首个面向两轮车骑行行为的多模态数据集，助力交通安全研究
MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding
- 采集16名骑手多视角视频、眼动与传感器数据，构建多模态数据集
- 12类骑行动作识别准确率达89.3%，融合视觉+眼动+传感数据表现最佳
- 适合交通安全、智能交通系统研究者使用，推动两轮车安全技术发展
两轮车在南半球地区导致了远超其数量比例的道路死亡事故。然而，针对两轮车骑行行为的研究远落后于四轮车，后者得益于多模态数据集在高级驾驶辅助系统（ADAS）中的突破性进展。为弥补这一差距，我们推出了首个大规模、多视角、多模态的两轮车骑行行为数据集MOTOR。该数据集包含1,629段序列（超过25小时视频），来自16位骑手，整合了同步的前视、后视和头盔摄像头视频、可穿戴设备记录的骑手眼动数据、道路音频以及定位（GPS）、加速度计、陀螺仪等车载传感器数据。数据标注涵盖交通环境、骑手状态、12种骑行动作（包括常规与非常规行为）及合法性标签（合法、非法、未明确）。我们采用先进的视频动作识别骨干网络（基于CNN与Transformer），结合多模态融合进行基准测试，结果表明，融合RGB图像、眼动与遥测数据的模型性能最优。MOTOR为两轮车骑行行为的安全性理解提供了独特基础，向研究社区开放，可用于开发和评估行为分析、合法性感知预测及智能交通系统模型。数据与代码已发布于 https://varuniiith.github.io/MOTOR-Dataset/
原文摘要 · Abstract (English)
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 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. Dataset and code is available at https: //varuniiith.github.io/MOTOR-Dataset/
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