Automotive Data Annotation for ADAS & Self-Driving AI

We annotate images, video, LiDAR point clouds, radar data and sensor-fusion datasets. Common labeled classes include vehicles, pedestrians, traffic signs, lanes, road edges and other objects in the driving environment. The resulting ground truth supports perception and navigation work from Level 2 ADAS through Level 4 autonomy. We also support bird's-eye-view data, and our wider AI data labeling services cover other industries.

Automotive datasets commonly use bounding boxes, polygons, polylines, semantic, instance and panoptic segmentation, 3D cuboids and LiDAR point cloud annotation. Each suits a different job: object detection, lane tracking, depth estimation or scene understanding. Which one you need depends on your sensors and your model, and the comparison table above covers the usual choices.

We label LiDAR point clouds with 3D cuboids and semantic segmentation, giving perception models object distance, volume and spatial context. Our annotators are trained specifically for 3D sensor data and work to tight IoU thresholds. Outputs are delivered in KITTI, JSON, PCD or custom formats that match your perception stack.

Sensor fusion annotation aligns labels across camera, LiDAR and radar so perception models receive spatially consistent data. Our annotators cross-reference the sensor feeds and give each object the same label and ID in every input. This matters for ADAS, where no single sensor is enough for safe decision-making.

Annotated datasets are delivered in COCO, KITTI, JSON, XML, YOLO, Pascal VOC, CSV, OpenLABEL or client-specified schemas, so they load directly into most ML pipelines, simulation tools and evaluation frameworks.

We scale by adding trained annotators to your project and keeping the same guidelines and QA across every batch. We support continuous data uploads, phased delivery and evolving dataset requirements.

Yes. Our workflows are ISO 27001-aligned and GDPR-aligned. All annotators sign NDAs before project access, roles are permission-scoped to prevent data exposure, and automated security monitoring runs across all project environments. If your security team needs more detail, we can walk through our controls and complete your vendor questionnaire before the pilot starts.

Pricing depends on data type, annotation complexity, volume and turnaround. We price per image, per frame, per hour or as a monthly retainer, and because our team is based in India, rates are typically well below US and UK providers. Send us your dataset details and we will quote against your volume and annotation requirements.

Yes. Our annotators work inside your internal tooling or any third-party platform, including Scale AI, Labelbox, CVAT, Roboflow, SuperAnnotate and V7. We adapt to your workflow rather than asking you to switch platforms.

Our annotators are trained specifically for LiDAR, 3D cuboid, ADAS and sensor fusion work rather than general-purpose tasks, and every batch goes through multi-pass QA and expert review. We are based in Pune, have labeled data for AI teams since 2017, work on any platform and offer white-label capacity for other AI vendors and BPOs.