Leveraging Test Logs for Building a Self-Adaptive Path PlannerNIER
Recent approaches in testing autonomous driving systems (ADS) are able to generate a scenario in which the autonomous car collides , and a different ADS configuration that avoids the collision. However, such test information is too low level to be used by engineers to improve the ADS. In this paper, we consider a path planner component provided by our industry partner, that can be configured through some weights. We propose a technique to automatically re-engineer the path planner in terms of a self-adaptive path planner (SAPP) following the MAPE loop reference architecture. The Knowledge Base (KB) of SAPP contains descriptions of collision scenarios discovered with testing, and the corresponding alternative weights that avoid the collisions. We forecast two main usages of SAPP. First of all, designers are provided with a prototype that should facilitate the re-implementation of the path planner. As second usage, SAPP can be useful for improving the diversity of testing, as performing test case generation on SAPP will guarantee to find dangerous situations different from those used to build SAPP. Preliminary experiments indicate that SAPP can effectively adapt on the base of the solutions stored in KB.
Tue 30 JunDisplayed time zone: (UTC) Coordinated Universal Time change
06:00 - 07:30 | Session 2: Testing, Analysis, Reasoning, and MonitoringSEAMS 2020 at SEAMS Chair(s): Sona Ghahremani Hasso Plattner Institute, University of Potsdam | ||
06:00 5mTalk | Leveraging Test Logs for Building a Self-Adaptive Path PlannerNIER SEAMS 2020 Kun Liu Peking University, China, Xiao-Yi Zhang National Institute of Informatics, Japan, Paolo Arcaini National Institute of Informatics
, Fuyuki Ishikawa National Institute of Informatics, Wenpin Jiao Peking University, China Pre-print Media Attached | ||
06:05 5mTalk | Supporting Viewpoints to Review the Lack of Requirements in Space Systems with Machine LearningExperience SEAMS 2020 Kenji Mori Japan Aerospace Exploration Agency, Japan, Naoko Okubo Japan Aerospace Exploration Agency, Japan, Yasushi Ueda Japan Aerospace Exploration Agency, Japan, Masafumi Katahira Japan Aerospace Exploration Agency, Toshiyuki Amagasa University of Tsukuba, Japan Media Attached | ||
06:10 5mTalk | DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint ReasoningTechnicalBest Student Paper SEAMS 2020 Satish Kumar University of Birmingham, United Kingdom, Tao Chen Loughborough University, Rami Bahsoon University of Birmingham, Rajkumar Buyya University of Melbourne, Australia DOI Pre-print Media Attached | ||
06:15 5mTalk | Towards Bridging the Gap between Control and Self-Adaptive System PropertiesNIER SEAMS 2020 Javier Camara University of York, Alessandro Vittorio Papadopoulos Mälardalen University, Thomas Vogel Humboldt-Universität zu Berlin, Danny Weyns KU Leuven, David Garlan Carnegie Mellon University, Shihong Huang Florida Atlantic University, Kenji Tei Waseda University / National Institute of Informatics, Japan DOI Pre-print Media Attached | ||
06:20 5mTalk | Explanation for Human-on-the-loop: a probabilistic model checking approachNIER SEAMS 2020 NIANYU LI Peking University, China, Sridhar Adepu Singapore University of Technology and Design, Singapore, Eunsuk Kang Carnegie Mellon University, David Garlan Carnegie Mellon University Pre-print Media Attached | ||
06:25 65mOther | Q&A and Discussion (Session 2) SEAMS 2020 | ||