Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction

TL;DR

This paper introduces an end-to-end model that predicts pedestrian crossing behavior using future video frame prediction, enhancing autonomous vehicle safety by anticipating pedestrian actions.

Contribution

The work presents a novel two-stage deep learning model combining future frame prediction with pedestrian crossing behavior forecasting, achieving state-of-the-art results.

Findings

State-of-the-art accuracy on pedestrian crossing prediction

Effective integration of future frame prediction with behavior forecasting

Improved safety in autonomous driving scenarios

Abstract

In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective of the vehicle, to predict if a pedestrian will cross in front of the vehicle. The long term goal of this work is to design a fully autonomous system that acts and reacts as a defensive human driver would --- predicting future events and reacting to mitigate risk. We focus on pedestrian-vehicle interactions because of the high risk of harm to the pedestrian if their actions are miss-predicted. Our end-to-end model consists of two stages: the first stage is an encoder/decoder network that learns to predict future video frames. The second stage is a deep spatio-temporal network that utilizes the predicted frames of the first stage to predict the pedestrian's future action. Our system achieves state-of-the-art accuracy on pedestrian behavior prediction and future frames prediction on the Joint Attention for Autonomous Driving (JAAD) dataset.