Aurora Schmidt

A Formally Verified Hybrid System for the Next-Generation Airborne Collision Avoidance System

The Next-Generation Airborne Collision Avoidance System ACASi¾?X is intended to be installed on all large aircraft to give advice to pilots and prevent mid-air collisions with other aircraft. It is currently being developed by the Federal Aviation Administration FAA. In this paper we determine the geometric configurations under which the advice given by ACAS X is safe under a precise set of assumptions and formally verify these configurations using hybrid systems theorem proving techniques. We conduct an initial examination of the current version of the real ACAS X system and discuss some cases where our safety theorem conflicts with the actual advisory given by that version, demonstrating how formal, hybrid approaches are helping ensure the safety of ACAS X. Our approach is general and could also be used to identify unsafe advice issued by other collision avoidance systems or confirm their safety.

Complex contagions and the diffusion of popular Twitter hashtags in Nigeria

Social media sites such as Facebook and Twitter provide highly granular time-stamped data about the interactions and communications between people and provide us unprecedented opportunities for empirically testing theory about information flow in social networks. Using publicly available data from Twitter’s free API (Application Program Interface), we track the adoption of popular hashtags in Nigeria during 2014. These hashtags reference online marketing campaigns, major news stories, and events and issues specific to Nigeria, including reactions to the kidnapping of 276 schoolgirls in Northeastern Nigeria by the Islamic extremist group Boko Haram. We find that hashtags related to Nigerian sociopolitical issues, including the #bringbackourgirls hashtag, which was associated with protests against the Nigerian government’s response to the kidnapping, are more likely to be adopted among densely connected users with multiple network neighbors who have also adopted the hashtag, compared to mainstream news hashtags. This association between adoption threshold and local network structure is consistent with theory about the spread of complex contagions, a type of social contagion which requires social reinforcement from multiple adopting neighbors. Theory also predicts the need for a critical mass of adopters before the contagion can become viral. We illustrate this with the #bringbackourgirls hashtag by identifying the point at which the local social movement transforms into a more widespread phenomenon. We also show that these results are robust across both the follow and reply/mention/retweet networks on Twitter. Our analysis involves data mining records of hashtag adoption and of the social connections between adopters.

Formal verification of ACAS X, an industrial airborne collision avoidance system

Formal verification of industrial systems is very challenging, due to reasons ranging from scalability issues to communication difficulties with engineering-focused teams. More importantly, industrial systems are rarely designed for verification, but rather for operational needs. In this paper we present an overview of our experience using hybrid systems theorem proving to formally verify ACAS X, an airborne collision avoidance system for airliners scheduled to be operational around 2020. The methods and proof techniques presented here are an overview of the work already presented in [8], while the evaluation of ACAS X has been significantly expanded and updated to the most recent version of the system, run 13. The effort presented in this paper is an integral part of the ACAS X development and was performed in tight collaboration with the ACAS X development team.

Hierarchical zero-shot classification with convolutional neural network features and semantic attribute learning

We examine hierarchical approaches to image classification problems that include categories for which we have no training examples. Building on prior work in hierarchical classification that optimizes the trade-off between depth in a tree and accuracy of placement, we compare the performance of multiple formulations of the problem on both previously seen (non-novel) and previously unseen (novel) classes. We use a subset of 150 object classes from the ImageNet ILSVRC2012 data set, for which we have 218 human-annotated semantic attribute labels and for which we compute deep convolutional features using the OVERFEAT network. We quantitatively evaluate several approaches, using input posteriors derived from distances to SVM classifier boundaries as well as input posteriors based on semantic attribute estimation. We find that the relative performances of the methods differ in non-novel and novel applications and achieve information gains in novel applications through the incorporation of attribute-based posteriors.

Formally Verified Safe Vertical Maneuvers for Non-deterministic, Accelerating Aircraft Dynamics

We present the formally verified predicate and strategy used to independently evaluate the safety of the final version (Run 15) of the FAAs next-generation air-traffic collision avoidance system, ACAS X. This approach is a general one that can analyze simultaneous vertical and horizontal maneuvers issued by aircraft collision avoidance systems. The predicate is specialized to analyze sequences of vertical maneuvers, and in the horizontal dimension is modular, allowing it to be safely composed with separately analyzed horizontal dynamics. Unlike previous efforts, this approach enables analysis of aircraft that are turning, and accelerating non-deterministically. It can also analyze the safety of coordinated advisories, and encounters with more than two aircraft. We provide results on the safety evaluation of ACAS X coordinated collision avoidance on a subset of the system state space. This approach can also be used to establish the safety of vertical collision avoidance maneuvers for other systems with complex dynamics.

Probabilistic model checking of the next-generation airborne collision avoidance system

We present a probabilistic model checking approach for evaluating the safety and operational suitability of the Airborne Collision Avoidance System X (ACAS X). This system issues advisories to pilots when the risk of mid-air collision is imminent, and is expected to be equipped on all large, piloted aircraft in the future. We developed an approach to efficiently compute the probabilities of generically specified events and the most likely sequences of states leading to those events within a discrete-time Markov chain model of aircraft flight and ACAS X. The probabilities and sequences are computed for all states in the model. Events of interest include near mid-air collisions (NMACs) and undesirable sequences of advisories that affect operational suitability. We have validated numerous observations of the model with higher-fidelity simulations of the full system. This analysis has revealed several characteristics of ACAS Xs behavior.