# IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengers

arXiv: https://arxiv.org/abs/2410.02592 · HTML: https://mlsanity.com/paper/2410.02592/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 84 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 30 of 40)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
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- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Learning with Pseudo-Ensembles](https://mlsanity.com/paper/1412.4864/) (2014)
- [FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning](https://mlsanity.com/paper/2205.07246/) (2022)
- [OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations](https://mlsanity.com/paper/1903.08550/) (2019)
- [Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning](https://mlsanity.com/paper/2003.04390/) (2020)
- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities](https://mlsanity.com/paper/2309.16739/) (2023)
- [DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing](https://mlsanity.com/paper/1611.01942/) (2016)
- [BMAD: Benchmarks for Medical Anomaly Detection](https://mlsanity.com/paper/2306.11876/) (2023)
- [Split Learning in 6G Edge Networks](https://mlsanity.com/paper/2306.12194/) (2023)
- [AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks](https://mlsanity.com/paper/2403.13101/) (2024)
- [SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models](https://mlsanity.com/paper/2407.00952/) (2024)
- [Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2404.06448/) (2024)
- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2009.02902/) (2020)
- [AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning](https://mlsanity.com/paper/2404.06345/) (2024)
- [AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception](https://mlsanity.com/paper/2307.13933/) (2023)
- [Optimal Resource Allocation for U-Shaped Parallel Split Learning](https://mlsanity.com/paper/2308.08896/) (2023)
- [AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving](https://mlsanity.com/paper/2302.08646/) (2023)
- [Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities](https://mlsanity.com/paper/2401.01544/) (2024)
- [FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data](https://mlsanity.com/paper/2403.16460/) (2024)
- [AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging](https://mlsanity.com/paper/2408.03624/) (2024)

## Built on by (most cited first, 20 of 20)

- [Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring](https://mlsanity.com/paper/2508.09085/) (2025)
- [State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning](https://mlsanity.com/paper/2503.20613/) (2025)
- [LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems](https://mlsanity.com/paper/2511.19368/) (2025)
- [Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks](https://mlsanity.com/paper/2503.20844/) (2025)
- [Rethinking Membership Inference Attacks Against Transfer Learning](https://mlsanity.com/paper/2501.11577/) (2025)
- [LEED: A Highly Efficient and Scalable LLM-Empowered Expert Demonstrations Framework for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.14680/) (2025)
- [NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learning](https://mlsanity.com/paper/2602.02696/) (2026)
- [Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generation](https://mlsanity.com/paper/2506.10685/) (2025)
- [A Satellite-Ground Synergistic Large Vision-Language Model System for Earth Observation](https://mlsanity.com/paper/2507.05731/) (2025)
- [Sample Efficient Experience Replay in Non-stationary Environments](https://mlsanity.com/paper/2509.15032/) (2025)
- [Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge](https://mlsanity.com/paper/2504.03718/) (2025)
- [SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Framework](https://mlsanity.com/paper/2504.13479/) (2025)
- [t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving](https://mlsanity.com/paper/2410.09747/) (2024)
- [Secure Resource Allocation via Constrained Deep Reinforcement Learning](https://mlsanity.com/paper/2501.11557/) (2025)
- [Intra-DP: A High Performance Collaborative Inference System for Mobile Edge Computing](https://mlsanity.com/paper/2507.05829/) (2025)
- [SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression](https://mlsanity.com/paper/2604.07316/) (2026)
- [Tracing Human Stress from Physiological Signals using UWB Radar](https://mlsanity.com/paper/2410.10155/) (2024)
- [DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection](https://mlsanity.com/paper/2505.06860/) (2025)
- [Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Driving](https://mlsanity.com/paper/2607.10438/) (2026)
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## Abstract (arXiv)

Recently, in-car monitoring has emerged as a promising technology for detecting early-stage abnormal status of the driver and providing timely alerts to prevent traffic accidents. Although training models with multimodal data enhances the reliability of abnormal status detection, the scarcity of labeled data and the imbalance of class distribution impede the extraction of critical abnormal state features, significantly deteriorating training performance. Furthermore, missing modalities due to environment and hardware limitations further exacerbate the challenge of abnormal status identification. More importantly, monitoring abnormal health conditions of passengers, particularly in elderly care, is of paramount importance but remains underexplored. To address these challenges, we introduce our IC3M, an efficient camera-rotation-based multimodal framework for monitoring both driver and passengers in a car. Our IC3M comprises two key modules: an adaptive threshold pseudo-labeling strategy and a missing modality reconstruction. The former customizes pseudo-labeling thresholds for different classes based on the class distribution, generating class-balanced pseudo labels to guide model training effectively, while the latter leverages crossmodality relationships learned from limited labels to accurately recover missing modalities by distribution transferring from available modalities. Extensive experimental results demonstrate that IC3M outperforms state-of-the-art benchmarks in accuracy, precision, and recall while exhibiting superior robustness under limited labeled data and severe missing modality.