# Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding

arXiv: https://arxiv.org/abs/1901.05203 · HTML: https://mlsanity.com/paper/1901.05203/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [Deep Occupancy-Predictive Representations for Autonomous Driving](https://mlsanity.com/paper/2303.04218/) (2023)
- [Inverse RL Scene Dynamics Learning for Nonlinear Predictive Control in Autonomous Vehicles](https://mlsanity.com/paper/2504.01336/) (2025)

## Abstract (arXiv)

Grid maps obtained from fused sensory information are nowadays among the most popular approaches for motion planning for autonomous driving cars. In this paper, we introduce Deep Grid Net (DGN), a deep learning (DL) system designed for understanding the context in which an autonomous car is driving. DGN incorporates a learned driving environment representation based on Occupancy Grids (OG) obtained from raw Lidar data and constructed on top of the Dempster-Shafer (DS) theory. The predicted driving context is further used for switching between different driving strategies implemented within EB robinos, Elektrobit's Autonomous Driving (AD) software platform. Based on genetic algorithms (GAs), we also propose a neuroevolutionary approach for learning the tuning hyperparameters of DGN. The performance of the proposed deep network has been evaluated against similar competing driving context estimation classifiers.