openACC. An open database of car-following experiments to study the properties of commercial ACC systems

Commercial Adaptive Cruise Control (ACC) systems are increasingly available as standard options in modern vehicles. At the same time, still little information is openly available on how these systems actually operate and how different is their behavior, depending on the vehicle manufacturer or model.T o reduce this gap, the present paper summarizes the main features of the openACC, an open-access database of different car-following experiments involving a total of 16 vehicles, 11 of which equipped with state-of-the-art commercial ACC systems. As more test campaigns will be carried out by the authors, OpenACC will evolve accordingly. The activity is performed within the framework of the openData policy of the European Commission Joint Research Centre with the objective to engage the whole scientific community towards a better understanding of the properties of ACC vehicles in view of anticipating their possible impacts on traffic flow and prevent possible problems connected to their widespread. A first preliminary analysis on the properties of the 11 ACC systems is conducted in order to showcase the different research topics that can be studied within this open science initiative

2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018

The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies. As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency. In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community. In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor

Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow. The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios

Electric vehicle chassis dynamometer test methods at JPL and their correlation to track tests

Early in its electric vehicle (EV) test program, JPL recognized that EV test procedures were too vague and too loosely defined to permit much meaningful data to be obtained from the testing. Therefore, JPL adopted more stringent test procedures and chose the chassis dynamometer rather than the track as its principal test technique. Through the years, test procedures continued to evolve towards a methodology based on chassis dynamometers which would exhibit good correlation with track testing. Based on comparative dynamometer and track test results on the ETV-1 vehicle, the test methods discussed in this report demonstrate a means by which excellent track-to-dynamometer correlation can be obtained

Microscopic Modeling of Human and Automated Driving: Towards Traffic-Adaptive Cruise Control

