Estimating adaptive cruise control model parameters from on-board radar units

Two new methods are presented for estimating car-following model parameters using data collected from the Adaptive Cruise Control (ACC) enabled vehicles. The vehicle is assumed to follow a constant time headway relative velocity model in which the parameters are unknown and to be determined. The first technique is a batch method that uses a least-squares approach to estimate the parameters from time series data of the vehicle speed, space gap, and relative velocity of a lead vehicle. The second method is an online approach that uses a particle filter to simultaneously estimate both the state of the system and the model parameters. Numerical experiments demonstrate the accuracy and computational performance of the methods relative to a commonly used simulation-based optimization approach. The methods are also assessed on empirical data collected from a 2019 model year ACC vehicle driven in a highway environment. Speed, space gap, and relative velocity data are recorded directly from the factory-installed radar unit via the vehicle's CAN bus. All three methods return similar mean absolute error values in speed and spacing compared to the recorded data. The least-squares method has the fastest run-time performance, and is up to 3 orders of magnitude faster than other methods. The particle filter is faster than real-time, and therefore is suitable in streaming applications in which the datasets can grow arbitrarily large.Comment: Accepted for poster presentation at the Transportation Research Board 2020 Annual Meeting, Washington D.

Aeronautical Engineering: A continuing bibliography, supplement 120

This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Aeronautical engineering: A continuing bibliography with indexes, supplement 100

This bibliography lists 295 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in August 1978

Aeronautical engineering: A continuing bibliography, supplement 122

This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980

Aeronautical Engineering: A special bibliography with indexes, supplement 55

This bibliography lists 260 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1975

Exploring Smart Infrastructure Concepts to Improve the Reliability and Functionality of Safety Oriented Connected Vehicle Applications

Cooperative adaptive cruise control (CACC), a form of vehicle platooning, is a well known connected vehicle application. It extends adaptive cruise control (ACC) by incorporating vehicle-to-vehicle communications. A vehicle periodically broadcasts a small message that includes in the least a unique vehicle identifier, its current geo-location, speed, and acceleration. A vehicle might pay attention to the message stream of only the car ahead. While CACC is under intense study by the academic community, the vast majority of the relevant published literature has been limited to theoretical studies that make many simplifying assumptions. The research presented in this dissertation has been motivated by our observation that there is limited understanding of how platoons actually work under a range of realistic operating conditions. Our research includes a performance study of V2V communications based on actual V2V radios supplemented by simulation. These results are in turn applied to the analysis of CACC. In order to understand a platoon at scale, we resort to simulations and analysis using the ns3 simulator. Assessment criteria includes network reliability measures as well as application oriented measures. Network assessment involves latency and first and second order loss dynamics. CACC performance is based on stability, frequency of crashes, and the rate of traffic flow. The primary goal of CACC is to maximize traffic flow subject to a maximum allowed speed. This requires maintaining smaller inter-vehicle distances which can be problematic as a platoon can become unstable as the target headway between cars is reduced. The main contribution of this dissertation is the development and evaluation of two heuristic approaches for dynamically adapting headway both of which attempt to minimize the headway while ensure stability. We present the design and analysis of a centralized and a distributed implementation of the algorithm. Our results suggest that dynamically adapting the headway time can improve the overall platoon traffic flow without the platoon becoming unstable

Statistical prediction of aircraft trajectory : regression methods vs point-mass model

International audienceGround-based aircraft trajectory prediction is a critical issue for air traffic management. A safe and efficient prediction is a prerequisite for the implementation of automated tools that detect and solve conflicts between trajectories. Moreover, regarding the safety constraints, it could be more reasonable to predict intervals rather than precise aircraft positions . In this paper, a standard point-mass model and statistical regression method is used to predict the altitude of climbing aircraft. In addition to the standard linear regression model, two common non-linear regression methods, neural networks and Loess are used. A dataset is extracted from two months of radar and meteorological recordings, and several potential explanatory variables are computed for every sampled climb segment. A Principal Component Analysis allows us to reduce the dimensionality of the problems, using only a subset of principal components as input to the regression methods. The prediction models are scored by performing a 10-fold cross-validation. Statistical regression results method appears promising. The experiment part shows that the proposed regression models are much more efficient than the standard point-mass model. The prediction intervals obtained by our methods have the advantage of being more reliable and narrower than those found by point-mass model

