Driving Data Importer

This skill loads raw driving sensor data into scenariobuilder.* objects (GPSData, CameraData, LidarData, ActorTrackData, Trajectory, laneData) and provides the CLI for every preprocessing step DLA exposes (sync, crop, offset, normalize, convert timestamps). It also covers the drivingLogAnalyzer (DLA) app as an opt-in inspection tool — see Rule 0 below; DLA is never a default step.

After wrapping is done, the canonical next move is matlab-scenario-builder (trajectory smoothing, scene/scenario generation, lane localization, RoadRunner / drivingScenario / OpenSCENARIO / OpenDRIVE / OpenCRG / Unreal export).

Rule 0 — DLA is opt-in only (HARD RULE, READ FIRST)

Never call `drivingLogAnalyzer` unless the user explicitly asks for it or has reported a sensor-data problem DLA is built to debug. Auto-launching DLA after wrapping data is a first-attempt-success failure: it stalls the user (a UI app forces context-switch, scrub, click, confirm) and signals that the agent is not confident the import worked.

When DLA IS allowed — only these two cases:

1. Explicit user request — user types DLA, drivingLogAnalyzer, "open in DLA", "inspect / visualize / explore / replay / analyze the recording", "open the driving log analyzer".
2. Reported sensor-data problem that DLA is the right tool for:

• "the sensors look out of sync"
• "camera and lidar timestamps don't match"
• "actor cuboids float above the cars" (overlay alignment)
• "I see missing frames / a gap in the timeline"
• "the offset looks wrong / the timeline is shifted"

When DLA is NOT allowed (defaults — go straight to `matlab-scenario-builder`):

• "Virtualize this data", "build a scenario from this data", "generate a scene", "export to RoadRunner / drivingScenario / OpenSCENARIO / OpenDRIVE / OpenCRG / Unreal", "simulate this drive", "I have data, do something with it".
• Anything that has a downstream simulation or scenario target.

If you ever feel an urge to add a "let me open DLA so you can verify" step after wrapping — stop. Save the wrapped objects to sandbox/<dataset>_wrapped.mat, print a short summary, and hand off.

When to Use

• User has raw driving dataset files (KITTI, nuScenes, Waymo, custom logs, ROS/ROS2 bags, .mat, .csv, .xls, video) and needs them loaded into scenariobuilder.* objects
• User says "open in DLA", "drivingLogAnalyzer", "inspect / visualize / explore / replay / analyze this dataset" (triggers DLA — Rule 0 case 1)
• User wants to map raw structs/tables/rosbag topics to GPSData, CameraData, LidarData, ActorTrackData, Trajectory, or laneData
• User wants to attach camera/lidar Mounting Location / Mounting Angles / Intrinsics / Ego Origin Height
• User asks for multi-sensor synchronization of any kind: "sync", "align sensors", "match sample rates", "resample to a common timeline", "sensor-to-sensor alignment"
• User asks for offset correction ("drag-to-align", "time offset", "shift this sensor by X seconds")
• User asks to crop / trim / extract a segment of a recording across sensors
• User asks to normalize timestamps ("common t=0", "time origin", "POSIX to seconds", "datetime to numeric")
• User wants to export sensor data to MAT, workspace, timetable, or a reproducible script
• User needs to inspect dataset structure, identify available sensor modalities, validate calibration transforms, or check for pre-computed annotations
• User needs to compute actor tracks from lidar when no annotations exist (clustering / detector / camera-based pipeline)
• User reports a sensor-data problem (Rule 0 case 2) — wrap, then offer DLA as the debugging path

