Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping

TL;DR

This paper presents a hybrid deep learning architecture that improves radar-based dynamic occupancy grid mapping for autonomous vehicles by enhancing object detection accuracy through neural network-based state correction.

Contribution

It introduces a neural network-based state correction mechanism and a heuristic fusion approach to significantly improve dynamic object detection in radar-based DOGM.

Findings

Enhanced detection of dynamic objects

Improved grid accuracy on NuScenes Dataset

Effective deep learning-based state correction

Abstract

This paper introduces a novel hybrid architecture that enhances radar-based Dynamic Occupancy Grid Mapping (DOGM) for autonomous vehicles, integrating deep learning for state-classification. Traditional radar-based DOGM often faces challenges in accurately distinguishing between static and dynamic objects. Our approach addresses this limitation by introducing a neural network-based DOGM state correction mechanism, designed as a semantic segmentation task, to refine the accuracy of the occupancy grid. Additionally a heuristic fusion approach is proposed which allows to enhance performance without compromising on safety. We extensively evaluate this hybrid architecture on the NuScenes Dataset, focusing on its ability to improve dynamic object detection as well grid quality. The results show clear improvements in the detection capabilities of dynamic objects, highlighting the effectiveness of the deep learning-enhanced state correction in radar-based DOGM.