# Real-Time In-Cabin Driver Behavior Recognition on Low-Cost Edge Hardware

arXiv: https://arxiv.org/abs/2512.22298 · HTML: https://mlsanity.com/paper/2512.22298/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 26 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [Real-time Distracted Driver Posture Classification](https://mlsanity.com/paper/1706.09498/) (2017)
- [Security Risks of Agentic Vehicles: A Systematic Analysis of Cognitive and Cross-Layer Threats](https://mlsanity.com/paper/2512.17041/) (2025)
- [Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework](https://mlsanity.com/paper/1910.09722/) (2019)
- [Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection](https://mlsanity.com/paper/2010.06235/) (2020)
- [Long-term Multi-granularity Deep Framework for Driver Drowsiness Detection](https://mlsanity.com/paper/1801.02325/) (2018)
- [Real-time Driver Monitoring Systems on Edge AI Device](https://mlsanity.com/paper/2304.01555/) (2023)

## Abstract (arXiv)

In-cabin driver monitoring systems (DMS) must recognize distraction- and drowsiness-related behaviors with low latency under strict constraints on compute, power, and cost. We present a single-camera in-cabin driver behavior recognition system designed for deployment on two low-cost edge platforms: Raspberry Pi 5 (CPU-only) and the Google Coral development board with an Edge Tensor Processing Unit (Edge TPU) accelerator. The proposed pipeline combines (i) a compact per-frame vision model, (ii) a confounder-aware label taxonomy to reduce confusions among visually similar behaviors, and (iii) a temporal decision head that triggers alerts only when predictions are both confident and sustained. The system supports 17 behavior classes. Training and evaluation use licensed datasets plus in-house collection (over 800,000 labeled frames) with driver-disjoint splits, and we further validate the deployed system in live in-vehicle tests. End-to-end performance reaches approximately 16 FPS on Raspberry Pi 5 using 8-bit integer (INT8) inference (per-frame latency <60 ms) and approximately 25 FPS on Coral Edge TPU (end-to-end latency ~40 ms), enabling real-time monitoring and stable alert generation on embedded hardware. Finally, we discuss how reliable in-cabin perception can serve as an upstream signal for human-centered vehicle intelligence, including emerging agentic vehicle concepts.