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--- en:safeav:maps:summary [2026/04/23 11:29] – raivo.sell
+++ en:safeav:maps:summary [2026/04/24 09:59] (current) – raivo.sell
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+====== Summary ======
+The chapter develops a comprehensive view of perception, mapping, and localization as the foundation of autonomous systems, emphasizing how modern autonomy builds on both historical automation (e.g., autopilots across domains) and recent advances in AI. It explains how perception converts raw sensor data—across cameras, LiDAR, radar, and acoustic systems—into structured understanding through object detection, sensor fusion, and scene interpretation. A key theme is that no single sensor is sufficient; instead, robust autonomy depends on multi-modal sensor fusion, probabilistic estimation, and careful calibration to manage uncertainty. The chapter also highlights the transformative role of AI, particularly deep learning, in enabling scalable perception and scene understanding, while noting that these methods introduce new challenges related to data dependence, generalization, and interpretability.
+A second major focus is on sources of instability and validation, where the chapter connects environmental effects (weather, electromagnetic interference), infrastructure constraints, and semiconductor economics to system-level performance. It underscores that validation must be grounded in the operational design domain (ODD) and cannot rely solely on physical testing, requiring a combination of simulation, hardware-in-the-loop, and scenario-based methods. The introduction of AI further complicates verification and validation because of its probabilistic, non-deterministic nature, challenging traditional assurance techniques. As a result, safety approaches across domains are evolving toward lifecycle-based assurance, incorporating data governance, simulation-driven testing, and continuous monitoring. The chapter concludes with a structured validation framework that links perception, mapping, and localization performance to system-level safety metrics, emphasizing reproducibility, coverage, and traceability in building a credible safety case.

en/safeav/maps/summary.txt · Last modified: 2026/04/24 09:59 by raivo.sell