Industry · Automotive & AV

Indian roads need annotation data that Western AV datasets do not have

Autonomous vehicles and ADAS systems trained on Western datasets fail on Indian roads — not because the models are wrong, but because the training data never contained auto-rickshaws, cattle crossings, or monsoon visibility. We annotate the scenarios that matter for Indian roads.

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India-specific road scenarios Western datasets miss entirely

Auto-rickshaws, cattle crossings, unstructured intersections, monsoon visibility, traffic police hand signals — annotated by people who drive these roads, not people who have only seen them in photos.

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LiDAR + camera + video annotation with AI pre-labeling

SAM2 pre-annotation for images, ByteTrack for video object tracking, Open3D for point cloud processing — tooling that reduces annotation labor 50–70% while maintaining centimetre-level accuracy.

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Per-class kappa scores on every AV delivery

Inter-annotator agreement calculated separately per object class — vehicles, pedestrians, two-wheelers, India-specific classes. Published in your data card on every delivery.

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The Challenge

Indian roads are unlike any dataset Western AV companies have

Autonomous vehicles and ADAS systems trained on Western datasets fail on Indian roads — not because the models are wrong, but because the training data does not contain the scenarios that Indian roads present continuously. This is an annotation gap, not a model gap.

India has the world's second-largest road network — 6.37 million kilometres — and some of the world's most complex driving conditions. Mixed traffic with two-wheelers, three-wheelers, pedestrians, cattle, and freight vehicles sharing undemarcated road space. Unstructured intersections without traffic signals. Road conditions ranging from expressways to rural kutcha roads. Monsoon visibility. Night driving without road lighting.

None of these scenarios exist in the nuScenes, KITTI, Waymo Open, or Argoverse datasets that most AV models are benchmarked on. A model trained on Western AV datasets and tested on Indian roads will miss auto-rickshaws at roundabouts, fail to classify cattle as obstacles, and misread hand signals from traffic policemen. These are annotation coverage gaps — the training data simply never included these scenarios.

"Ola Electric, Mahindra, Tata Motors, and every Indian ADAS company building for Indian roads needs annotation data that captures Indian driving scenarios. No Western annotation provider can deliver this authentically. We can."

The AV annotation market is also the most technically demanding in the industry. LiDAR point cloud annotation, multi-camera sensor fusion, temporal consistency across video frames, 3D cuboid labeling with centimetre-level accuracy — these require not just domain knowledge but purpose-built annotation tooling and workflows. We have built both.

India-specific scenarios we annotate

🛺Auto-rickshaws — 3-wheel unique profile, unpredictable lane changes

🐄Cattle on roads — livestock as obstacle class with movement prediction

👮Traffic police hand signals — India-specific direction gestures

🚧Unstructured intersections — no lane markings, mixed right-of-way

🌧️Monsoon conditions — wet roads, spray, reduced visibility, flooded lanes

🏍️Two-wheeler swarms — dense motorcycle traffic between vehicles

📊Every scenario annotated with published kappa on object-class accuracy

Vehicle · 0.97

Vehicle · 0.95

Vehicle · 0.98

Vehicle · 0.96

Vehicle · 0.94

Vehicle · 0.99

Vehicle · 0.92

Two-Wheeler · 0.91

Two-Wheeler · 0.88

Two-Wheeler · 0.93

Auto-Rickshaw · 0.87

Pedestrian · 0.90

Pedestrian · 0.85

Lane Marking · Dashed

Use Cases

Annotation services for Automotive & AV AI

From basic ADAS sensor fusion to full Level 4 autonomous driving perception — every annotation service designed for Indian road conditions.

Use Case 01

2D image annotation for camera perception

Bounding box, polygon, and semantic segmentation annotation for vehicle, pedestrian, cyclist, two-wheeler, three-wheeler, livestock, road infrastructure, and traffic sign detection. SAM2 pre-annotation reduces manual work 50%. Human experts validate and correct all AI-suggested labels. Sub-pixel accuracy on boundary annotation.

Use Case 02

Video annotation with temporal consistency

Multi-frame object tracking with consistent ID assignment across video sequences. ByteTrack-assisted tracking reduces manual frame-by-frame work 70%. Human annotators handle occlusion, ID swaps, and entry/exit events. Action recognition — lane changes, turns, stopping, emergency braking — annotated with temporal segmentation boundaries.