The thesis is composed of two main parts. The first part deals with a microscopic traffic flow theory. Models describing the individual acceleration, deceleration and lane-changing behavior are formulated and the emerging collective traffic dynamics are investigated by means of numerical simulations. The models and simulation tools presented provide the methodical prerequisites for the second part of the thesis in which a novel concept of a traffic-adaptive control strategy for ACC systems is presented. The impact of such systems on the traffic dynamics can solely be investigated and assessed by traffic simulations. The focus is on future adaptive cruise control (ACC) systems and their potential applications in the context of vehicle-based intelligent transportation systems. In order to ensure that ACC systems are implemented in ways that improve rather than degrade traffic conditions, the thesis proposes an extension of ACC systems towards traffic-adaptive cruise control by means of implementing an actively jam-avoiding driving strategy. The newly developed traffic assistance system introduces a driving strategy layer which modifies the driver's individual settings of the ACC driving parameters depending on the local traffic situation. Whilst the conventional operational control layer of an ACC system calculates the response to the input sensor data in terms of accelerations and decelerations on a short time scale, the automated adaptation of the ACC driving parameters happens on a somewhat longer time scale of, typically, minutes. By changing only temporarily the comfortable parameter settings of the ACC system in specific traffic situations, the driving strategy is capable of improving the traffic flow efficiency whilst retaining the comfort for the driver. The traffic-adaptive modifications are specified relative to the driver settings in order to maintain the individual preferences. The proposed system requires an autonomous real-time detection of the five traffic states by each ACC-equipped vehicle. The formulated algorithm is based on the evaluation of the locally available data such as the vehicle's velocity time series and its geo-referenced position (GPS) in conjunction with a digital map. It is assumed that the digital map is complemented by information about stationary bottlenecks as most of the observed traffic flow breakdowns occur at these fixed locations. By means of a heuristic, the algorithm determines which of the five traffic states mentioned above applies best to the actual traffic situation. Optionally, inter-vehicle and infrastructure-to-car communication technologies can be used to further improve the accuracy of determining the respective traffic state by providing non-local information. By means of simulation, we found that the automatic traffic-adaptive driving strategy improves traffic stability and increases the effective road capacity. Depending on the fraction of ACC vehicles, the driving strategy "passing a bottleneck" effects a reduction of the bottleneck strength and therefore delays (or even prevents) the breakdown of traffic flow. Changing to the driving mode "leaving the traffic jam" increases the outflow from congestion resulting in reduced queue lengths in congested traffic and, consequently, a faster recovery to free flow conditions. The current travel time (as most important criterion for road users) and the cumulated travel time (as an indicator of the system performance) are used to evaluate the impact on the quality of service. While traffic congestion in the reference scenario was completely eliminated when simulating a proportion of 25% ACC vehicles, travel times were significantly reduced even with much lower penetration rates. Moreover, the cumulated travel times decreased consistently with the increase in the proportion of ACC vehicles.In der Arbeit wird ein neues verkehrstelematisches Konzept für ein verkehrseffizientes Fahrverhalten entwickelt und als dezentrale Strategie zur Vermeidung und Auflösung von Verkehrsstaus auf Richtungsfahrbahnen vorgestellt. Die operative Umsetzung erfolgt durch ein ACC-System, das um eine, auf Informationen über die lokale Verkehrssituation basierende, automatisierte Fahrstrategie erweitert wird. Die Herausforderung bei einem Eingriff in das individuelle Fahrverhalten besteht - unter Berücksichtigung von Sicherheits-, Akzeptanz- und rechtlichen Aspekten - im Ausgleich der Gegensätze Fahrkomfort und Verkehrseffizienz. Während sich ein komfortables Fahren durch große Abstände bei geringen Fahrzeugbeschleunigungen auszeichnet, erfordert ein verkehrsoptimierendes Verhalten kleinere Abstände und eine schnellere Anpassung an Geschwindigkeitsänderungen der umgebenden Fahrzeuge. Als allgemeiner Lösungsansatz wird eine verkehrsadaptive Fahrstrategie vorgeschlagen, die ein ACC-System mittels Anpassung der das Fahrverhalten charakterisierenden Parameter umsetzt. Die Wahl der Parameter erfolgt in Abhängigkeit von der lokalen Verkehrssituation, die auf der Basis der im Fahrzeug zur Verfügung stehenden Informationen automatisch detektiert wird. Durch die Unterscheidung verschiedener Verkehrssituationen wird ein temporärer Wechsel in ein verkehrseffizientes Fahrregime (zum Beispiel beim Herausfahren aus einem Stau) ermöglicht. Machbarkeit und Wirkungspotenzial der verkehrsadaptiven Fahrstrategie werden im Rahmen eines mikroskopischen Modellierungsansatzes simuliert und hinsichtlich der kollektiven Verkehrsdynamik, insbesondere der Stauentstehung und Stauauflösung, auf mehrspurigen Richtungsfahrbahnen bewertet. Die durchgeführte Modellbildung, insbesondere die Formulierung eines komplexen Modells des menschlichen Fahrverhaltens, ermöglicht eine detaillierte Analyse der im Verkehr relevanten kollektiven Stabilität und einer von der Stabilität abhängigen stochastischen Streckenkapazität. Ein tieferes Verständnis der Stauentstehung und -ausbildung wird durch das allgemeine Konzept der Engstelle erreicht. Dieses findet auch bei der Entwicklung der Strategie für ein stauvermeidendes Fahrverhalten Anwendung. In der Arbeit wird die stauvermeidende und stauauflösende Wirkung eines individuellen, verkehrsadaptiven Fahrverhaltens bereits für geringe Ausstattungsgrade nachgewiesen. Vor dem Hintergrund einer zu erwartenden Verbreitung von ACC-Systemen ergibt sich damit eine vielversprechende Option für die Steigerung der Verkehrsleistung durch ein teilautomatisiertes Fahren. Der entwickelte Ansatz einer verkehrsadaptiven Fahrstrategie ist unabhängig vom ACC-System. Er erweitert dessen Funktionalität im Hinblick auf zukünftige, informationsbasierte Fahrerassistenzsysteme um eine neue fahrstrategische Dimension. Die lokale Interpretation der Verkehrssituation kann neben einer verkehrsadaptiven ACC-Regelung auch der Entwicklung zukünftiger Fahrerinformationssysteme dienen

