# Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring

arXiv: https://arxiv.org/abs/2006.11557 · HTML: https://mlsanity.com/paper/2006.11557/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception](https://mlsanity.com/paper/2504.02264/) (2025)
- [CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers](https://mlsanity.com/paper/2305.07840/) (2023)
- [Learning Road Scene-level Representations via Semantic Region Prediction](https://mlsanity.com/paper/2301.00714/) (2023)
- [Artificial Intelligence Methods in In-Cabin Use Cases: A Survey](https://mlsanity.com/paper/2101.02082/) (2021)
- [Where and What: Driver Attention-based Object Detection](https://mlsanity.com/paper/2204.12150/) (2022)
- [EraW-Net: Enhance-Refine-Align W-Net for Scene-Associated Driver Attention Estimation](https://mlsanity.com/paper/2408.08570/) (2024)
- [Last Layer Hamiltonian Monte Carlo](https://mlsanity.com/paper/2507.08905/) (2025)
- [CaTFormer: Causal Temporal Transformer with Dynamic Contextual Fusion for Driving Intention Prediction](https://mlsanity.com/paper/2507.13425/) (2025)
- [Predicting Driver Intention Using Deep Neural Network](https://mlsanity.com/paper/2105.14790/) (2021)
- [Designing deep neural networks for driver intention recognition](https://mlsanity.com/paper/2402.05150/) (2024)
- [Latent Uncertainty Representations for Video-based Driver Action and Intention Recognition](https://mlsanity.com/paper/2510.05006/) (2025)
- [Towards Safer and Understandable Driver Intention Prediction](https://mlsanity.com/paper/2510.09200/) (2025)

## Abstract (arXiv)

Numerous car accidents are caused by improper driving maneuvers. Serious injuries are however avoidable if such driving maneuvers are detected beforehand and the driver is assisted accordingly. In fact, various recent research has focused on the automated prediction of driving maneuver based on hand-crafted features extracted mainly from in-cabin driver videos. Since the outside view from the traffic scene may also contain informative features for driving maneuver prediction, we present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. More specifically, we (1) propose a Convolutional-LSTM (ConvLSTM)-based auto-encoder to extract motion features from the out-cabin traffic, (2) train a classifier which considers motions from both in- and outside of the cabin jointly for maneuver intention anticipation, (3) experimentally prove that the in- and outside image features have complementary information. Our evaluation based on the publicly available dataset Brain4cars shows that our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.