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Autonomous systems must successfully operate in complex time-varying spatial environments even when dealing with system faults that may occur during a mission. Consequently, evaluating the robustness, or ability to operate correctly under unexpected conditions, of autonomous vehicle control software is an increasingly important issue in software testing. New methods to automatically generate test cases for robustness testing of autonomous vehicle control software in closed-loop simulation are needed. Search-based testing techniques were used to automatically generate test cases, consisting of initial conditions and fault sequences, intended to challenge the control software more than test cases generated using current methods. Two different search-based testing methods, genetic algorithms and surrogate-based optimization, were used to generate test cases for a simulated unmanned aerial vehicle attempting to fly through an entryway. The effectiveness of the search-based methods in generating challenging test cases was compared to both a truth reference (full combinatorial testing) and the method most commonly used today (Monte Carlo testing). The search-based testing techniques demonstrated better performance than Monte Carlo testing for both of the test case generation performance metrics: (1) finding the single most challenging test case and (2) finding the set of fifty test cases with the highest mean degree of challenge.

Leidos has developed an operational test-ready navigation system for Positive Train Location (PTL) demonstrating position accuracies that enable cross-track discrimination using low-cost onboard components and no trackside infrastructure.The system combines information from multiple sensors and a track database (when available) in the Leidos Embedded Data-fusion Geospatial Engine (EDGE) sensor fusion algorithms to create optimal state estimates for position, velocity, and attitude. In Phase I of the PTL development effort, Leidos tested a proof-of-concept system using both simulation and real-world track testing at the Transportation Technology Center (TTC) test track in Pueblo, Colorado. The system demonstrated position errors less than 20 cm in the alongtrack and across-track axes as measured by a fixed-base station Real-Time Kinematic (RTK) GPS reference system. In Phase II, the hardware has been redesigned to support operational railroad installation and testing. The production design has been tested at both the TTC test track as well as on United States Class I railroad operational track territory.

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The Ares Real-Time Environment for Modeling, Integration, and Simulation (ARTEMIS) has been developed for use by the Ares I launch vehicle System Integration Laboratory at the Marshall Space Flight Center. The primary purpose of the Ares System Integration Laboratory is to test the vehicle avionics hardware and software in a hardwarein-the-loop environment to certify that the integrated system is prepared for flight. ARTEMIS has been designed to be the real-time simulation backbone to stimulate all required Ares components for verification testing. ARTE_VIIS provides high -fidelity dynamics, actuator, and sensor models to simulate an accurate flight trajectory in order to ensure realistic test conditions. ARTEMIS has been designed to take advantage of the advances in underlying computational power now available to support hardware-in-the-loop testing to achieve real-time simulation with unprecedented model fidelity. A modular realtime design relying on a fully distributed computing architecture has been implemented.