# Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

## TL;DR

This paper presents a deep learning approach for long-term dynamic occupancy grid prediction in urban autonomous driving, utilizing automatic labeling and Bayesian filtering to improve environment understanding and interaction modeling.

## Contribution

It introduces a fully automatic labeling method combined with a deep CNN for long-term prediction of complex urban scenarios, addressing sensor data imbalance and interaction modeling.

## Key findings

- The method achieves accurate long-term predictions in urban environments.
- Automatic label generation enables unsupervised training.
- The approach outperforms Monte-Carlo simulation in evaluations.

## Abstract

Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation.