mlsanityData to 7 Oct 2026

Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps

arXiv 2410.14799 ↗First posted 18 Oct 2024cs.CV cs.AIcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

13 of 21 references.

References · 13

  1. 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 622
  2. 2019Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challengescited by 313
  3. 2021ReDet: A Rotation-equivariant Detector for Aerial Object Detectioncited by 202
  4. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  5. 2022MMRotate: A Rotated Object Detection Benchmark using PyTorchcited by 115
  6. 2016A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Applicationcited by 17
  7. 2022A Multi-Task Recurrent Neural Network for End-to-End Dynamic Occupancy Grid Mappingcited by 5
  8. 2018Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNscited by 4
  9. 2017Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Mapscited by 2
  10. 2018Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generationcited by 2
  11. 2018Environment Perception Framework Fusing Multi-Object Tracking, Dynamic Occupancy Grid Maps and Digital Mapscited by 2
  12. 2020Single-Stage Object Detection from Top-View Grid Maps on Custom Sensor Setupscited by 2
1 more
  1. 2023Deep Radar Inverse Sensor Models for Dynamic Occupancy Grid Mapscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Studycited by 0