# Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps

Source: [Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps](<https://www.sota2.com/research/paper/68fa75541e209131cea6737e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:32:00.805Z

Authors: Rujiao Yan, Linda Schubert, Alexander Kamm, Matthias Komar, Matthias Schreier

Published: 2024-10-18T18:15:32.000Z

## Abstract

This paper describes a method to detect generic dynamic objects for automated driving. First, a LiDAR-based dynamic grid is generated online. Second, a deep learning-based detector is trained on the dynamic grid to infer the presence of dynamic objects of any type, which is a prerequisite for safe automated vehicles in arbitrary, edge-case scenarios. The Rotation-equivariant Detector (ReDet) - originally designed for oriented object detection on aerial images - was chosen due to its high detection performance. Experiments are conducted based on real sensor data and the benefits in comparison to classic dynamic cell clustering strategies are highlighted. The false positive object detection rate is strongly reduced by the proposed approach.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2410.14799>)

[PDF](<https://arxiv.org/pdf/2410.14799.pdf>)