When NOT to Use

• Do NOT auto-launch DLA after wrapping. If the user asked to virtualize / build a scenario / export to a sim format, finish wrapping and hand off to matlab-scenario-builder directly. (Rule 0.)
• User wants to build / generate / export a scenario (RoadRunner, drivingScenario, OpenSCENARIO, OpenDRIVE, OpenCRG, Unreal) — wrap here, then hand off to `matlab-scenario-builder`.
• User wants to smooth a trajectory, localize ego on a lane, correct height on a terrain scene, place static objects (signs/trees/poles), extract a road surface (OpenCRG) from lidar, generate 3D assets from images, add elevation to a map, or georeference point clouds — all matlab-scenario-builder
• User wants to run sensor-fusion tracking (multiSensorTargetTracker, JPDA + smoother) to get cleaner tracks — matlab-scenario-builder Workflow 14
• User is debugging general MATLAB code unrelated to dataset import — use matlab-debugging
• User wants to install a toolbox or check MATLAB products — use matlab-list-products / matlab-install-products
• Task is about non-driving sensor data (medical imaging, audio, etc.) — out of scope

Boundary heuristic: wrapping into scenariobuilder.* and any sync/crop/offset/normalize CLI work belongs here. The moment the user says scenario / scene / RoadRunner / simulate / drive in a virtual world / export to OpenSCENARIO / virtualize — wrap, save, hand off. DLA stays parked unless invoked by name or summoned by a reported problem.

IMPORTANT — Execution Rules

Rule 1: Inspect Before Importing

Always inspect the dataset structure first. Before writing any import code:

1. List the top-level directory structure
2. Identify what sensor modalities are available (GPS, lidar, camera, annotations)
3. Determine if pre-computed annotations/labels exist (3D bounding boxes, tracks)
4. Check calibration files for coordinate frame definitions

Rule 2: Check for Existing Annotations Before Computing Tracks

Never run a lidar tracker if the dataset already provides actor tracks or 3D bounding box annotations. Always check first:

• Look for annotation files (object_detection.json, labels/, annotations/, tracking/)
• Check if annotations are per-frame (temporal tracks) or single-keyframe only
• If annotations exist, map them directly to ActorTrackData
• If only keyframe annotations exist (no temporal tracking), inform the user and discuss options

Rule 3: Understand Dataset Structure Types

Driving datasets commonly have two types of recordings:

Always clarify which type the user's data is before proceeding. If data lacks annotations, inform the user that actor tracks must be computed (via lidar detection/tracking or camera detection) and set expectations about quality.

Rule 4: Validate Coordinate Frames

Before using any transform, verify:

1. What coordinate frame convention the dataset uses (e.g., X-forward vs Y-forward)
2. Whether extrinsic transforms are sensor-to-ego, sensor-to-vehicle, or sensor-to-sensor
3. Validate by checking that transformed ground points have Z near 0 in ego frame

Rule 5: Report Data Summary to User

After initial inspection, always present a summary:

Dataset: <name>
Recording: <ID/name>
Duration: <X seconds>
Available sensors:
  - GPS: <format, sample count, rate>
  - Lidar: <format, frame count, rate, sensor model>
  - Camera: <format, frame count, rate, resolution>
  - Annotations: <YES/NO — type if yes>
Calibration: <available transforms>

Common Dataset Formats

GPS / GNSS / IMU

Key fields needed: timestamps, latitude, longitude, altitude

Lidar Point Clouds

Important: Always check the point cloud coordinate frame. Common conventions:

• X-forward, Y-left, Z-up (ROS/vehicle standard)
• X-right, Y-forward, Z-up (some lidars)
• X-forward, Y-right, Z-up (KITTI)

Camera Images

Key info needed: timestamps, file paths, intrinsics, distortion model, extrinsics (camera-to-ego)

Calibration

Calibration files typically provide:

• Intrinsics: focal length, principal point, distortion coefficients
• Extrinsics: 4x4 homogeneous transforms between sensor frames
• Distortion model: pinhole, fisheye (Kannala), equidistant, etc.

Common pitfall: The naming of extrinsic transforms is inconsistent across datasets. A field named lidar_extrinsics could mean:

• lidar-to-ego (most common)
• lidar-to-camera
• ego-to-lidar (inverse)

Always verify by checking translation values against physical sensor mounting positions (e.g., lidar mounted ~1.7m high should have Z translation ~1.7 in lidar-to-ego).

