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 Jun Times are displayed in time zone: (UTC) Coordinated Universal Time change
06:00 - 07:30 | Session 2: Testing, Analysis, Reasoning, and MonitoringSEAMS 2020 at SEAMS Chair(s): Sona GhahremaniHasso Plattner Institute, University of Potsdam | ||
06:00 5mTalk | Leveraging Test Logs for Building a Self-Adaptive Path PlannerNIER SEAMS 2020 Kun LiuPeking University, China, Xiaoyi ZhangNational Institute of Informatics, Japan, Paolo ArcainiNational Institute of Informatics
, Fuyuki IshikawaNational Institute of Informatics, Wenpin JiaoPeking 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 MoriJapan Aerospace Exploration Agency, Japan, Naoko OkuboJapan Aerospace Exploration Agency, Japan, Yasushi UedaJapan Aerospace Exploration Agency, Japan, Masafumi KatahiraJapan Aerospace Exploration Agency, Toshiyuki AmagasaUniversity 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 KumarUniversity of Birmingham, United Kingdom, Tao ChenLoughborough University, Rami BahsoonUniversity of Birmingham, Rajkumar BuyyaUniversity 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 CamaraUniversity of York, Alessandro Vittorio PapadopoulosMälardalen University, Thomas VogelHumboldt-Universität zu Berlin, Danny WeynsKU Leuven, David GarlanCarnegie Mellon University, Shihong HuangFlorida Atlantic University, Kenji TeiWaseda 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 LIPeking University, China, Sridhar AdepuSingapore University of Technology and Design, Singapore, Eunsuk KangCarnegie Mellon University, David GarlanCarnegie Mellon University Pre-print Media Attached | ||
06:25 65mOther | Q&A and Discussion (Session 2) SEAMS 2020 | ||