviour by predicting the behaviour of the human-driven follower, even if the prediction is not perfect.The performance of the proposed controllers are assessed by simulating a platoon of 11 vehicles with reference to the non-cooperative controller proposed in Part I. Evaluations show that the multi-anticipative controller generates smoother behaviour in accelerating phase. By a careful choice of the running cost specification, cooperative controllers lead to smoother decelerating behaviour and more responsive and agile accelerating behaviour compared to the non-cooperative controller. The dynamic characteristics of the proposed controllers provide new insights into the potential impact of cooperative systems on traffic flow operations, particularly at the congestion head and tail.     

Impacts of driving patterns on tank-to-wheel energy use of plug-in hybrid electric vehicles

We evaluate the implications of a range of driving patterns on the tank-to-wheel energy use of plug-in hybrid electric vehicles. The driving patterns, which reflect short distance, low speed, and congested city driving to long distance, high speed, and uncongested highway driving, are estimated using an approach that involves linked traffic assignment and vehicle motion models. We find substantial variation in tank-to-wheel energy use of plug-in hybrid electric vehicles across driving patterns. Tank-to-wheel petroleum energy use on a per kilometer basis is lowest for the city and highest for the highway driving, with the opposite holding for a conventional internal combustion engine vehicle.     

Connected cruise control among human-driven vehicles: Experiment-based parameter estimation and optimal control design

In this paper, we consider connected cruise control design in mixed traffic flow where most vehicles are human-driven. We first propose a sweeping least square method to estimate in real time feedback gains and driver reaction time of human-driven vehicles around the connected automated vehicle. Then we propose an optimal connected cruise controller based on the mean dynamics of human driving behavior. We test the performance of both the estimation algorithm and the connected cruise control algorithm using experimental data. We demonstrate that by combining the proposed estimation algorithm and the optimal controller, the connected automated vehicle has significantly improved performance compared to a human-driven vehicle.