Update README.md

README.md

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 pretty_name: MOTOR
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 - 1K<n<10K
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+---
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+# MOTOR — A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding
+
+**Paper:** *MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding*, ICRA 2026.
+
+## Abstract
+
+Two-wheelers account for a disproportionately high share of road fatalities in the Global South. Research on two-wheeler rider behavior, however, lags far behind four-wheelers, where multimodal datasets have driven major advances in Advanced Driver Assistance Systems (ADAS). To address this gap, we present the MOtorized TwO-wheeler Rider (MOTOR) dataset, the first large-scale, multi-view, multimodal resource dedicated to two-wheelers in dense, unstructured traffic. MOTOR 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). Rich annotations capture traffic context, rider state, 12 riding maneuvers spanning conventional and unconventional behaviors, and legality labels (Legal, Illegal, Unspecified). We benchmark rider behavior recognition and maneuver legality classification using state-of-the-art video action recognition backbones (CNN and Transformer-based), extended with multimodal fusion, and find that combining RGB, gaze, and telemetry consistently yields the best performance. MOTOR thus provides a unique foundation for advancing safety-critical understanding of two-wheeler riding. It offers the research community a benchmark to develop and evaluate models for behavior analysis, legality-aware prediction, and intelligent transportation systems.
+
+## 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.
+
+## 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.