Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time
TL;DR: An optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle to reduce idle time at stop lights and fuel consumption.
Abstract: This brief proposes the use of upcoming traffic signal information within the vehicle's adaptive cruise control system to reduce idle time at stop lights and fuel consumption. To achieve this goal an optimization-based control algorithm is formulated that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle. The control objectives are: timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed. Three example simulation case studies are presented to demonstrate the potential impact on fuel economy, emission levels, and trip time.
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TL;DR: The review shows that first-order impacts on road capacity, fuel efficiency, emissions, and accidents risk are expected to be beneficial and the balance between the short-term benefits and long-term impacts of vehicle automation remains an open question.
607 citations
Cites background from "Predictive Cruise Control: Utilizin..."
...Moreover, Ala, Yang, and Rakha (2016), Kamalanathsharma and Rakha (2016) and Asadi and Vahidi (2011) reported fuel savings up to 19%,...
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...Moreover, Ala, Yang, and Rakha (2016), Kamalanathsharma and Rakha (2016) and Asadi and Vahidi (2011) reported fuel savings up to 19%, 30%, and 47%, respectively, for their cooperative adaptive cruise controller that uses vehicle-to-infrastructure (traffic signal in this case) communication to…...
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TL;DR: This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy using a model predictive control method.
Abstract: Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system measures the relevant information of the current road and traffic, predicts the future states of the preceding vehicle, and computes the optimal vehicle control input using model predictive control (MPC). A typical control objective is chosen to maximize fuel economy by regulating a safe head-distance or cruising at the optimal velocity under bounded driving torque condition. The proposed vehicle control system is evaluated in urban traffic containing thousands of diverse vehicles using the microscopic traffic simulator AIMSUN. Simulation results show that the vehicles controlled by the proposed MPC method significantly improve their fuel economy.
389 citations
Cites background from "Predictive Cruise Control: Utilizin..."
...Utilizing the signal transition timing of the upcoming intersection, fuel consumption of a vehicle can also be reduced by avoiding aggressive braking [19], [20]....
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TL;DR: In this paper, the authors introduce a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency.
363 citations
TL;DR: 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.
Abstract: 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.
297 citations
TL;DR: In this article, the authors present a survey of early-stage traffic control technologies and discuss potential benefits that will be gained by using vehicle-to-vehicle (V2V) communications.
Abstract: During the last 60 years, incessant efforts have been made to improve the efficiency of traffic control systems to meet ever-increasing traffic demands. Some recent works attempt to enhance traffic efficiency via vehicle-to-vehicle communications. In this paper, we aim to give a survey of some research frontiers in this trend, identifying early-stage key technologies and discussing potential benefits that will be gained. Our survey focuses on the control side and aims to highlight that the design philosophy for traffic control systems is undergoing a transition from feedback character to feedforward character. Moreover, we discuss some contrasting preferences in the design of traffic control systems and their relations to vehicular communications. The first pair of contrasting preferences are model-based predictive control versus simulation-based predictive control. The second pair are global planning-based control versus local self-organization-based control. The third pair are control using rich information that may be highly redundant versus control using concise information that is necessary. Both the potentials and drawbacks of these control strategies are explained. We hope these comparisons can shed some interesting light on future traffic control studies.
278 citations
References
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Book•
01 Dec 2001
TL;DR: A standard formulation of Predictive Control is presented, with examples of step response and transfer function formulations, and a case study of robust predictive control in the context of MATLAB.
Abstract: 1. Introduction to Predictive Control. 2. A Standard Formulation of Predictive Control. 3. Solving Predictive Control Problems. 4. Step Response and Transfer Function Formulations. 5. Tuning. 6. Stability. 7. Robust Predictive Control. 8. Perspectives. 9. Case Studies. 10. The Model Predictive Control Toolbox. References Appendices A. Some Commercial MPC Products B. MATLAB Program basicmpc C. The MPC Toolbox D. Solutions to Problems
5,468 citations
"Predictive Cruise Control: Utilizin..." refers methods in this paper
...According to the standard MPC design, only the first entry of the control sequence , is applied to the vehicle; the optimization horizon is moved one step forward, the model and constraints are updated if necessary, and the optimization process is repeated to obtain the next optimal control sequence [21]–[23]....
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Book•
01 Feb 1992TL;DR: In this article, the authors attempt to find a middle ground by balancing engineering principles and equations of use to every automotive engineer with practical explanations of the mechanics involved, so that those without a formal engineering degree can still comprehend and use most of the principles discussed.
Abstract: This book attempts to find a middle ground by balancing engineering principles and equations of use to every automotive engineer with practical explanations of the mechanics involved, so that those without a formal engineering degree can still comprehend and use most of the principles discussed. Either as an introductory text or a practical professional overview, this book is an ideal reference.
3,166 citations
"Predictive Cruise Control: Utilizin..." refers background in this paper
...For example assume the speed limits are [5,20] m/s and the distance to the first traffic light is 1000 m....
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...braking on dry asphalt, the coefficient of braking is chosen to be [20]....
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...For the th vehicle with mass , the longitudinal dynamics is [20]...
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01 Jan 1992
1,596 citations
TL;DR: This article suggests an alternative mechanism for coordinating the movement of autonomous vehicles through intersections and demonstrates in simulation that this new mechanism has the potential to significantly outperform current intersection control technology--traffic lights and stop signs.
Abstract: Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging.
We believe current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. In this article, we suggest an alternative mechanism for coordinating the movement of autonomous vehicles through intersections. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol, which we also present. We demonstrate in simulation that our new mechanism has the potential to significantly outperform current intersection control technology--traffic lights and stop signs. Because our mechanism can emulate a traffic light or stop sign, it subsumes the most popular current methods of intersection control. This article also presents two extensions to the mechanism. The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles. The second gives priority to emergency vehicles without significant cost to civilian vehicles. The mechanism, including both extensions, is implemented and tested in simulation, and we present experimental results that strongly attest to the efficacy of this approach.
1,163 citations
"Predictive Cruise Control: Utilizin..." refers background in this paper
...[12], [13] have proposed replacing traffic lights and stop signs by intelligent lights: via a two way communication protocol, the autonomous vehicles call the intersection ahead to reserve a time-space slot to pass; which among other things can help improve the fuel economy....
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