FULLY AUTONOMOUS SELF-POWERED INTELLIGENT WIRELESS SENSOR FOR REAL-TIME TRAFFIC SURVEILLANCE IN SMART CITIES

Reliable, real-time traffic surveillance is an integral and crucial function of the 21st century intelligent transportation systems (ITS) network. This technology facilitates instantaneous decision-making, improves roadway efficiency, and maximizes existing transportation infrastructure capacity, making transportation systems safe, efficient, and more reliable. Given the rapidly approaching era of smart cities, the work detailed in this dissertation is timely in that it reports on the design, development, and implementation of a novel, fully-autonomous, self-powered intelligent wireless sensor for real-time traffic surveillance. Multi-disciplinary, innovative integration of state-of-the-art, ultra-low-power embedded systems, smart physical sensors, and the wireless sensor network—powered by intelligent algorithms—are the basis of the developed Intelligent Vehicle Counting and Classification Sensor (iVCCS) platform. The sensor combines an energy-harvesting subsystem to extract energy from multiple sources and enable sensor node self-powering aimed at potentially indefinite life. A wireless power receiver was also integrated to remotely charge the sensor’s primary battery. Reliable and computationally efficient intelligent algorithms for vehicle detection, speed and length estimation, vehicle classification, vehicle re-identification, travel-time estimation, time-synchronization, and drift compensation were fully developed, integrated, and evaluated. Several length-based vehicle classification schemes particular to the state of Oklahoma were developed, implemented, and evaluated using machine learning algorithms and probabilistic modeling of vehicle magnetic length. A feature extraction employing different techniques was developed to determine suitable and efficient features for magnetic signature-based vehicle re-identification. Additionally, two vehicle re-identification models based on matching vehicle magnetic signature from a single magnetometer were developed. Comprehensive system evaluation and extensive data analyses were performed to fine-tune and validate the sensor, ensuring reliable and robust operation. Several field studies were conducted under various scenarios and traffic conditions on a number of highways and urban roads and resulted in 99.98% detection accuracy, 97.4782% speed estimation accuracy, and 97.6951% classification rate when binning vehicles into four groups based on their magnetic length. Threshold-based, re-identification results revealed 65.25%~100% identification rate for a window of 25~500 vehicles. Voting-based, re-identification evaluation resulted in 90~100% identification rate for a window of 25~500 vehicles. The developed platform is portable and cost-effective. A single sensor node costs only $30 and can be installed for short-term use (e.g., work zone safety, traffic flow studies, roadway and bridge design, traffic management in atypical situations), as well as long-term use (e.g., collision avoidance at intersections, traffic monitoring) on highways, roadways, or roadside surfaces. The power consumption assessment showed that the sensor is operational for several years. The iVCCS platform is expected to significantly supplement other data collection methods used for traffic monitoring throughout the United States. The technology is poised to play a vital role in tomorrow’s smart cities

Modeling and understanding the implications of future truck technology scenarios for performance-based freight corridor planning

Autonomous highway vehicles are coming. The question regarding this technology has shifted from “if” to “when”. Many advocates predict that autonomous trucks, in particular, will be commercially available within the next decade, and perhaps even before autonomous passenger vehicles. This includes the emergence of autonomous and connected multi-vehicle truck platoons. Unfortunately, this technology is developing more rapidly than the public sector is preparing for it; the situation is exacerbated by the fact that the timeframe for which the technology is expected to make up a substantial portion of the motor vehicle fleet is within the current planning horizon of most transportation planning agencies. Thus, there is an immediate need to explore the implications of this technology for public agency planning purposes; exploring these implications will in turn require the development of tools to quantify the potential costs and benefits involved. With these needs in mind, the objectives of this dissertation were to (1) develop a simulation modeling and performance measurement tool that incorporates autonomous and connected truck platooning technology into the long-range planning process, (2) demonstrate how this tool can be applied to a selected interstate corridor in Georgia (I-85 and I-285), and (3) develop a scenario planning framework that uses the results from the tool to guide policy development. The model consists of an iteratively linked, supply-demand equilibrium based multi-commodity and multi-vehicle class truck trip distribution and a highway traffic assignment model, requiring changes be made to the typical travel demand modeling process to capture the characteristics of platooning technology. The results from an empirical application of this model were then used to assess the safety-, economic-, congestion-, and emissions-related impacts of platooning technology. The model developed is flexible enough to allow for a number of variations in platooning details, and was supported by a multi-variable sensitivity analysis of key input variables. This sensitivity analysis showed a range of costs and benefits of the technology, with the greatest benefits seen when labor costs were cut by allowing some of the trucks to be driverless (which would also help to alleviate a currently significant shortage of experienced truck drivers). Allowing the autonomous trucks to operate on a dedicated lane was found to tremendously reduce travel time and congestion for those trucks. However, the magnitude of cost savings depends on a variety of factors, including the deployment of platoons of different sizes, the potential for platoon-supported fuel savings, and the level of corridor traffic congestion. In some scenarios, these congestion benefits came at the expense of the convenience of other vehicles, while in other scenarios, these vehicles experienced modest congestion-reduction benefits. The emissions impacts varied; the benefits for fuel consumption and emissions for platoons were as much as 9.6% at optimal speeds. While these findings are insightful, it is important to note that they are based on a specific set of assumptions and do not consider infrastructure costs related to the implementation of the technology. Changing the assumptions in some cases could significantly change the results. This research is one of the first efforts to modify a traditional travel demand model to simulate autonomous truck platoons. One of the key components of this contribution is the use of an origin-user equilibrium (OUE) traffic assignment, a relatively new path-based assignment which allows the user to specify vehicle class and origin specific traffic flows, and assign them to the network simultaneously. The OUE assignment has yet to be explored in depth with respect to multiple truck class-based, notably platoon-inclusive freight movements. Additionally, the research presents a new application of the Freight Analysis Framework, which is a widely used freight database within the United States. Given the uncertainty associated with platooning technology, there are a number of limitations associated with this research, and the final chapter of this dissertation discusses such limitations and presents opportunities for future work. As the details of platooning technology become clearer, tools such as the one developed here can assist transportation planners with incorporating such technological advances into their planning processes.Ph.D