Use Case 03

LiDAR point cloud 3D annotation

3D cuboid labeling on LiDAR point clouds for object detection and distance estimation. Object classes: vehicles, pedestrians, cyclists, motorcycles, auto-rickshaws, trucks, and India-specific obstacle classes. Each cuboid annotated with: object class, tracking ID, occlusion level, truncation status, and activity state. Centimetre-level precision required for ADAS safety systems.

Use Case 04

Sensor fusion annotation (LiDAR + camera)

Synchronized annotation across LiDAR point clouds and camera image frames. Timestamp matching, projection verification, and cross-modal label consistency checking. Critical for perception systems that fuse multiple sensor inputs for depth estimation and object classification — the standard architecture for Level 2+ ADAS systems.

Use Case 05

Lane and road infrastructure annotation

Lane marking type classification (solid, dashed, double, absent), road surface type, speed breaker detection, pothole annotation, road sign recognition, and traffic signal state labeling. India-specific: unmarked road detection, kutcha road classification, and impromptu lane formation annotation for unstructured driving environments.

Use Case 06

Edge case and scenario curation

Systematic collection and annotation of edge cases — rare events that appear infrequently in normal driving data but are critical for safety. Annotators classify severity, create detailed scenario descriptions, and tag relevant failure modes. Indian edge cases: cattle crossings, wedding processions on roads, hand-pushed carts, and informal road blockages.

Technical Capability

The annotation stack behind our AV services

AV annotation requires tooling that most annotation companies do not have. We have built it — using open-source components that we configure, integrate, and operate in-house.

Image Pre-annotation

SAM2 (Meta, open source)

Segment Anything Model 2 pre-draws segmentation boundaries on every image before annotators see it. Reduces annotation time 40–60%. Annotators correct boundaries and assign class labels rather than drawing from scratch. Integrated into Label Studio via ML backend API.

Video Tracking

ByteTrack / DeepSORT

Multi-object tracking algorithm maintains consistent object IDs across video frames automatically. Annotators focus on occlusion handling and ID correction events rather than frame-by-frame labeling. Reduces video annotation labor 60–70% without losing temporal consistency.

3D Point Cloud

Open3D + custom cuboid tool

Open-source 3D point cloud processing library integrated with a custom cuboid annotation interface. Supports .pcd and .bin file formats from all major LiDAR sensors (Velodyne, Ouster, Livox). Annotators work in a browser-based 3D viewer with rotation, zoom, and cross-section tools.

Quality Standard
Per-class kappa on every AV project
Inter-annotator agreement is calculated separately for each object class — vehicle kappa, pedestrian kappa, two-wheeler kappa, India-specific classes. Published in the data card. If any class falls below 0.68, we annotate additional gold standard tasks in that class and recalibrate before continuing.

Sensor Fusion

Timestamp synchronisation pipeline

Python pipeline that aligns LiDAR frames with camera frames using hardware timestamps. Projection verification checks that 3D LiDAR labels are consistent with 2D camera labels. Misalignment flagged for human review before training data export.

Temporal Interpolation

Keyframe + auto-interpolation

Annotators label key frames; the system auto-interpolates labels for intermediate frames using linear or spline interpolation. Human review on all interpolated frames for objects with non-linear motion. 70% reduction in frame-level annotation labor for smooth, predictable object motion.

Market Size

The AV annotation market is the fastest growing segment globally

India's automotive AI opportunity is structurally different from any other market — and it requires annotation capabilities that only an India-native provider can deliver.

35.9%

CAGR of the global AV annotation tools market 2026–2034 — the fastest growing annotation segment

$10B

Projected AV data annotation market by 2034, from $1.3B in 2026

50K hrs

Typical LiDAR annotation hours required for a Level 4 AV program's initial training phase

6.37M km

India's road network — the world's second-largest, with driving conditions no Western dataset covers

Indian roads need annotation data that Western AV datasets do not have

Indian roads are unlike any dataset Western AV companies have

Indian road conditions that Western providers cannot supply

Annotation services for Automotive & AV AI

The annotation stack behind our AV services

The AV annotation market is the fastest growing segment globally

Ready to build better AI for Automotive & AV?