# Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning

## TL;DR

This paper introduces a neural network-based framework that predicts future occupancy states in urban environments for autonomous driving, enhancing trajectory planning by modeling dynamic agents' behavior using recurrent representation learning.

## Contribution

It presents a novel recurrent neural network architecture leveraging occupancy grid data to improve environment prediction accuracy in urban scenes.

## Key findings

- Higher accuracy than baseline methods
- Effective prediction of dynamic agents' future states
- Validated on KITTI dataset

## Abstract

A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles. A reliable prediction of the future environment, including the behavior of dynamic agents, would allow planning algorithms to proactively generate a trajectory in response to a rapidly changing environment. We present a novel framework that predicts the future occupancy state of the local environment surrounding an autonomous agent by learning a motion model from occupancy grid data using a neural network. We take advantage of the temporal structure of the grid data by utilizing a convolutional long-short term memory network in the form of the PredNet architecture. This method is validated on the KITTI dataset and demonstrates higher accuracy and better predictive power than baseline methods.

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Source: https://tomesphere.com/paper/1904.12374