Field Implementation of Freeway Control

This dissertation presents a series of traffic management studies including freeway and intersection traffic modeling, estimation, control methodologies and field implementation tests. First, a traffic flow prediction method that combines the most recent traffic data with historical traffic data is studied. An autoregressive moving average with exogenous input (ARMAX) model is estimated on-line based on the most recent vehicle detector station (VDS) data. Results obtained using empirical freeway mainline and on-ramp data show that this method outperforms methods that rely only on the historical average of the data to perform a prediction, especially during days with unusual traffic flow demands. Second, two freeway control strategies: coordinated ramp metering (CRM) and variable speed advisory (VSA) are investigated and implemented in field tests. In the control of CRM study, the freeway is modeled by the cell transmission model (CTM) and the control problem is solved by the model predictive control (MPC) scheme. The proposed CRM is deployed in a segment of California State Route 99 Northbound (SR-99N) for a five-week field test. The test results shows that the freeway efficiency can be improved by 7.25% for morning peak hours. In the VSA control study, an advisory speed limit control is designed by using traffic flow stabilization of the Lighthill-Whitham-Richards (LWR) model. The proposed VSA is deployed in a segment of California State Route 78 Eastbound (SR-78E) for a five-week field test. The test results shows that the freeway efficiency can be improved by 8.71% for morning peak hours. Both control strategies indicate freeway efficiency improvement in congested traffic. Third, the large-scale signalized intersection traffic network control by offset optimization is also studied. The traffic network is described by a directed graph and the traffic dynamic is represented by continuous-time fluid queue model with sinusoidal arrival and departure rate assumption. The original non-convex offset optimization problem can be relaxed into a semidefinite program (SDP). The Burer-Monteiro (BM) method is used for solving the large SDP to avoid conic constraints. Two real-world traffic simulation networks, respectively in Manhattan, NY and in Pasadena, CA are constructed for demonstrating the BM method. Numerical simulation results indicate that BM method has good scalability and it can efficiently recover optimal solutions of the SDP

Energy efficient adaptive cruise control: Towards benchmarking energy efficiency in the control of partially and fully automated vehicles

Fully automated vehicles are expected to have a significant share of the road network traffic in the near future. Several commercial vehicles with full-range adaptive cruise control systems or semi-autonomous functionalities are already available in the market. This provides a unique opportunity to improve the acceleration behaviour of vehicles, and thereby, improve network’s efficiency in terms of important performance indicators such as fuel consumption and traffic throughput. However, automated driving systems usually adopt a highly conservative driving strategy to ensure safety and fuel efficiency for individual vehicles. The collective impacts of such strategies on the network level can lead to the deterioration of traffic flow and to an increase in fuel/energy consumption. Much of the existing research in this area either target driving conditions where there are no additional complexities caused by interaction between vehicles, or make simplistic assumptions about the dynamics of driving behaviour and its relationship with fuel consumption in order to formulate a feasibly solvable optimisation problem. The reduction of the question of fuel efficiency to optimisation scenarios where only a pair of vehicles are considered and little attention is paid to the surrounding traffic, leads to a user-optimal driving strategy at best, however addressing environmental concerns and a more efficient use of fossil fuels in road transport networks necessitates a system-optimal approach. A system-optimal approach means the scope of the problem must be broadened so that a) the complex relationship between individual driving styles and the dynamical features of traffic flow are incorporated within the optimisation framework and b) the long-term impacts of driving strategies on network’s performance are modelled within optimisation scenarios. The challenge here is to model driving behaviour and traffic flow with sufficient accuracy and devise an optimisation framework that is computationally efficient enough to cope with the complexity of the problem. In this study, the use of car-following models and limiting the search space for optimal strategies to the parameter space of car-following models is proposed. This framework enables performing much more comprehensive optimisations and conducting more extensive tests on the collective impacts of fuel-economy driving strategies. The results obtained in this study show that formulating the optimisation in a short-sighted way where merely individual vehicles are considered and no attention is paid to the collective impacts of a fuel-economy driving strategy, can lead to significant increase in fuel consumption for the whole network while delivering marginal benefits for the target vehicle. This study establishes a complex relationship between traffic flow and fuel consumption on the link level, where the former cannot be achieved without addressing the latter correctly in the optimisation process. In addition to the main research question discussed above, the present thesis proposes a new method for the analysis of car-following models. The conventional method in the analysis of car-following models relies on the cumulative error between real data and modelled data in order to benchmark car-following models. Although the cumulative error is indeed an informative measure of performance, it leaves many questions regarding the capacity of models for replicating driving behaviour unanswered. Here the use of dynamic system identification is investigated as a way to provide a more in-depth analysis of the strengths and weaknesses of car-following models in reproducing realistic driving behaviour. Subsequently, the proposed method is applied to compare a number of car-following models. Although the application of this method for the comparison of car-following models presents some challenges that require further research, the results are very promising