MOTOR — A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding

Project Page | Paper | Code

MOTOR is the first large-scale, multi-view, multimodal resource dedicated to two-wheelers in dense, unstructured traffic. It comprises 1,629 annotated sequences (25+ hours of video data) collected from 16 riders and integrates synchronized front, rear, and helmet videos, rider eye-gaze from wearable trackers, on-road audio, and telemetry (GPS, accelerometer, gyroscope).

Sample Usage

To use this dataset with the official codebase, follow these steps:

Setup

conda create -n motor python=3.10 -y
conda activate motor
pip install -r requirements.txt

Training

The following command trains a three-stream fusion model (RGB + gaze + telemetry) on a Swin3D-T backbone:

python train.py --config configs/swin3dt_gaze_tele.yaml

Structure

annotations.csv                              # one row per clip
clips/<ride>/front/<clip>.mp4                # front-facing road view
clips/<ride>/helmet/<clip>.mp4               # helmet-mounted view
clips/<ride>/rear/<clip>.mp4                 # rear-facing view
clips/<ride>/eye_tracker/<clip>.mp4          # rider's first-person gaze view
clips/<ride>/telemetry/<clip>.csv            # per-frame gyro + GPS for the clip

Faces and license plates in all camera videos are blurred for privacy. Two rides (14, 15) do not include the eye-tracker modality. One ride (12) has GPS-only telemetry.

Annotation columns

Video ID, Clip ID, Timestamp, Road Type, No. of Lanes, Road Marking, Divider, Traffic Density, Event, Head Pose Direction, Legality

• Event: going straight, lane change, overtake, left/right turn, u-turn, weaving through traffic, obstruction avoidance, distracted driving, stop, violation, near collision
• Legality: legal / illegal — based on Indian Motor Vehicle Act and local conventions

Per-clip telemetry CSV schema

frame, timestamp_s, gyro_x, gyro_y, gyro_z, gps_lat, gps_lon, gps_alt_m, speed_2d_mps, speed_3d_mps

Resampled to 30 Hz; timestamp_s is clip-local.

Citation

If you find this work useful, please cite:

@inproceedings{paturkar2026motor,
  title={MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding},
  author={Paturkar, Varun and others},
  booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  year={2026}
}

License

Released under CC BY-NC 4.0 — non-commercial research use only.

Note

This release contains the annotated clip-level data. The full uncut raw rides (long-form multi-camera recordings together with their continuous telemetry) will be made publicly available soon.