A Learning-Based Guidance Selection Mechanism for a Formally Verified Sense and Avoid Algorithm

This paper describes a learning-based strategy for selecting conflict avoidance maneuvers for autonomous unmanned aircraft systems. The selected maneuvers are provided by a formally verified algorithm and they are guaranteed to solve any impending conflict under general assumptions about aircraft dynamics. The decision-making logic that selects the appropriate maneuvers is encoded in a stochastic policy encapsulated as a neural network. The networks parameters are optimized to maximize a reward function. The reward function penalizes loss of separation with other aircraft while rewarding resolutions that result in minimum excursions from the nominal flight plan. This paper provides a description of the technique and presents preliminary simulation results

Supporting Validation of UAV Sense-and-Avoid Algorithms with Agent-Based Simulation and Evolutionary Search

A Sense-and-Avoid (SAA) capability is required for the safe integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace. Given their safety-critical nature, SAA algorithms must undergo rigorous verification and validation before deployment. The validation of UAV SAA algorithms requires identifying challenging situations that the algorithms have difficulties in handling. By building on ideas from Search-Based Software Testing, this thesis proposes an evolutionary-search-based approach that automatically identifies such situations to support the validation of SAA algorithms. Specifically, in the proposed approach, the behaviours of UAVs under the control of selected SAA algorithms are examined with agent-based simulations. Evolutionary search is used to guide the simulations to focus on increasingly challenging situations in a large search space defined by (the variations of) parameters that configure the simulations. An open-source tool has been developed to support the proposed approach so that the process can be partially automated. Positive results were achieved in a preliminary evaluation of the proposed approach using a simple two-dimensional SAA algorithm. The proposed approach was then further demonstrated and evaluated using two case studies, applying it to a prototype of an industry-level UAV collision avoidance algorithm (specifically, ACAS XU) and a multi-UAV conflict resolution algorithm (specifically, ORCA-3D). In the case studies, the proposed evolutionary-search-based approach was empirically compared with some plausible rivals (specifically, random-search-based approaches and a deterministic-global-search-based approach). The results show that the proposed approach can identify the required challenging situations more effectively and efficiently than the random-search-based approaches. The results also show that even though the proposed approach is a little less competitive than the deterministic-global-search-based approach in terms of effectiveness in relatively easy cases, it is more effective and efficient in more difficult cases, especially when the objective function becomes highly discontinuous. Thus, the proposed evolutionary-search-based approach has the potential to be used for supporting the validation of UAV SAA algorithms although it is not possible to show that it is the best approach

UAS Surveillance Criticality

The integration of unmanned aircraft systems (UAS) into the national airspace system (NAS) poses considerable challenges. Maintaining human safety is perhaps chief among these challenges as UAS remote pilots will need to interact with other UAS, piloted aircraft, and other conditions associated with flight. A research team of 6 leading UAS research universities was formed to respond to a set of surveillance criticality research questions. Five analysis tools were selected following a literature review to evaluate airborne surveillance technology performance. The analysis tools included: Fault Trees, Monte Carlo Simulations, Hazard Analysis, Design of Experiments (DOE), and Human-in-the-Loop Simulations. The Surveillance Criticality research team used results from these analyses to address three primary research questions and provide recommendations for UAS detect-and-avoid mitigation and areas for further research

A Review of Formal Methods applied to Machine Learning

We review state-of-the-art formal methods applied to the emerging field of the verification of machine learning systems. Formal methods can provide rigorous correctness guarantees on hardware and software systems. Thanks to the availability of mature tools, their use is well established in the industry, and in particular to check safety-critical applications as they undergo a stringent certification process. As machine learning is becoming more popular, machine-learned components are now considered for inclusion in critical systems. This raises the question of their safety and their verification. Yet, established formal methods are limited to classic, i.e. non machine-learned software. Applying formal methods to verify systems that include machine learning has only been considered recently and poses novel challenges in soundness, precision, and scalability. We first recall established formal methods and their current use in an exemplar safety-critical field, avionic software, with a focus on abstract interpretation based techniques as they provide a high level of scalability. This provides a golden standard and sets high expectations for machine learning verification. We then provide a comprehensive and detailed review of the formal methods developed so far for machine learning, highlighting their strengths and limitations. The large majority of them verify trained neural networks and employ either SMT, optimization, or abstract interpretation techniques. We also discuss methods for support vector machines and decision tree ensembles, as well as methods targeting training and data preparation, which are critical but often neglected aspects of machine learning. Finally, we offer perspectives for future research directions towards the formal verification of machine learning systems

System elements required to guarantee the reliability, availability and integrity of decision-making information in a complex airborne autonomous system

Current air traffic management systems are centred on piloted aircraft, in which all the main decisions are made by humans. In the world of autonomous vehicles, there will be a driving need for decisions to be made by the system rather than by humans due to the benefits of more automation such as reducing the likelihood of human error, handling more air traffic in national airspace safely, providing prior warnings of potential conflicts etc. The system will have to decide on courses of action that will have highly safety critical consequences. One way to ensure these decisions are robust is to guarantee that the information being used for the decision is valid and of very high integrity. [Continues.