3D Bounding Box Annotations

Common formats:

Per object:
  - class: "Car", "Truck", "Pedestrian", etc.
  - location_3d: [x, y, z] — center position in some reference frame
  - size: [length, width, height] in meters
  - orientation: quaternion or yaw angle
  - track_id: persistent ID across frames (if temporal tracking exists)

Frame of reference: Annotations may be in:

• Ego/vehicle frame (most common for driving datasets)
• World/global frame
• Sensor frame (lidar or camera)

Always check which frame and transform to ego if needed.

Import Pipeline

Step 1: GPS → GPSData

Canonical GPSData construction is THREE lines, always together — bare scenariobuilder.GPSData(...) is incomplete. The post-construction convertTimestamps + normalizeTimestamps calls are part of the canonical construction, not optional cleanup. Downstream APIs (synchronize, trajectory, actorprops, localizeEgoUsingLanes, RoadRunner export) expect numeric timestamps starting at t=0.

% Load timestamps, lat, lon, alt from dataset
gpsData = scenariobuilder.GPSData(timestamps, latitude, longitude, altitude);

% Canonical post-construction pair (always run both)
convertTimestamps(gpsData, "numeric");
timeRef = normalizeTimestamps(gpsData);

If you skip these two lines, the object will silently fail later (sample-rate mismatches in synchronize, scenario time bounds wrong, RoadRunner export errors). Run them every time, even when you "just" construct a GPSData for inspection — and even when the prompt only says "construct the GPSData object."

Altitude handling:

• If using OpenStreetMap roads: zero the altitude (altitude = zeros(...)) — OSM has no elevation
• If using a scene with terrain elevation: keep real altitude
• If altitude is missing: use zeros

Step 2: Annotations → ActorTrackData

When per-frame 3D annotations with track IDs exist:

% For each timestamp, collect track IDs and positions
timestamps = <Nx1 numeric>;
trackIDs = cell(N, 1);    % each cell: Mx1 string array
positions = cell(N, 1);   % each cell: Mx3 [x y z] in ego frame

for i = 1:N
    % Get annotations for frame i
    frameAnnots = <filter annotations for this frame>;
    trackIDs{i} = string({frameAnnots.track_id}');
    positions{i} = [frameAnnots.x, frameAnnots.y, frameAnnots.z];
end

trackData = scenariobuilder.ActorTrackData(timestamps, trackIDs, positions);

If annotations are in world frame (not ego frame):

% Transform world positions to ego-relative positions
% ActorTrackData expects positions relative to ego at each timestamp
for i = 1:N
    worldPos = positions_world{i};
    egoPos = egoPositionAtTime(i);  % from GPS/odometry
    egoYaw = egoYawAtTime(i);
    R = [cos(egoYaw) sin(egoYaw) 0; -sin(egoYaw) cos(egoYaw) 0; 0 0 1];
    positions{i} = (worldPos - egoPos) * R';
end

Step 3: Camera → CameraData

% Match camera image files to timestamps
imageFiles = dir(fullfile(camDir, '*.jpg'));
camTimestamps = <parse timestamps from filenames or metadata>;

cameraData = scenariobuilder.CameraData(camTimestamps, ...
    fullfile(camDir, {imageFiles.name}'), Name="FrontCamera");

Step 4: Lidar → LidarData

Always wrap lidar via `scenariobuilder.LidarData` — this is the only wrapper DLA accepts and the only one downstream Scenario Builder APIs (Workflow 10 OpenCRG extraction, Workflow 11 georeferencing) consume. Do not hand DLA raw pointCloud arrays or paths.

% Match lidar files (.pcd, .ply, .las/.laz) to timestamps
lidarFiles = dir(fullfile(lidarDir, '*.pcd'));
lidarTimestamps = <parse timestamps from filenames or metadata>;

lidarData = scenariobuilder.LidarData(lidarTimestamps, ...
    fullfile(lidarDir, {lidarFiles.name}'), Name="OSLidar");

For multi-lidar setups, build one scenariobuilder.LidarData per sensor with a distinct Name (e.g., "OSLidar", "VLP32", "OuterLeft"). DLA will render each in its own pane.

