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Found 12 papers, 2 papers with code
Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection
LiDAR-based 3D object detection has become an essential part of automated driving due to its ability to localize and classify objects precisely in 3D.
Group Regression for Query Based Object Detection and Tracking
We show that the proposed method is applicable to many existing transformer based perception approaches and can bring potential benefits.
Sensor Visibility Estimation: Metrics and Methods for Systematic Performance Evaluation and Improvement
We introduce metrics and a framework to assess the performance of visibility estimators.
Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?
Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving.
Transformers for Object Detection in Large Point Clouds
We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture.
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars
We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and radars in different combinations for 3D object detection.
Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks
Labels for the Cartesian velocities or contiguous sequences, which are expensive to obtain, are not required.
Transformers for Multi-Object Tracking on Point Clouds
The model utilizes a cross- and a self-attention mechanism and is applicable to lidar data in an automotive context, as well as other data types, such as radar.
Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar
This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks.
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features
In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach.
DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections
The proposed network exploits the specific characteristics of radar reflection data: It handles unordered lists of arbitrary length as input and it combines both extraction of local and global features.
Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving
Lane detection is an essential part of the perception sub-architecture of any automated driving (AD) or advanced driver assistance system (ADAS).
