This paper examines the degree to which connectivity and automation can potentially reduce the overall fuel consumption of on-road vehicles. The paper begins with a simulation study highlighting the tradeoff between: (i) the fuel that a vehicle can save through speed trajectory shaping, versus (ii) the additional inter-vehicle spacing needed for this trajectory shaping to be feasible. This study shows that connectivity and automation are essential, rather than merely useful, for substantial reductions in the fuel consumed by fixed on-road vehicle powertrain/chassis configurations in traffic. Motivated by this insight, we survey the literature on the fuel savings achievable through different connected/automated vehicle technologies. This includes optimal vehicle routing, eco-arrival/departure at intersections, platooning, speed trajectory optimization, predictive driveline disengagement, predictive gear shifting, and predictive powertrain accessory control. This survey shows that the ability to shape vehicle speed trajectories collaboratively plays a dominant role in reducing urban/suburban fuel consumption, while platooning plays a dominant role in influencing the attainable fuel savings on the highway. Moreover, the survey shows that the degree to which connectivity/automation can reduce on-road vehicle fuel consumption, in both urban/suburban and highway settings, depends critically on the integration of powertrain- and chassis-level control.
The topic of this paper is the design of a fully distributed and real-time capable control scheme for the automation of road intersections. State of the art Vehicle-to-Vehicle (V2V) communication technology is adopted. Vehicles distributively negotiate crossing priorities by a Consensus-Based Auction Algorithm (CBAA-M). Then, each agent solves a nonlinear Model Predictive Control (MPC) problem that computes the optimal trajectory avoiding collisions with higher priority vehicles and deciding the crossing order. The scheme is shown to be real-time capable and able to respond to sudden priority …
Nonholonomic models of automobiles are developed by utilizing tools of analytical mechanics, in particular the Appellian approach that allows one to describe the vehicle dynamics with minimum number of time-dependent state variables. The models are categorized based on how they represent the wheel-ground contact, whether they incorporate the longitudinal dynamics, and whether they consider the steering dynamics. It is demonstrated that the developed models can be used to design low-complexity controllers that enable automated vehicles to execute a large variety of maneuvers with high precision…
Coasting has been widely used in the eco-driving guidelines to reduce fuel consumption by profiting from kinetic energy. However, the comprehensive comparison between different coasting strategies and online performance of the eco-coasting strategy using road grade preview are still unclear because of the oversimplification and the integer variable in the optimal control problems. Herein, two different coasting strategies (fuel cut-off and engine start/stop) are proposed to reveal the potential benefit of eco-coasting using the road grade preview. Engine drag torque and energy cost used for en…
Over 30 million tons of deicing salt is applied on U.S. roads annually at a cost of roughly $1.2 billion and with significant negative environmental impact. Therefore, it is desirable to reduce salt use while maintaining winter road safety. Automatic adjustment of application rate in response to road, weather, traffic, and other conditions has the potential to achieve this goal. In the US, salt application rates are typically pre-set manually based on roadway classification, cycle time, desired level of service (LOS), and expected traffic, road, and weather conditions. The operators can tempor…
Several approaches have been presented during the last decades to reduce carbon pollution from transportation. One example is the use of platooning mode. This paper considers data obtained from daily trips to investigate the impact of platooning on travel time, emissions of CO2, CO, NO2 and HC and fuel consumption on a road network in Upper Austria. For this purpose, we studied fuel combustion-based engines relying on the extension of the 3DCoAutoSim simulation platform. The obtained results showed that the platooning mode not only increased driving efficiency but also decreased the total emis…
ACCESS Linnaeus Centre and the Department of Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology（ACCESS林奈斯中心，电气工程学院自动控制系，瑞典皇家理工学院）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Scania CV AB（斯堪尼亚商用车公司）
Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin's Minimum Principle in a feedbac…
This paper investigates the problem of ecological driving (eco-driving) of vehicle platoons. To reduce the probability of the platoon avoiding red lights and increase fuel efficiency, a two-layer control architecture is proposed. The first layer is in charge of optimizing the leader's long-term motion profile using the traffic light and road slope information. The long-term planning model is defined based on the reachability analysis of the platoon to the green light windows. An event-triggered mechanism is proposed to operate the long-term planning model. The second layer is the short-term ad…
