PASSAGE: A Real-Terrain Benchmark for Trustworthy Constrained Path Planning

Machine learning is increasingly used for autonomous navigation, yet safety regulations governing safety-critical AI applications require rigorous datasets and protocols to evaluate learned planners on terrain-aware routing. Existing path-planning benchmarks address this only partially: synthetic grids offer algorithmic control without geographic realism, while Geographic Information System (GIS) resources provide real terrain without controllable obstacles or standardised baselines. To close this gap and steer research toward more trustworthy AI-based planners, especially in line with EASA and FAA guidance on AI, we introduce **PASSAGE**, an open-source benchmark and dataset that reframes constrained terrain path planning as a *controlled trustworthiness-oriented evaluation harness* rather than a static dataset. PASSAGE is sampled from a well-defined Operational Design Domain (ODD) built on JAXA ALOS AW3D30 elevation at its native 30 m/pixel resolution, and is derived into seven input-tensor resolutions from 64×64 to 4096×4096 pixels, totalling *4.4 million samples* for ≈ *2.43 TB* of compressed data. Each sample is a real-terrain cost grid annotated with start and goal markers, procedurally generated super-ellipse obstacles, and exact A* path labels under three physically interpretable cost models. The release pairs this dataset with a deterministic generation pipeline, a geographic hold-out, ODD-coverage diagnostics, and a rigorous evaluation protocol — a shift from *dataset collection* towards *auditable benchmark design* that provides a rare level of experimental control for studying robustness, generalisation, and other trustworthiness properties of safety-critical embedded AI. PASSAGE thereby offers a common surface for comparing AI-based path planners, including two dense-vision approaches we introduce as illustrative examples (path-mask segmentation and depth-estimation-inspired cost-to-go regression), with graph-neural and reinforcement-learning families as natural future extensions. Although motivated by aerospace trustworthiness requirements such as completeness, correctness, and representativeness, PASSAGE broadly supports trustworthy machine learning across robotics, autonomous navigation, and other safety-critical planning domains. Code (anonymous repository) and dataset (anonymous mirror) are released openly under permissive licences.