Autonomous Vehicle Data: Annotation Challenges for Indian Road Conditions
Waymo and nuScenes were built for American highways. Indian roads are a completely different challenge.
Why Western Driving Datasets Fail on Indian Roads
Standard AV datasets (KITTI, nuScenes, Waymo Open Dataset) reflect Western driving conditions: well-marked lanes, predictable vehicle types, structured intersections. Indian roads are structurally different.
India-Specific Vehicle Classes: Auto-Rickshaw, Cycle-Rickshaw, Hand-Pulled Cart
Western vehicle ontologies don't include auto-rickshaws, cycle-rickshaws, hand-pulled carts, bullock carts, or overloaded two-wheelers. Without training data for these classes, AV models are blind to a large proportion of Indian road users.
Mixed Traffic Challenges: Two-Wheelers, Pedestrians, Animals
Indian road scenes regularly include two-wheelers overtaking on both sides, pedestrians crossing mid-road, stray animals (cattle, dogs), and manual workers on highway construction zones — all absent from standard AV datasets.
Road Surface Annotation: Unmarked Roads, Construction Zones, Potholes
Lane detection models trained on well-marked Western roads fail on unmarked rural roads, construction zones with temporary lane markings, and pothole-dense urban streets.
Weather and Lighting Variations: Monsoon, Dust, Night Driving
Monsoon rain, dust storms, and the absence of street lighting in rural areas create sensor data challenges that require specific training data and annotation protocols.
Annotation Standards: COCO JSON, KITTI, Custom Schema for Indian Data
We support standard AV annotation formats (KITTI, nuScenes schema, COCO) while extending class taxonomies to include India-specific object categories.
Case Study: 200,000 Frames Annotated for an AV Company by eQOURSE
We annotated 200,000 dashcam frames across 8 Indian cities, with a 42-class object taxonomy including all major Indian road user types, achieving 97% bounding box accuracy.
Build India-specific AV datasets with eQOURSE