Alëna Rodionova

Selected publications

1. 

   Time-Robust Control for STL Specifications.

   Alëna Rodionova, Lars Lindemann Manfred Morari, George J. Pappas

   In Proceedings of the IEEE Conference on Decision and Control (CDC) 2021

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   We present a robust control framework for time-critical systems in which satisfying real-time constraints robustly is of utmost importance for the safety of the system. Signal Temporal Logic (STL) provides a formal means to express a large variety of real-time constraints over signals and is suited for planning and control purposes as it allows us to reason about the time robustness of such constraints. The time robustness of STL particularly quanti es the extent to which timing uncertainties can be tolerated without violating real-time specifications. In this paper, we first pose a control problem in which we aim to find an optimal input sequence to a control system that maximizes the time robustness of an STL constraint. We then propose a Mixed Integer Linear Program (MILP) encoding and provide correctness guarantees along with a complexity analysis of the encoding. We also show in two case studies that maximizing STL time robustness allows to account for timing uncertainties of the underlying control system.

   @inproceedings{rodionova2021time, author={Rodionova, Al{\"e}na and Lindemann, Lars and Morari, Manfred and Pappas, George J.}, booktitle={2021 60th IEEE Conference on Decision and Control (CDC)}, title={Time-Robust Control for STL Specifications}, year={2021}, volume={}, number={}, pages={572-579}, doi={10.1109/CDC45484.2021.9683477} }

2. 

   How safe is safe enough? Automatic safety constraints boundary estimation for decision-making in automated vehicles.

   Alëna Rodionova, Ignacio Alvarez, Maria S. Elli, Fabian Oboril, Johannes Quast, Rahul Mangrharam

   IEEE Intelligent Vehicles Symposium 2020

   Abs Bib PDF

   The determination of safety assurances for automated driving vehicles is one of the most critical challenges in the industry today. Several behavioral safety models for automated driving have been proposed recently and standards discussions are on the way. In this paper we present a method to automatically explore the performance of automated vehicle (AV) safety models utilizing robustness of Metric Temporal Logic (MTL) specifications as a continuous metric of safety. We present a case study of the Responsibility Sensitive Safety model (RSS), introducing a safety evaluation pipeline based on the CARLA driving simulator, RSS and a set of safety-critical driving scenarios. Our method automatically extracts safety relevant profiles for these scenarios providing practical parametric boundaries for implementation. Furthermore, we evaluate the trade-offs between safety and utility within the safe RSS parameter space through a proposed naturalistic benchmark challenge that we open-sourced. We analyze different RSS parameter configurations including assertive and more conservative settings, extracted by our specification-driven framework. Our results show that while maintaining the safety boundaries, the extracted RSS configuration for assertive driving behavior achieves the highest utility.

   @article{rodionova2020rss, author={Rodionova, Al{\"e}na and Alvarez, Ignacio and Elli, Maria Soledad and Oboril, Fabian and Quast, Johannes and Mangharam, Rahul}, booktitle={2020 IEEE Intelligent Vehicles Symposium (IV)}, title={How safe is safe enough? Automatic Safety Constraints Boundary Estimation for Decision-Making in Automated Vehicles}, year={2020}, volume={}, number={}, pages={1457-1464}, doi={10.1109/IV47402.2020.9304756} }

3. 

   Learning-’N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme

   Alëna Rodionova, Yash Vardhan Pant, Connor Kurtz, Kuk Jang, Houssam Abbas, Rahul Mangharam

   ACM Transactions on Cyber-Physical Systems 2021

   Abs Bib PDF

   Urban Air Mobility, the scenario where hundreds of manned and Unmanned Aircraft System (UAS) carry out a wide variety of missions (e.g. moving humans and goods within the city), is gaining acceptance as a transportation solution of the future. One of the key requirements for this to happen is safely managing the air traffic in these urban airspaces. Due to the expected density of the airspace, this requires fast autonomous solutions that can be deployed online. We propose Learning-’N-Flying (LNF) a multi-UAS Collision Avoidance (CA) framework. It is decentralized, works on-the-fly and allows autonomous UAS managed by different operators to safely carry out complex missions, represented using Signal Temporal Logic, in a shared airspace. We initially formulate the problem of predictive collision avoidance for two UAS as a mixed-integer linear program, and show that it is intractable to solve online. Instead, we first develop Learning-to-Fly (L2F) by combining: a) learning-based decision-making, and b) decentralized convex optimization-based control. LNF extends L2F to cases where there are more than two UAS on a collision path. Through extensive simulations, we show that our method can run online (computation time in the order of milliseconds), and under certain assumptions has failure rates of less than 1% in the worst-case, improving to near 0% in more relaxed operations. We show the applicability of our scheme to a wide variety of settings through multiple case studies.

   @article{rodionova2021learning, title = {Learning-'N-Flying: A Learning-based, Decentralized Mission Aware UAS Collision Avoidance Scheme}, author = {Rodionova, Al{\"e}na and Pant, Yash Vardhan and Kurtz, Connor and Jang, Kuk and Abbas, Houssam and Mangharam, Rahul}, journal = {ACM Transactions on Cyber-Physical Systems}, year = {2021}, pdf = {https://arxiv.org/abs/2101.10404}, selected = {true} }