In this paper, we introduce an open-source model "MOVESTAR" to calculate the fuel consumption and pollutant emissions of motor vehicles. This model is developed based on U.S. Environmental Protection Agency's (EPA) Motor Vehicle Emission Simulator (MOVES), which provides an accurate estimate of vehicle emissions under a wide range of user-defined conditions. Originally, MOVES requires users to specify many parameters through its software, including vehicle types, time periods, geographical areas, pollutants, vehicle operating characteristics, and road types. In this paper, MOVESTAR is develope…
Towards a Joint Understanding of Remote Operation for Vehicles in Public Road Traffic
Federal Highway and Transport Research Institute (BASt)（联邦公路与交通研究所）；Technical University of Munich (TUM)（慕尼黑工业大学）；Technical University of Darmstadt（达姆施塔特工业大学）；Valeo Schalter und Sensoren（法雷奥开关与传感器）；T-Systems International GmbH, Deutsche Telekom AG（德国电信T-Systems国际有限公司）；German Aerospace Center (DLR)（德国航空航天中心）
Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety. Formation control is a typical multi-vehicle coordination method in the multi-lane scenario. Among the existing formation control methods, the formation switching process is predefined and the collision-free behavior of vehicles has not been considered and explained in detail. This paper proposes a formation control method for multiple Connected and Automated Vehicles (CAVs) on multi-lane roads. Firstly, a bi-level planning framework is proposed to switch the structure of the…
Road information such as road profile and traffic density have been widely used in intelligent vehicle systems to improve road safety, ride comfort, and fuel economy. However, vehicle heterogeneity and parameter uncertainty make it extremely difficult for a single vehicle to accurately and reliably measure such information. In this work, we propose a unified framework for learning-based collaborative estimation to fuse local road estimation from a fleet of connected heterogeneous vehicles. The collaborative estimation scheme exploits the sequential measurements made by multiple vehicles traver…
Speed advisory systems for connected vehicles rely on the estimation of green (or red) light duration at signalized intersections. A particular challenge is to predict the signal phases of semi- and fully-actuated traffic lights. In this paper, we introduce an algorithm that processes traffic measurement data collected from advanced detectors on road links and assigns "PASS"/"WAIT" labels to connected vehicles according to their predicted ability to go through the upcoming signalized intersection within the current phase. Additional computations provide an estimate for the duration of the curr…
In this research, we are going to design a neural nonlinear predictive functional controller (PFC) to achieve a reduced fuel consumption for a chosen autonomous car walks according to a supplied speed trajectory on known roads. We used a fitting neural network as a simple tool for modelling the car's engine and control laws needed to calculate the suitable control commands passed to the brakes and gas pedals' actuators. Independent model method and constraints handling are used to provide controller robustness. We used MATLAB Simulink and IPG CarMaker to design and test our PFC controller. The…
MIT（麻省理工学院）；Laboratory for Information & Decision Systems（信息与决策系统实验室）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Department of Civil and Environmental Engineering（土木与环境工程系）；Institute of Data Systems and Society（数据系统与社会研究所）
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2…
Consecutive traffic signalized intersections can increase vehicle stops, producing vehicle accelerations on arterial roads and potentially increasing vehicle fuel consumption levels. Eco-driving systems are one method to improve vehicle energy efficiency with the help of vehicle connectivity. In this paper, an eco-driving system is developed that computes a fuel-optimized vehicle trajectory while traversing more than one signalized intersection. The system is designed in a modular and scalable fashion allowing it to be implemented in large networks without significantly increasing the computat…
Centre of Excellence for Testing & Research of AVs - NTU (CETRAN)（南洋理工大学自动驾驶车辆测试与研究卓越中心（CETRAN））；Nanyang Technological University（南洋理工大学）；Siemens Mobility Pte. Ltd.（西门子交通私人有限公司）
Vehicles with driving automation are increasingly being developed for deployment across the world. However, the onboard sensing and perception capabilities of such automated or autonomous vehicles (AV) may not be sufficient to ensure safety under all scenarios and contexts. Infrastructure-augmented environment perception using roadside infrastructure sensors can be considered as an effective solution, at least for selected regions of interest such as urban road intersections or curved roads that present occlusions to the AV. However, they incur significant costs for procurement, installation a…
Intelligent connected vehicles equipped with wireless sensors, intelligent control system, and communication devices are expected to commercially launch and emerge on road in short-term. These smart vehicles are able to partially/fully drive themselves; collect data from sensors, make and execute decisions based on that data; communicate with other vehicles, pedestrians, and nodes installed on the road; and provide infotainment and value-added services, such as broadband transmission of ultra-high definition video, files/apps downloading and uploading, online gaming, access to social media, au…
