Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities

Traffic smoothing using explicit local controllers

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment.Comment: 21 pages, 1 Table , 9 figure

A Sigmoid-based car-following model to improve acceleration stability in traffic oscillation and following failure in free flow

This paper proposes an improved Intelligent driving model (Sigmoid-IDM) to address the problems of excessive acceleration in traffic oscillation and following failure in free flow. The Sigmoid-IDM uses a Sigmoid function to enhance the start-following characteristics, improve the output strategy of the spacing term, and stabilize the steady-state velocity in free flow. Moreover, the model asymmetry is improved by means of introducing cautious following distance, driving caution factor, and segmentation function. The anti-interference ability of the Sigmoid-IDM is demonstrated by local stability and string stability analyses.Comment: 15 pages, 51 figures

String Stability of a Vehicular Platoon with the use of Macroscopic Information

We investigate the possibility to use macroscopic information to improve control performance of a vehicular platoon composed of autonomous vehicles. A general mesoscopic traffic modeling is described, and a closed loop String Stability analysis is performed using Input-to-State Stability (ISS) results. Examples of mesoscopic control laws are provided and shown to ensure String Stability properties. Simulations are implementedin order to validate the control laws and to show the efficacy of the proposed approach.Comment: arXiv admin note: substantial text overlap with arXiv:2003.1252

Development and verification of cooperative adaptive cruise control via LTE-V

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIn this paper, we present a testbed platform for realizing cooperative adaptive cruise control (CACC) enabled by LTE-V (LTE-vehicle). The platform is developed on a platoon of vehicles, each of which is equipped with a suite of on-board sensing and computing devices for environment perception and automated vehicle control, as well as an LTE-V transceiver for high-performance vehicle-to-vehicle (V2V) communication. The hardware architecture and software architecture, especially the perception and control methods, of the platform are described. Field experiments in different road conditions are conducted to verify the feasibility of our platform. The results also show the potential of V2V communications via LTE-V in terms of improving the sensing capability of individual vehicle’s on-board sensors.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesShanghai Yangfan ProgramEuropean Union Horizon 202

Visual Counting of Traffic Flow from a Car via Vehicle Detection and Motion Analysis

Visual traffic counting so far has been carried out by static cameras at streets or aerial pictures from sky. This work initiates a new approach to count traffic flow by using populated vehicle driving recorders. Mainly vehicles are counted by a camera moves along a route on opposite lane. Vehicle detection is first implemented in video frames by using deep learning YOLO3, and then vehicle trajectories are counted in the spatial-temporal space called motion profile. Motion continuity, direction, and detection missing are considered to avoid multiple counting of oncoming vehicles. This method has been tested on naturalistic driving videos lasting for hours. The counted vehicle numbers can be interpolated as a flow of opposite lanes from a patrol vehicle for traffic control. The mobile counting of traffic is more flexible than the traffic monitoring by cameras at street corners

Control Problems for Conservation Laws with Traffic Applications

Conservation and balance laws on networks have been the subject of much research interest given their wide range of applications to real-world processes, particularly traffic flow. This open access monograph is the first to investigate different types of control problems for conservation laws that arise in the modeling of vehicular traffic. Four types of control problems are discussed - boundary, decentralized, distributed, and Lagrangian control - corresponding to, respectively, entrance points and tolls, traffic signals at junctions, variable speed limits, and the use of autonomy and communication. Because conservation laws are strictly connected to Hamilton-Jacobi equations, control of the latter is also considered. An appendix reviewing the general theory of initial-boundary value problems for balance laws is included, as well as an appendix illustrating the main concepts in the theory of conservation laws on networks

Impacts of vehicle automation on traffic flow stability

This dissertation is motivated by the possibility of a small number of autonomous vehicles (AVs) or partially autonomous vehicles that may soon be present on our roadways. This automation may take the form of fully autonomous vehicles without human intervention (Society of Automotive Engineers, SAE Level 5) or, as is already the case in many modern vehicles, may take the form of driver assist features such as adaptive cruise control (ACC), or other SAE Level 1 features. Regardless of the extent of automation, changing the vehicle dynamics of a small number of vehicles in the bulk traffic flow may have substantial implications on the underlying traffic flow and may influence the development of emergent phenomena such as phantom traffic jams, or traffic stability. This dissertation has four main contributions: (i) experimental evidence to validate that human driving behavior alone is sufficient for the development of phantom jams, (ii) theoretical work as well as experimental work to demonstrate that current commercially-available ACC systems may be string unstable under certain circumstances, (iii) theoretical and experimental results that demonstrate the ability of autonomous vehicles to stabilize traffic flow and prevent phantom jams from arising even at low autonomous vehicle penetration rates (∼5%), and (iv) experimental evidence for the emissions impacts of phantom traffic jams, and the potential for AVs to substantially reduce these emissions