Step 5: Synchronize All Sensors

convertTimestamps(gpsData, "numeric");
convertTimestamps(trackData, "numeric");

timeRef = normalizeTimestamps(gpsData);
normalizeTimestamps(trackData, timeRef);
synchronize(trackData, gpsData);

Step 6: Launch DLA — only when explicitly requested (Rule 0)

`drivingLogAnalyzer` does NOT accept programmatic sensor inputs. It opens the app; the user then imports sensors via the GUI (Import → From Workspace). Make sure the wrapped objects are in the base workspace, then launch the app bare:

% Wrapped objects must already exist in the base workspace
% (gpsData, cameraData, lidarData, trackData)
drivingLogAnalyzer;   % user clicks Import → From Workspace

Do NOT call forms like drivingLogAnalyzer(sensors, Plot=true) or drivingLogAnalyzer(gpsData, Plot=true) — those signatures are not supported and will error. Remember Rule 0: launch DLA only when the user explicitly asks for it or reports a sensor-data problem DLA is built to debug. (See workflow-driving-log-analyzer.md for the full opt-in recipe.)

When No Annotations Exist — Computing Actor Tracks from Lidar

If the dataset has NO pre-computed 3D bounding boxes or temporal tracks, actor tracks must be computed. Always inform the user about:

1. This requires significant processing and tuning
2. Results depend heavily on scene complexity (open highway = good, dense urban = poor)
3. Deep learning detectors (PointPillars, CenterPoint) give much better results than clustering

Option A: Deep Learning Detector (Preferred)

% Requires a trained model (e.g., PointPillars)
detector = pointPillarsObjectDetector(net, pcRange, classNames, anchorBoxes);
bboxes = detect(detector, ptCloud);

Option B: Classical Pipeline (Clustering + Tracking)

% Per frame:
% 1. Transform lidar to ego frame
% 2. Remove ground plane
% 3. Filter ROI
% 4. Euclidean clustering
% 5. Size filtering
% 6. Feed detections to tracker (JPDA or GNN)

Known limitations of clustering approach:

• Dense urban scenes: parked cars, buildings, and road infrastructure form continuous surfaces that cannot be segmented into individual objects
• Mega-clusters: thousands of points merging into single clusters spanning 20+ meters
• False positives: poles, signs, trees, walls pass size filters
• Works best on: highways, open roads with isolated vehicles

Option C: Camera-Based Detection

% Use pretrained camera detector
detector = vehicleDetectorYOLOv2();  % or vehicleDetectorFasterRCNN()
[bboxes, scores] = detect(detector, img);

Limitation: Gives 2D boxes only. Requires depth estimation or lidar fusion for 3D positions.

Projection: Lidar Points → Camera Image

To verify detection alignment or overlay lidar on camera:

% Transform lidar to camera frame
T_lidar2cam = inv(T_cam2ego) * T_lidar2ego;  % compose transforms
pts_cam = (T_lidar2cam * [pts_lidar, ones(N,1)]')';

% Project to pixels (pinhole model)
inFront = pts_cam(:,3) > 0;
u = fx * pts_cam(inFront,1) ./ pts_cam(inFront,3) + cx;
v = fy * pts_cam(inFront,2) ./ pts_cam(inFront,3) + cy;

% Display
imshow(img); hold on;
scatter(u, v, 1, depth, 'filled');

For fisheye cameras: Standard pinhole projection will have errors at image edges. Use the camera's distortion model for accurate projection.

Checklist Before Starting Import

• [ ] What sensors does the dataset provide?
• [ ] Are there pre-computed annotations/3D bounding boxes?
• [ ] Are annotations per-frame (temporal) or keyframe-only?
• [ ] What coordinate frame are positions in?
• [ ] What is the calibration transform naming convention?
• [ ] Does altitude data exist? Will OSM or scene-based roads be used?
• [ ] What is the recording duration? Need to crop?

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