https://www.robolabor.ee/homelab/ 2026-04-17T13:36:43+00:00 https://www.robolabor.ee/homelab/ https://www.robolabor.ee/homelab/lib/tpl/arctic/images/favicon.ico text/html 2025-10-21T16:18:12+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.robolabor.ee/homelab/en/safeav/maps/ai?rev=1761052692&do=diff AI-based Perception and Scene Understanding [ Bachelors (1st level) classification icon ] Advances in AI, especially the convolutional neural network, allow us to process raw sensory information and recognize objects and categorize them into classes with higher levels of abstraction (pedestrians, cars, trees, etc.). Taking these categories into account allows autonomous vehicles to understand the scene and reason about future actions of the vehicle as well as about the other participants in … text/html 2026-04-07T10:12:57+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.robolabor.ee/homelab/en/safeav/maps/detection?rev=1775545977&do=diff Object Detection, Sensor Fusion, Mapping, and Positioning [ Bachelors (1st level) classification icon ] Object Detection Object detection is the fundamental perception function that allows an autonomous vehicle to identify and localize relevant entities in its surroundings. It converts raw sensor inputs into structured semantic and geometric information, forming the basis for higher-level tasks such as tracking, prediction, and planning. By maintaining awareness of all objects within its op… text/html 2026-04-07T10:16:13+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.robolabor.ee/homelab/en/safeav/maps/instability?rev=1775546173&do=diff Design Challenges Designing autonomous systems which perform reliability has many design challenges. For the front-end of the AV pipeline discussed in this chapter, the challenges center around gracefully working across a range of operating conditions (ODD), performance characteristics of the sensors, and supply chain concerns. text/html 2026-04-07T10:04:17+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.robolabor.ee/homelab/en/safeav/maps/summary?rev=1775545457&do=diff 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 … text/html 2026-04-07T10:04:00+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.robolabor.ee/homelab/en/safeav/maps/validation?rev=1775545440&do=diff Validation Approaches [ Masters (2nd level) classification icon ] Having designed a sensor, object recognition, and location services section, how does one test these components. The fundamentals are consistent with the discussions in chapter 2. One defines an ODD, builds tests underneath this ODD, applies these tests, and determines correctness. The application of the tests can virtual (simulation), physical (test track), or even a mix based on components (Hardware in Loop or Software in…