IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification, regression, and related tasks. Apart from the graph construction (based on AI planning problems) that is interesting in its own right, the data set possesses distinctly different characteristics from popularly used benchmarks. The data set, named IPC, consists of two self-contained versions, grounded and lifted, both including graphs of large and skewedly distributed sizes, posing substantial challenges for the computation of graph models such as graph kernels and graph neural networks. The graphs in this data set are directed and the lifted version is acyclic, offering the opportunity of benchmarking specialized models for directed (acyclic) structures. Moreover, the graph generator and the labeling are computer programmed; thus, the data set may be extended easily if a larger scale is desired. The data set is accessible from \url{https://github.com/IBM/IPC-graph-data}.Comment: ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. The data set is accessible from https://github.com/IBM/IPC-graph-dat

InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning

Inferring traffic object such as lane information is of foremost importance for deployment of autonomous driving. Previous approaches focus on offline construction of HD map inferred with GPS localization, which is insufficient for globally scalable autonomous driving. To alleviate these issues, we propose online HD map learning framework that detects HD map elements from onboard sensor observations. We represent the map elements as a graph; we propose InstaGraM, instance-level graph modeling of HD map that brings accurate and fast end-to-end vectorized HD map learning. Along with the graph modeling strategy, we propose end-to-end neural network composed of three stages: a unified BEV feature extraction, map graph component detection, and association via graph neural networks. Comprehensive experiments on public open dataset show that our proposed network outperforms previous models by up to 13.7 mAP with up to 33.8X faster computation time.Comment: Workshop on Vision-Centric Autonomous Driving (VCAD) at Conference on Computer Vision and Pattern Recognition (CVPR) 202

Platform Dependent Verification: On Engineering Verification Tools for 21st Century

The paper overviews recent developments in platform-dependent explicit-state LTL model checking.Comment: In Proceedings PDMC 2011, arXiv:1111.006