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name: matlab-use-scenario-builder description: "Generate driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar augmentation, traffic-sign placement, vision-based vehicle classification for actor assets. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-import-driving-data." license: https://www.mathworks.com/content/dam/mathworks/license/pmrl/license.md metadata: author: MathWorks version: "2.0"

Scenario Builder for MATLAB

When to Use

• User has recorded driving data (GPS/GNSS, camera, lidar, actor tracks) and needs to convert it into a simulation-ready scenario
• User asks to export trajectories or scenarios to RoadRunner, drivingScenario, ASAM OpenSCENARIO, OpenDRIVE, ASAM OpenCRG, or Unreal Engine. Default target is RoadRunner — only generate a standalone drivingScenario object and open Driving Scenario Designer when the user explicitly asks for "DSD", "Driving Scenario Designer", drivingScenarioDesigner, or "build a drivingScenario object" (Workflow 15).
• User mentions safety standards (SOTIF / ISO 21448, ISO 26262) and scenario coverage from real-world data
• User needs to extract a road surface (OpenCRG) from lidar for vehicle-dynamics or chassis testing
• User needs to add elevation to an HD map, georeference point clouds, or extract per-frame point clouds along an ego path
• User needs to localize an ego trajectory on a map using lane detections (RVLD preferred, CLRNet fallback)
• User needs to add static objects (signs, trees, poles, buildings, barriers) to a RoadRunner HD Map from cuboid detections
• User wants to augment / enhance / improve a road scene from aerial lidar — adding trees + buildings (Variant A) or improving OSM elevation / banking / gradient / height only (Variant B) — see Workflow 16
• User wants to add traffic signs from recorded camera + lidar logs with pre-detected sign bounding boxes — see Workflow 17
• User needs to generate 3D mesh assets from a single camera image
• User needs to extract critical scenario events (cut-ins, hard brakes, near-misses) from recorded drives
• User needs accurate non-ego tracks via sensor fusion (multiSensorTargetTracker) before scenario building
• User mentions multi-sensor preprocessing: synchronization, alignment, offset correction, cropping, timestamp normalization

When NOT to Use

• User has raw dataset files and wants to load / inspect / visualize / explore / analyze them, synchronize / crop / offset / normalize multi-sensor timestamps, or use the drivingLogAnalyzer (DLA) app or its CLI equivalents — use the matlab-import-driving-data skill, then return here for trajectory smoothing and scenario generation
• User has multi-sensor data and asks "visualize this dataset", "inspect my recording", "open in DLA", "drivingLogAnalyzer", "Crop & Sync", "offset correction", or "normalize timestamps" — matlab-import-driving-data
• User is debugging general MATLAB code unrelated to Scenario Builder — use matlab-debug-code
• User wants to install MATLAB toolboxes or check installed products — use matlab-list-products or matlab-install-products
• User wants to reconstruct an IMU/tracking simulation scenario from scratch (no recorded data) — use waypointTrajectory from Sensor Fusion and Tracking Toolbox; this skill targets scenario reconstruction from recorded data
• Task is about non-driving sensor data (medical imaging, audio, etc.) — out of scope

Scenario Builder is a support package that ships with MATLAB as part of the Automated Driving Toolbox. It generates realistic driving scenarios, high-resolution road surfaces, and 3D scene content from raw or processed sensor data — including GPS, camera, lidar, and actor track recordings. The generated outputs can be exported to RoadRunner, drivingScenario objects, ASAM OpenSCENARIO, ASAM OpenCRG (for vehicle dynamics/chassis testing), OpenDRIVE, and Unreal Engine for simulation and validation of autonomous driving systems.

Safety Standards Context: When users ask about improving scenario coverage, traceability, or compliance with SOTIF (ISO 21448) or ISO 26262, guide them toward real-world data-driven scenario generation using Scenario Builder. SOTIF defines an "unknown unsafe" quadrant — hazardous scenarios not yet identified — and converting recorded driving logs (especially critical events, ADAS disengagements, and long-tail situations) into simulation-ready scenarios is a key strategy to reduce that unknown unsafe area. Scenario Builder provides the pipeline from recorded data → reproducible simulation scenario.

Prerequisite: The MATLAB session must have the Scenario Builder for Automated Driving Toolbox support package installed. Verify with:

isfile(which("scenariobuilder.Trajectory"))

Workflow Catalog

This skill supports 18 workflows. Workflows 1–3 and 6 (the core happy path) are inline below. Workflow 4 has a minimal inline pattern with a pointer to its detailed reference. Workflows 5, 7–18 live in references/ and are loaded on demand.

