Private versus Shared, Automated Electric Vehicles for U.S. Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration, and Cost Impacts
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Scientific objectives of the MARV mission are outlined and specific science systems requirements and experimental payloads defined. All aspects of the Martian surface relative to biotic and geologic elements and those relating to geophysical and geochemical properties are explored.
Recent advances in eye-safe, short wavelength solid-state lasers offer real potential for the development of compact, reliable, light-weight, efficient coherent lidar. Laser diode pumping of these devices has been demonstrated, thereby eliminating the need for flash lamp pumping, which has been a major drawback to the use of these lasers in space based applications. Also these lasers now have the frequency stability required to make them useful in coherent lidar, which offers all of the advantages of non-coherent lidar, but with the additional advantage that direct determination of target velocity is possible by measurement of the Doppler shift. By combining the Doppler velocity measurement capability with the inherent high angular resolution and range accuracy of lidar it is possible to construct Doppler images of targets for target motion assessment. A coherent lidar based on a Tm,Ho:YAG 2-micrometer wavelength laser was constructed and successfully field tested on atmospheric targets in 1990. This lidar incorporated an all solid state (laser diode pumped) master oscillator, in conjunction with a flash lamp pumped slave oscillator. Solid-state laser technology is rapidly advancing, and with the advent of high efficiency, high power, semiconductor laser diodes as pump sources, all-solid-state, coherent lidars are a real possibility in the near future. MSFC currently has a feasibility demonstration effort under way which will involve component testing, and preliminary design of an all-solid-state, coherent lidar for automatic rendezvous, and capture. This two year effort, funded by the Director's Discretionary Fund is due for completion in 1992.
Final Paper and not the abstract is attached. see attached
Our current air transportation system has underserved markets, including local, regional, intraregional, and urban transportation of both people and cargo. Recent advances in aviation technology such as small highly-automated vehicles, electric aircraft, and automated air traffic are enabling business opportunities in these markets. Advanced Air Mobility, or AAM, refers to a community effort to overcome the gaps in operational rules, safety analysis, and overall acceptance, so that these new operations will be possible. For full details, please see: https://www.nasa.gov/aam As we think about how we can safely introduce these new operations, fundamental questions about how the structure of Safety Management Systems can be applied to AAM, and how that structure can be used to help us overcome the necessary technical and societal obstacles arise. Two of these questions are: • How do we tailor the requirements and desired level of monitoring and assessment to achieve safety given the breadth of possible operations and the associated risk of those operations? • How do we aid innovation by rapidly evaluating the safety of novel operations without losing associated rigor? We assume that a Safety Management System that enables these future systems will incorporate knowledge of the acceptable level of risk, and will utilize data science to automate the core monitor, assess and mitigate functions that will allow us to respond to risks and hazards in time to prevent safety incidents. In 2018, the National Academies proposed an In-Time Aviation Safety Management System (IASMS) that would advance these goals. (https://www.nap.edu/catalog/24962/in-time-aviation-safety-management-challenges-and-research-for-an) The National Academies made a clear distinction between in-time systems, in which hazards could be identified and risks mitigated in time to prevent incidents, and real-time systems, since many hazards and risks do not need real-time data and analysis to detect and mitigate.
Advances in automated vehicle (AV) technology and expanded operations are rapidly emerging with Automated Mobility District (AMD) deployments in global cities. NLR's AMD research addresses critical elements of human supervision of AV fleet operations and associated passenger communications for vehicles in which no driver or safety attendant is present. Although sufficiently advanced AVs no longer have direct oversight by a driver, fleet management remains staffed with operations personnel at the operations command and control (OCC) facility. This paper examines the functionality of the OCC, drawing comparisons of how automated train control and automated people mover OCCs operate. Within an AMD, the OCC manages various vehicle types, sizes, and operational modes, including on-demand and fixed route service, to facilitate a 'network of networks' for transport within a metropolitan area. The OCC serves as oversight for multiple AV fleets assisting AVs via remote operation of vehicles, communication, and dispatching personnel to resolve problems. The OCC also coordinates system operation, geographically staging vehicles, and managing weather, police, and emergency events. Informed by traffic management center (TMC) strategies using highly integrated software and communications, OCCs facilitate seamless information flows. OCC personnel remotely assist passengers and oversee multi-party operation to ensure safety and security. Although social norms mitigate large-capacity unattended vehicle operations, social interaction in multi-party automated small vehicles has little precedent. This poses a new frontier for society and requires research to effectively understand and manage. Future research will monitor OCC implementations, passenger interfaces, and deployment scaling of initial AMD systems.
Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.
Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter-vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop-and-go driving patterns. Here, by applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real-time on-vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline.
