Towards secure & robust PNT for automated systems

This dissertation makes four contributions in support of secure and robust position, navigation, and timing (PNT) for automated systems. The first two relate to PNT security while the latter two address robust positioning for automated ground vehicles. The first contribution is a fundamental theory for provably-secure clock synchronization between two agents in a distributed automated system. All one-way synchronization protocols, such as those based on the Global Positioning System (GPS) and other Global Navigation Satellite Systems (GNSS), are shown to be vulnerable to man-in-the-middle delay attacks. This contribution is the first to identify the necessary and sufficient conditions for provably secure clock synchronization. The second contribution, also related to PNT security, is a three-year study of the world-wide GPS interference landscape based on data from a dual-frequency GNSS receiver operating continuously on the International Space Station (ISS). This work is the first publicly-reported space-based survey of GNSS interference, and unveils previously-unreported GNSS interference activity. The third contribution is a novel ground vehicle positioning technique that is robust to GNSS signal blockage, poor lighting conditions, and adverse weather events such as heavy rain and dense fog. The technique relies on sensors that are commonly available on automated vehicles and are insensitive to lighting and inclement weather: automotive radar, low-cost inertial measurement units (IMUs), and GNSS. Remarkably, it is shown that, given a prior radar map, the proposed technique operating on data from off-the-shelf all-weather automotive sensors can maintain sub-50-cm horizontal position accuracy during 60 min of GNSS-denied driving in downtown Austin, TX. This dissertation’s final contribution is an analysis and demonstration of the feasibility of crowd-sourced digital mapping for automated vehicles. Localization techniques, such as the one described in the previous contribution, rely on such digital maps for accuracy and robustness. A key enabler for large-scale up-to-date maps is enlisting the help of the very consumer vehicles that need the map to build and update it. A method for fusing multi-session vision data into a unified digital map is developed. The asymptotic limit of such a map’s globally-referenced position accuracy is explored for the case in which the mapping agents rely on low-cost GNSS receivers performing standard code-phase-based navigation. Experimental validation along a semi-urban route shows that low-cost consumer vehicles incrementally tighten the accuracy of the jointly-optimized digital map over time enough to support sub-lane-level positioning in a global frame of reference.Electrical and Computer Engineerin

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

3D LiDAR Point Cloud Processing Algorithms

In the race for autonomous vehicles and advanced driver assistance systems (ADAS), the automotive industry has energetically pursued research in the area of sensor suites to achieve such technological feats. Commonly used autonomous and ADAS sensor suites include multiples of cameras, radio detection and ranging (RADAR), light detection and ranging (LiDAR), and ultrasonic sensors. Great interest has been generated in the use of LiDAR sensors and the value added in an automotive application. LiDAR sensors can be used to detect and track vehicles, pedestrians, cyclists, and surrounding objects. A LiDAR sensor operates by emitting light amplification by stimulated emission of radiation (LASER) beams and receiving the reflected LASER beam to acquire relevant distance information. LiDAR reflections are organized in a three-dimensional environment known as a point cloud. A major challenge in modern autonomous automotive research is to be able to process the dimensional environmental data in real time. The LiDAR sensor used in this research is the Velodyne HDL 32E, which provides nearly 700,000 data points per second. The large amount of data produced by a LiDAR sensor must be processed in a highly efficient way to be effective. This thesis provides an algorithm to process the LiDAR data from the sensors user datagram protocol (UDP) packet to output geometric shapes that can be further analyzed in a sensor suite or utilized for Bayesian tracking of objects. The algorithm can be divided into three stages: Stage One - UDP packet extraction; Stage Two - data clustering; and Stage Three - shape extraction. Stage One organizes the LiDAR data from a negative to a positive vertical angle during packet extraction so that subsequent steps can fully exploit the programming efficiencies. Stage Two utilizes an adaptive breakpoint detector (ABD) for clustering objects based on a Euclidean distance threshold in the point cloud. Stage Three classifies each cluster into a shape that is either a point, line, L-shape, or a polygon using principal component analysis and shape fitting algorithms that have been modified to take advantage of the pre-organized data from Stage One. The proposed algorithm was written in the C language and the runtime was tested on a two Windows equipped machines where the algorithm completed the processing, on average, sparing 30% of the time between UDP data packets sent from the HDL32E. In comparison to related research, this algorithm performed over seven hundred and thirty-seven times faster

Predicting Driver Takeover Performance in Conditionally Automated Driving

http://deepblue.lib.umich.edu/bitstream/2027.42/156409/1/AAP_Predicting_takeover_performance.pdfSEL

combined activation of braking and steering for automated driving systems adaptive intervention by injury risk based criteria

Abstract Increase in advanced driver assistance systems (ADAS) performances is a crucial step towards autonomous driving, allowing the design of increasingly reliable automated driving systems (ADS); ADAS devices play a key role in the enhancement of vehicle safety, which primarily results from the ability to avoid possible impacts. Nevertheless, inevitable collision states (ICS) can be triggered by obstacles as buildings and stationary vehicles interposing between the opponent and the working field of ADAS sensors, compromising their functions; therefore, the performance increase of ADAS devices on the market necessarily passes from the optimal handling of an ICS, which is not currently subject to evaluations. The work introduces ADAS intervention criteria which are based on the occupants' injury risk (IR): in a specific road scenario, the ADAS must primarily avoid the collision with maximum margin and minimize IR in the case of an ICS. Specifically, the ADAS must monitor the environment and intervene on braking and steering adapting to the scenario evolution, following an "adaptive" logic. The most critical aspect of the approach lies in reconstructing, for the specific intervention, the eventual impact: while being a time-consuming process, reconstruction of the impact phase is necessary to compute impact-related parameters (e.g., velocity change of the vehicle ∆V) which directly affect IR. To highlight the benefits offered by an adaptive ADAS compared to traditional ADASs, a special testing software has been developed: the best adaptive intervention to be applied at each instant is chosen in real-time through the criteria proposed, retrieving the required information from a pre-calculated database which collects the results of each braking and steering manoeuvre for a large variety of critical scenarios. Analyzing three ICS conditions, it is shown that the adaptive logic, differing from an autonomous emergency braking, aims at creating eccentrical impacts with minimum ∆V: the IR values associated with the ADAS adaptive intervention are consequently an order of magnitude lower than those obtained through traditional ADAS interventions