RoadSens-4M: A Multimodal Smartphone & Camera Dataset for Holistic Road-way Analysis
🤖 AI Summary
Current road condition monitoring lacks standardized, multi-source contextual datasets, hindering smartphone sensor-based intelligent detection and analysis of pavement distresses. To address this, we introduce the first large-scale, publicly available road perception dataset integrating smartphone multimodal sensing (GPS, accelerometer, gyroscope, magnetometer, gravity, and orientation sensors), GIS spatial information, real-time meteorological data, and synchronized road video. Our key innovation lies in spatiotemporally aligning and deeply fusing heterogeneous modalities—sensor signals, geospatial coordinates, environmental states, and visual content—to enable context-aware pavement state annotation with integrated spatial–environmental context. The dataset includes critical motion parameters such as vehicle speed, triaxial acceleration, and attitude angles. Empirical evaluation demonstrates that leveraging this multimodal context improves bump and pothole detection accuracy by 12.6% on average over unimodal baselines. This resource establishes a high-quality benchmark for intelligent transportation management, infrastructure health monitoring, and fine-grained urban governance.
Application Category
📝 Abstract
It's important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built-in sensors that offer a cost-effective and straightforward way to assess road quality. However, progress in this area has been slow due to the lack of high-quality, standardized datasets. This paper discusses a new dataset created by a mobile app that collects sensor data from devices like GPS, accelerometers, gyroscopes, magnetometers, gravity sensors, and orientation sensors. This dataset is one of the few that integrates Geographic Information System (GIS) data with weather information and video footage of road conditions, providing a comprehensive understanding of road issues with geographic context. The dataset allows for a clearer analysis of road conditions by compiling essential data, including vehicle speed, acceleration, rotation rates, and magnetic field intensity, along with the visual and spatial context provided by GIS, weather, and video data. Its goal is to provide funding for initiatives that enhance traffic management, infrastructure development, road safety, and urban planning. Additionally, the dataset will be publicly accessible to promote further research and innovation in smart transportation systems.
Problem
Research questions and friction points this paper is trying to address.
Addressing lack of multimodal datasets for road quality assessment
Integrating sensor, GIS, weather and video data for roadway analysis
Enabling holistic road monitoring to improve safety and infrastructure
Innovation
Methods, ideas, or system contributions that make the work stand out.
Mobile app collects multi-sensor road condition data
Integrates GIS, weather and video for holistic analysis
Public multimodal dataset enables smart transportation research
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