The Design of an Uninhabited Air Vehicle for Remote Sensing in the Cryosphere

This document summarizes the results of the preliminary design of an Uninhabited Air Vehicle (UAV) for use in Cryospheric research. This includes the development of a mission specification with all related performance requirements. In general, the design mission of this aircraft, named the Meridian, is to takeoff from a remote base camp in either Antarctica or Greenland, fly to some area of interest, acquire data such as ice thickness and surface elevation with ground penetrating radar, then return to base. These types of missions, which to date have been flown with inhabited aircraft, can be described as both dull and dangerous. These are characteristics that support the use of a UAV for this mission. The design of the Meridian is performed in parallel to the development of the primary payload: a ground penetrating Synthetic Aperture Radar (SAR). This concurrent system development warranted a certain amount of flexibility in the aircraft design. This led to the development of three candidate configurations, from which the primary configuration was selected and carried through to the more detailed design phases. This process, commonly referred to as Class I and Class II design phases, was used to develop three Class I conceptual configurations, named the Red, White, and Blue designs. The three designs represent three methods of integrating the radar antennas into the aircraft structure. The Red design utilizes a structurally synergistic approach where the antennas are integrated directly into the wing structure. The White design is a more flexible approach in that the antennas are simply mounted on pylons hung below the wing. The Blue design is a hybrid of the other configurations in that it integrates the antennas into a dielectric lower wing of a biplane configuration. Weight is one of the most common performance metrics associated with the merit of a preliminary aircraft design. This is due to the fact that the acquisition and operational cost of an aircraft are directly related to the vehicle weight. In these terms, the Red concept proved to be the most weight efficient with a takeoff weight of 760 lbs, while the White was the least efficient with a takeoff weight of 1,270 lbs. However, the purpose of this design is to choose the best design with respect to the whole system. The White concept was selected as the primary configuration as it represents the most flexible in terms of antenna integration. This is vital to the risk mitigation of this aircraft development. The White design was refined in the Class II design process resulting in the Meridian UAV. The Meridian has a takeoff weight of 1,080 lbs, an empty weight of 615 lbs, and a range of 950 nm (with reserves for an additional 160 nm). The Meridian is a turboprop powered aircraft with a design cruise speed of 120 kts and a takeoff and landing distance of 1,500 ft. The aircraft has ten hardpoints along the wing for antenna mounting, and is specifically designed for cold weather operations to include anti-icing provisions, system heating and cooling, and the ability to operate from snow/ice runways. The Meridian represents the application of conventional aircraft design methodologies to a UAV. This document discusses the viability of using these methods, which are typically used on inhabited aircraft, to design an uninhabited vehicle

Enhancing service quality and reliability in intelligent traffic system

Intelligent Traffic Systems (ITS) can manage on-road traffic efficiently based on real-time traffic conditions, reduce delay at the intersections, and maintain the safety of the road users. However, emergency vehicles still struggle to meet their targeted response time, and an ITS is vulnerable to various types of attacks, including cyberattacks. To address these issues, in this dissertation, we introduce three techniques that enhance the service quality and reliability of an ITS. First, an innovative Emergency Vehicle Priority System (EVPS) is presented to assist an Emergency Vehicle (EV) in attending the incident place faster. Our proposed EVPS determines the proper priority codes of EV based on the type of incidents. After priority code generation, EVPS selects the number of traffic signals needed to be turned green considering the impact on other vehicles gathered in the relevant adjacent cells. Second, for improving reliability, an Intrusion Detection System for traffic signals is proposed for the first time, which leverages traffic and signal characteristics such as the flow rate, vehicle speed, and signal phase time. Shannon’s entropy is used to calculate the uncertainty associated with the likelihood of particular evidence and Dempster-Shafer (DS) decision theory is used to fuse the evidential information. Finally, to improve the reliability of a future ITS, we introduce a model that assesses the trust level of four major On-Board Units (OBU) of a self-driving car along with Global Positioning System (GPS) data and safety messages. Both subjective logic (DS theory) and CertainLogic are used to develop the theoretical underpinning for estimating the trust value of a self-driving car by fusing the trust value of four OBU components, GPS data and safety messages. For evaluation and validation purposes, a popular and widely used traffic simulation package, namely Simulation of Urban Mobility (SUMO), is used to develop the simulation platform using a real map of Melbourne CBD. The relevant historical real data taken from the VicRoads website were used to inject the traffic flow and density in the simulation model. We evaluated the performance of our proposed techniques considering different traffic and signal characteristics such as occupancy rate, flow rate, phase time, and vehicle speed under many realistic scenarios. The simulation result shows the potential efficacy of our proposed techniques for all selected scenarios.Doctor of Philosoph