# PhysDrive: A Multimodal Remote Physiological Measurement Dataset for In-vehicle Driver Monitoring arXiv: https://arxiv.org/abs/2507.19172 ยท HTML: https://mlsanity.com/paper/2507.19172/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 78 references that are in the map. - First posted on arXiv 25 Jul 2025. ## Builds on (most cited first, 29 of 29) - [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018) - [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020) - [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019) - [PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer](https://mlsanity.com/paper/2111.12082/) (2021) - [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019) - [Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs](https://mlsanity.com/paper/1907.06724/) (2019) - [EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement](https://mlsanity.com/paper/2110.04447/) (2021) - [rPPG-Toolbox: Deep Remote PPG Toolbox](https://mlsanity.com/paper/2210.00716/) (2022) - [MMPD: Multi-Domain Mobile Video Physiology Dataset](https://mlsanity.com/paper/2302.03840/) (2023) - [A Reproducible Study on Remote Heart Rate Measurement](https://mlsanity.com/paper/1709.00962/) (2017) - [PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer](https://mlsanity.com/paper/2302.03548/) (2023) - [Non-Contrastive Unsupervised Learning of Physiological Signals from Video](https://mlsanity.com/paper/2303.07944/) (2023) - [Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast](https://mlsanity.com/paper/2309.06924/) (2023) - [SCAMPS: Synthetics for Camera Measurement of Physiological Signals](https://mlsanity.com/paper/2206.04197/) (2022) - [Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer](https://mlsanity.com/paper/2402.04798/) (2024) - [FactorizePhys: Matrix Factorization for Multidimensional Attention in Remote Physiological Sensing](https://mlsanity.com/paper/2411.01542/) (2024) - [BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements](https://mlsanity.com/paper/2303.11573/) (2023) - [MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar](https://mlsanity.com/paper/2111.08195/) (2021) - [PhysMLE: Generalizable and Priors-Inclusive Multi-task Remote Physiological Measurement](https://mlsanity.com/paper/2405.06201/) (2024) - [Camera-Based HRV Prediction for Remote Learning Environments](https://mlsanity.com/paper/2305.04161/) (2023) - [Efficient Mixture-of-Expert for Video-based Driver State and Physiological Multi-task Estimation in Conditional Autonomous Driving](https://mlsanity.com/paper/2410.21086/) (2024) - [Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes](https://mlsanity.com/paper/2409.19223/) (2024) - [Align the GAP: Prior-based Unified Multi-Task Remote Physiological Measurement Framework For Domain Generalization and Personalization](https://mlsanity.com/paper/2506.16160/) (2025) - [Camera Measurement of Blood Oxygen Saturation](https://mlsanity.com/paper/2503.01699/) (2025) - [Non-Contact Health Monitoring During Daily Personal Care Routines](https://mlsanity.com/paper/2506.09718/) (2025) - [Not Only Consistency: Enhance Test-Time Adaptation with Spatio-temporal Inconsistency for Remote Physiological Measurement](https://mlsanity.com/paper/2507.07908/) (2025) - [Memory-efficient Low-latency Remote Photoplethysmography through Temporal-Spatial State Space Duality](https://mlsanity.com/paper/2504.01774/) (2025) - [Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study](https://mlsanity.com/paper/2506.06360/) (2025) - [Comprehensive mm-Wave FMCW Radar Dataset for Vital Sign Monitoring: Embracing Extreme Physiological Scenarios](https://mlsanity.com/paper/2405.12659/) (2024) ## Built on by (most cited first, 7 of 7) - [PhysNeXt: Next-Generation Dual-Branch Structured Attention Fusion Network for Remote Photoplethysmography Measurement](https://mlsanity.com/paper/2603.19752/) (2026) - [M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings](https://mlsanity.com/paper/2511.02349/) (2025) - [Optimize-at-Capture: Highly-adaptive Exposure Controlling for In-Vehicle Non-contact Heart-rate Monitoring](https://mlsanity.com/paper/2605.04397/) (2026) - [RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement](https://mlsanity.com/paper/2606.28089/) (2026) - [FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements](https://mlsanity.com/paper/2510.12132/) (2025) - [SVC 2026: the Second Multimodal Deception Detection Challenge and the First Domain Generalized Remote Physiological Measurement Challenge](https://mlsanity.com/paper/2604.05748/) (2026) - [Why Do VLMs Fail at Remote Physiological Measurement? Rethinking with Physiological Grounding and Numerical Alignment](https://mlsanity.com/paper/2610.19052/) (2026) ## Abstract (arXiv) Robust and unobtrusive in-vehicle physiological monitoring is crucial for ensuring driving safety and user experience. While remote physiological measurement (RPM) offers a promising non-invasive solution, its translation to real-world driving scenarios is critically constrained by the scarcity of comprehensive datasets. Existing resources are often limited in scale, modality diversity, the breadth of biometric annotations, and the range of captured conditions, thereby omitting inherent real-world challenges in driving. Here, we present PhysDrive, the first large-scale multimodal dataset for contactless in-vehicle physiological sensing with dedicated consideration on various modality settings and driving factors. PhysDrive collects data from 48 drivers, including synchronized RGB, near-infrared camera, and raw mmWave radar data, accompanied with six synchronized ground truths (ECG, BVP, Respiration, HR, RR, and SpO2). It covers a wide spectrum of naturalistic driving conditions, including driver motions, dynamic natural light, vehicle types, and road conditions. We extensively evaluate both signal-processing and deep-learning methods on PhysDrive, establishing a comprehensive benchmark across all modalities, and release full open-source code with compatibility for mainstream public toolboxes. We envision PhysDrive will serve as a foundational resource and accelerate research on multimodal driver monitoring and smart-cockpit systems.