Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps
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.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionOriented Object DetectionSimilar Papers 제목 키워드 기반
AsyncBEV: Cross-modal Flow Alignment in Asynchronous 3D Object Detection
In autonomous driving, multi-modal perception tasks like 3D object detection typically rely on well-synchronized sensors, both at training and inference. However, despite the use of hardware- or software-based synchroniz…
Scene Flow Estimation3D Object DetectionAutonomous DrivingLSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes
Loop Closure Detection (LCD) is an essential component of visual simultaneous localization and mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts ari…
Loop Closure DetectionRetrievalSimultaneous Localization and MappingRadar-based Dynamic Occupancy Grid Mapping and Object Detection
Environment modeling utilizing sensor data fusion and object tracking is crucial for safe automated driving. In recent years, the classical occupancy grid map approach, which assumes a static environment, has been extend…
ClusteringObjectobject-detectionObject Detection+1Free Space Estimation using Occupancy Grids and Dynamic Object Detection
In this paper we present an approach to estimate Free Space from a Stereo image pair using stochastic occupancy grids. We do this in the domain of autonomous driving on the famous benchmark dataset KITTI. Later based on …
Autonomous Drivingobject-detectionObject DetectionIntegrating Specialized and Generic Agent Motion Prediction with Dynamic Occupancy Grid Maps
Accurate prediction of driving scene is a challenging task due to uncertainty in sensor data, the complex behaviors of agents, and the possibility of multiple feasible futures. Existing prediction methods using occupancy…
Motion Forecasting