# DeepSignals: Predicting Intent of Drivers Through Visual Signals

## TL;DR

DeepSignals introduces a deep neural network approach to accurately detect driver intentions through visual signals like turn signals and flashers in video sequences, enhancing autonomous vehicle responsiveness.

## Contribution

The paper presents a novel deep learning method that combines spatial and temporal reasoning to detect driver signals in challenging video scenarios.

## Key findings

- High per-frame accuracy achieved on over a million frames.
- Effective detection of turn signals and emergency flashers in complex conditions.
- Demonstrates potential for improving autonomous driving safety.

## Abstract

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.