Automated techniques for providing reliability assurance in launch operations of Saturn IB SPACE vehicle system
This article focuses on the speed planning problem for connected and automated vehicles (CAVs) communicating to traffic lights. The uncertainty of traffic signal timing for signalized intersections on the road is considered. The eco-driving problem is formulated as a data-driven chance-constrained robust optimization problem. Effective red-light duration (ERD) is defined as a random variable, and describes the feasible passing time through the signalized intersections. Usually, the true probability distribution for ERD is unknown. Consequently, a data-driven approach is adopted to formulate chance constraints based on empirical sample data. This incorporates robustness into the eco-driving control problem with respect to uncertain signal timing. Dynamic programming (DP) is employed to solve the optimization problem. The simulation results demonstrate that the proposed method can generate optimal speed reference trajectories with 40% less vehicle fuel consumption, while maintaining the arrival time at a similar level compared to a modified intelligent driver model (IDM). The proposed control approach significantly improves the controller's robustness in the face of uncertain signal timing, without requiring to know the distribution of the random variable a priori.
Eco-Driving with connected and automated vehicles has shown potential to reduce energy consumption of an individual (i.e., ego) vehicle by up to 15%. In a project funded by ARPA-E, a team led by Southwest Research Institute demonstrated an 8-12% reduction in energy consumption on a 2017 Prius Prime. This was demonstrated in simulation as well as chassis dynamometer testing. The authors presented a simulation study that demonstrated corridor-level energy consumption improvements by about 15%. This study was performed by modeling a six-kilometer-long urban corridor in Columbus, Ohio for traffic simulations. Five powertrain models consisting of two battery electric vehicles (BEVs), a hybrid electric vehicle (HEV), and two internal combustion engine (ICE) powered vehicles were developed. The design of experiment consisted of sweeps for various levels of traffic, penetration of smart vehicles, penetration of technology, and powertrain electrification. The large-scale simulation study consisted of doing approximately 96,000 powertrain simulations. A sophisticated clustering scheme was built and utilized to down select representative traces for each scenario from the simulation study for vehicle testing on a chassis dynamometer. Furthermore, this paper provides a summary of individual ego vehicle testing as well as a comprehensive overview of the method utilized for down selecting representative traces from large scale simulation studies that can be used to quantify corridor level benefits. Vehicle test results along with corresponding analyses are presented.
Previous efforts to explore the implications of partial market penetration of connected and automated vehicles (CAVs) show a consensus on the benefits of higher market penetration rates (MPR) of vehicles enabled with connectivity and/or automation. There is, however, a level of uncertainty regarding the effects of lower market penetration rates and the consideration of heterogeneous vehicle fleets. Using VISSIM to perform microscopic traffic simulation and, vehicle simulation models, we assess the impacts of different CAVs market penetration rates on fuel consumption considering a heterogeneous traffic environment. The results show that the fuel efficiency benefits of optimal coordination control are maximized in moderate congested scenarios when the CAVs MPR exceeds 40%.
Connected and automated vehicles (CAVs), particularly those with a hybrid electric powertrain, have the potential to significantly improve vehicle energy savings in real-world driving conditions. In particular, the ecodriving problem seeks to design optimal speed and power usage profiles based on available information from connectivity and advanced mapping features to minimize the fuel consumption over an itinerary. This paper presents a hierarchical multilayer model predictive control (MPC) approach for improving the fuel economy of a 48 V mild-hybrid powertrain in a connected vehicle environment. Approximate dynamic programing (DP) is used to solve the receding horizon optimal control problem, whose terminal cost is approximated with the base policy obtained from the long-term optimization. The controller was tested virtually (with deterministic and Monte Carlo simulation) across multiple real-world routes, demonstrating energy savings of more than 20%. The controller was then deployed on a test vehicle equipped with a rapid prototyping embedded controller. In-vehicle testing confirm the energy savings obtained in simulation and demonstrate the real-time ability of the controller.
The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.
Future launch vehicle programs for automated space missions, discussing higher launch velocities and economics
The paper describes an automated mixed traffic vehicle (AMTV), a driverless low-speed tram designed to operate in mixed pedestrian and vehicular traffic. The vehicle is a six-passenger electric tram equipped with sensing and control which permit it to function on existing streets in an automatic mode. The design includes established wire-following techniques for steering and near-IR headway sensors. A 7-mph cruise speed is reduced to 2 mph or a complete stop in response to sensor (or passenger) inputs. The AMTV performance is evaluated by operation on a loop route and by simulation. Some necessary improvements involving sensors, sensor pattern, use of an audible signal, and control lag are discussed. It is suggested that appropriate modifications will eliminate collision incidents.
Adaptive cruise control (ACC) vehicles are the first step toward comprehensive vehicle automation. However, the impacts of such vehicles on the underlying traffic flow are not yet clear. Therefore, it is of interest to accurately model vehicle-level dynamics of commercially available ACC vehicles so that they may be used in further modeling efforts to quantify the impact of commercially available ACC vehicles on traffic flow. Importantly, not only model selection but also the calibration approach and error metric used for calibration are critical to accurately model ACC vehicle behavior. In this work, we explore the question of how to calibrate car following models to describe ACC vehicle dynamics. Specifically, we apply a multi-objective calibration approach to understand the tradeoff between calibrating model parameters to minimize speed error vs. spacing error. Three different car-following models are calibrated for data from six vehicles. The results are in line with recent literature and verify that targeting a low spacing error does not compromise the speed accuracy whether the opposite is not true for modeling ACC vehicle dynamics.