SEAMS 2020
Mon 29 June - Fri 3 July 2020
co-located with ICSE 2020
Thu 2 Jul 2020 07:00 - 07:05 at SEAMS - Session 5: Design, Verification & Explainability Chair(s): Javier Camara

In the last four years, the number of distinct autonomous vehicles platforms deployed in the streets of California increased 6-fold, while the reported accidents increased 12-fold. This can become a trend with no signs of subsiding as it is fueled by a constant stream of innovations in hardware sensors and machine learning software. Meanwhile, if we expect the public and regulators to trust the autonomous vehicle platforms, we need to find better ways to solve the problem of adding technological complexity without increasing the risk of accidents. We studied this problem from the perspective of reliability engineering in which a given risk of an accident has an associated severity and probability of occurring. Timely information on accidents is important for engineers to anticipate and reuse previous failures to approximate the risk of accidents in a new city. However, this is challenging in the context of autonomous vehicles because of the sparse nature of data on the operational scenarios (driving trajectories in a new city). Our approach was to mitigate data sparsity by reducing the state space through the monitoring of multiple-vehicles operations. We then minimized the risk of accidents by determining proper allocation of tests for each equivalence class. Our contributions comprise (1) a set of strategies to monitor the operational data of multiple autonomous vehicles, (2) a Bayesian model that estimates changes in the risk of accidents, and (3) a feedback control-loop that minimizes these risks by reallocating test effort. Our results are promising in a sense that we were able to measure and control risk for a diversity of changes in the operational scenarios. We evaluated our models with data from two real cities with distinct traffic patterns and made the data available for the community.

Thu 2 Jul
Times are displayed in time zone: (UTC) Coordinated Universal Time change

07:00 - 08:20: Session 5: Design, Verification & ExplainabilitySEAMS 2020 at SEAMS
Chair(s): Javier CamaraUniversity of York
07:00 - 07:05
Collective Risk Minimization via a Bayesian Model for Statistical Software TestingTechnical
SEAMS 2020
Joachim HaenselHasso Plattner Institute, University of Potsdam, Germany, Christian Medeiros AdrianoHasso-Plattner-Institute, Potsdam, Johannes DyckHasso Plattner Institute for Software Systems Engineering, Germany, Holger GieseHasso Plattner Institute, University of Potsdam
Pre-print Media Attached
07:05 - 07:10
Expecting the Unexpected: Developing Autonomous-System Design Principles for Reacting to Unpredicted Events and ConditionsNIER
SEAMS 2020
Assaf MarronWeizmann Institute of Science, Israel, Lior LimonadIBM Corporation, Israel, Sarah PollackWeizmann Institute of Science, Israel, David HarelWeizmann Institute of Science, Israel
Media Attached
07:10 - 07:15
Self-Protection Against Business Logic VulnerabilitiesNIER
SEAMS 2020
Silvan ZellerOmegapoint AB, Sweden, Narges KhakpourLinnaeus University, Danny WeynsKU Leuven, Daniel DeogunOmegapoint AB, Sweden
Media Attached File Attached
07:15 - 07:20
Towards Highly Scalable Runtime Models with HistoryNIER
SEAMS 2020
Lucas SakizloglouHasso Plattner Institute, University of Potsdam, Sona GhahremaniHasso Plattner Institute, University of Potsdam, Thomas Brand, Matthias BarkowskyHasso Plattner Institute, University of Potsdam, Germany, Holger GieseHasso Plattner Institute, University of Potsdam
DOI Pre-print Media Attached
07:20 - 08:20
Q&A and Discussion (Session 5)
SEAMS 2020