Perception AI models for autonomous vehicles, robots, and ADAS systems learn from labeled sensor data. The labels teach the model what it is looking at, where objects are in 3D space, and how they move over time. Sensor fusion data annotation covers a range of specific labeling tasks  each serving a different purpose in model training. This post breaks down the main task types, explains what each involves, and describes when each one is used.

What Annotation Tasks Are Involved in Sensor Fusion?

Sensor fusion annotation includes tasks across camera, LiDAR, radar, and IMU data. Some tasks label a single modality. Others require the same object to be labeled across multiple sensor streams in alignment. The most common task types are 3D bounding box annotation, point cloud segmentation, object tracking, radar target labeling, free-space annotation, lane and HD map annotation, and keypoint or pose annotation.

3D Bounding Box Annotation in LiDAR Data

A 3D bounding box is a cuboid placed around an object in a LiDAR point cloud. It captures the object's position, dimensions, and orientation in three-dimensional space. Annotators adjust the box to fit tightly around the cluster of points that represent the object  a vehicle, pedestrian, cyclist, or obstacle.

Each box is assigned a class label and a heading angle that shows which direction the object is facing. In multi-frame sequences, the same object receives a track ID so the model can learn object continuity across time. 3D bounding box annotation is the most widely used task in autonomous driving and ADAS datasets. It trains detection and localisation models that need to know where objects are and how far away they are.

Accuracy in this task is measured by 3D Intersection over Union (IoU)  the overlap between the annotator's box and the reference box. Production datasets typically require a mean 3D IoU above 0.80 across all annotations before the data is accepted for training.

Point Cloud Semantic Segmentation

Semantic segmentation labels every point in a LiDAR scan with a class road surface, building, vegetation, vehicle, pedestrian, sky, and so on. Every point receives a label. No points are left unlabeled.

This task is more detailed than 3D bounding boxes. It trains models that need a complete understanding of the scene not just where specific objects are, but what every part of the environment is. Autonomous path planning systems use segmentation to understand drivable space, detect road edges, and identify surface types. The task is labour-intensive because each scan contains millions of points, and labels must be consistent across adjacent frames.

Instance segmentation goes one step further  it distinguishes between individual objects of the same class. Two adjacent parked cars are labeled as two separate instances, not one continuous vehicle region. This is required for models that need to count objects or track individual items through a scene.

How Does Radar Annotation Differ from LiDAR Annotation?

Radar produces a sparse grid of returns range and velocity measurements for detected objects — rather than the dense point clouds that LiDAR generates. Radar annotation labels each return cluster with a class, a velocity vector, and a target ID across frames.

Radar Target Classification and Velocity Labeling

Each radar return cluster represents a detected object in the sensor's field of view. Annotators classify the cluster — vehicle, pedestrian, stationary object, clutter  and assign the measured velocity vector a direction and magnitude label. For multi-frame sequences, a track ID connects the same object across consecutive radar scans.

Radar annotation is less spatially detailed than LiDAR annotation, but the velocity information is more precise. A pedestrian at 50 metres in heavy rain may be invisible to the camera and produce only a sparse LiDAR return, but the radar return will still provide velocity data. Labeling that return correctly is what trains the model to trust radar when other sensors degrade.

Cross-Modal Fusion: Linking Radar Returns to Camera and LiDAR Labels

In a fusion annotation workflow, radar-detected objects are matched to their camera bounding boxes and LiDAR cuboids. The same pedestrian is labeled in all three modalities with the same track ID and class. This linking teaches the model to associate what the radar detects with what the camera and LiDAR see  so that in production, when the camera and LiDAR data are degraded, the model knows the radar return is the same object it has seen before. For a detailed explanation of how different sensors contribute complementary data in fusion systems and why no single sensor is sufficient on its own, this sensor fusion overview covers the architecture, sensor roles, and real-world applications clearly.

What Is Object Tracking Annotation in Sensor Fusion?

Object tracking annotation assigns a consistent track ID to each object across every frame in a sequence. The same vehicle, pedestrian, or cyclist keeps the same ID from the frame it first appears in to the frame it leaves the scene. Annotators maintain this ID even when the object is partially occluded, briefly blocked by another object, or at the edge of a sensor's detection range.

Why Track IDs Matter for Perception Model Training

Track IDs teach the model to understand object continuity. A detection model identifies what is in each frame. A tracking model understands that the vehicle detected in frame 47 is the same vehicle detected in frames 45, 46, 48, and 49. This continuity is what enables trajectory prediction  knowing where a moving object will be in the next second based on where it has been in the past.

Track ID Consistency Across Modalities

In fusion datasets, track IDs must be consistent across all modalities in the same frame. The pedestrian labeled with track ID 12 in the LiDAR point cloud must also be labeled with track ID 12 in the camera bounding box and the radar return. A mismatch  where the same person has different IDs in different sensor streams — produces conflicting training signal that degrades the model's ability to associate detections across its sensor inputs.

How Do Annotators Label Free Space and Lanes in Sensor Fusion Data?

Free-space annotation marks the area in a scene where a vehicle or robot can safely travel. In camera data, annotators draw polygon boundaries around the drivable surface. In LiDAR data, they label the road surface points as traversable. In fusion annotation, both labels are created in alignment so the model learns a consistent definition of drivable space across sensor modalities.

Lane Annotation for ADAS and HD Maps

Lane annotation labels lane markings  solid lines, dashed lines, double lines, arrows  in camera images and projects them into the corresponding LiDAR and map coordinate frames. ADAS systems use this data to train lane-keeping and lane-departure models. HD map annotation extends this to full map coverage — labeling road geometry, signs, traffic lights, crosswalks, and road boundaries at a level of detail that supports centimetre-accurate localisation.

Pedestrian Keypoint and Pose Annotation

Keypoint annotation marks specific body joints  head, shoulders, elbows, wrists, hips, knees, ankles  on each detected person in the scene. In sensor fusion datasets, keypoints are annotated in camera images and matched to the 3D pose in the LiDAR coordinate frame. This trains models for pedestrian intent prediction  understanding whether a person is walking, standing still, about to cross a road, or reaching for something  based on body pose and movement patterns across frames.

Conclusion

Sensor fusion annotation is a collection of specific, technical labeling tasks  each producing a different type of ground truth for a different model training objective. Understanding what each task involves helps teams plan annotation programs more accurately: which tasks the use case requires, what annotator skills those tasks demand, how quality is measured for each, and how cross-modal consistency must be verified before the data enters a training pipeline. The right annotation task mix depends on the AI system being trained and the sensors it uses  not on general best practices that apply equally to all perception programs.