# DeepSignals: Predicting Intent of Drivers Through Visual Signals

arXiv: https://arxiv.org/abs/1905.01333 · HTML: https://mlsanity.com/paper/1905.01333/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2019.

## Built on by (most cited first, 3 of 3)

- [ADAPT: Action-aware Driving Caption Transformer](https://mlsanity.com/paper/2302.00673/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads](https://mlsanity.com/paper/2109.05226/) (2021)

## Abstract (arXiv)

Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. Our experiments on more than a million frames show high per-frame accuracy in very challenging scenarios.