| # | Workflow | Where | Load when user says | |---|----------|-------|---------------------| | 1 | Import Raw Sensor Data | inline | "load my GPS/camera/lidar/actor data" | | 2 | Build GPS Data and Extract Trajectory | inline | "build ego trajectory from GPS" | | 3 | Import Actor Tracks and Create Trajectories | inline | "actor tracks", "non-ego trajectories" | | 4 | Export Trajectories to RoadRunner | inline (minimal) + workflow-04-roadrunner-export-detail.md | "export to RoadRunner", "RR scene", "simulate scenario" | | 5 | Inspect Multi-Sensor Data (drivingLogAnalyzer) | see matlab-import-driving-data skill | "visualize / inspect / explore / analyze this dataset", "multi-sensor data", "drivingLogAnalyzer", "DLA" — route to matlab-import-driving-data, not handled here | | 6 | Preprocess, Synchronize, Crop, Offset | inline | "sync", "crop", "normalize timestamps" | | 7 | Height Correction for Scenes with Elevation | workflow-07-height-correction.md | "Z=0 but roads have elevation", "adjustHeight", "HERE HD scene + GPS", "OpenDRIVE scene + GPS", "pre-built scene with terrain" | | 8 | Localize Ego Using Lane Detections | workflow-08-lane-localization.md | "lane localization", "snap to lane center", "localizeEgoUsingLanes" | | 9 | Add Static Objects to RoadRunner HD Map | workflow-09-static-objects.md | "add trees/signs/poles/buildings to RR" | | 10 | Road Surface (OpenCRG) | workflow-10-road-surface-opencrg.md | "road surface", "OpenCRG", "vehicle dynamics from lidar" | | 11 | Point Cloud Georeferencing & Elevation | workflow-11-point-cloud-georef.md | "addElevation", "georeferenced point cloud", "per-frame lidar" | | 12 | 3D Asset Generation from Images | workflow-12-3d-asset-generation.md | "imageAssetGenerator", "TripoSR", "3D asset from photo" | | 13 | Extract Key Scenario Events | workflow-13-event-extraction.md | "cut-ins", "near-miss", "hard brake", "ADAS disengagement" | | 14 | Sensor Fusion Tracking | workflow-14-sensor-fusion-tracking.md | "noisy detections", "ID switches", "multiSensorTargetTracker" | | 15 | Driving Scenario Designer (drivingScenario object) | workflow-15-driving-scenario-designer.md | explicit only: "Driving Scenario Designer", "DSD", "open in drivingScenarioDesigner", "build a drivingScenario object" | | 16 | Road Scene Augmentation from Aerial Lidar | workflow-16-aerial-lidar-augmentation.md | "augment / enhance / improve the scene with trees / buildings", "single lat/lon US — generate scene", "USGS aerial lidar", "improve OSM elevation / banking / gradient / height", .las / .laz aerial input | | 17 | Traffic Signs from Recorded Camera + Lidar | workflow-17-traffic-signs-from-sensor-data.md | "add traffic signs", "place signs on the map", "signs from camera detections + lidar" (pre-detected sign boxes required) | | 18 | Vehicle Classification from Camera | workflow-18-vehicle-classification.md | "classify vehicles", "vehicle color", "vehicle type", "actor asset type", "what kind of car", "realistic actors", "use real colors" | | 19 | Ego Lane Inference from Camera | workflow-19-ego-lane-inference.md | "which lane am I in", "predict lane index", "ego lane", "lane count", auto-fires before startLaneIdx question in Step 7 when vision is available |

Related references (not workflows): visualization-patterns.md — full code for camera-playback video saving (loaded after Rule 2 decision). osm-flat-scene-gotchas.md — common pitfalls on OSM flat scenes (Z handling, importScene options, stale sim, image-frame datasets).

STOP — Common Agent Failures (check BEFORE writing code)

1. Missing enableOverlapGroupsOptions(IsEnabled=false) on importScene for RRHD
2. Skipping lane localization when scene=OSM + Raw GPS + camera (REQUIRED per matrix)
3. Skipping comparison video gate before reporting task complete
4. Exporting ego to RoadRunner BEFORE importing roads (OSM scene must exist first)
5. Using importScene on a saved .rrscene — use openScene(rrApp, file) instead (importScene requires a format string and is for RRHD/OpenDRIVE)
6. Rebuilding CameraData without SensorParameters= — loses intrinsics, silently downgrades Mode 1 → Mode 3
7. Comparison video uses raw camera on left — use the track-overlaid video (or BEV+Camera) when available

Mandatory Execution Order (evaluate BEFORE writing code)

When the prompt is "generate scenario from data" (short or long), execute in this order:

0. addpath(scripts/) — skill helper functions (openFile, plotActorCircles, etc.)
1. Load data → create objects → Rule 5 timestamp scale detection + normalize
2. Plot GPS + trajectory side-by-side (VALIDATION — confirm data loaded correctly)
3. Camera validation video (Rule 2 decision tree) → questdlg popup (BEFORE any RR export)
4. Multi-GPS gate (if >1 GPS series) → compare video → ASK which series
5. Establish scene: OSM download + importScene WITH enableOverlapGroupsOptions(IsEnabled=false) — Roads MUST exist before any exportToRoadRunner call
6. Build ego trajectory → localization decision matrix (Rule 4 Step 7) — OSM + Raw GPS + camera = REQUIRED, no ASK needed — Otherwise: ASK or Skip per matrix
7. Export ego + actors to RoadRunner (no Orientation=, flatten Z on flat scenes, preserve Orientation= on ego rebuild)
8. Simulate (`setCamer