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Found 16 papers, 8 papers with code
Label-Efficient 3D Object Detection For Road-Side Units
We address this challenge by devising a label-efficient object detection method for RSU based on unsupervised object discovery.
OccFusion: A Straightforward and Effective Multi-Sensor Fusion Framework for 3D Occupancy Prediction
This paper introduces OccFusion, a straightforward and efficient sensor fusion framework for predicting 3D occupancy.
InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction
In contrast, our approach leverages two projection matrices to store the static mapping relationships and matrix multiplications to efficiently generate global Bird's Eye View (BEV) features and local 3D feature volumes.
Classification of Safety Driver Attention During Autonomous Vehicle Operation
This paper introduces a dual-source approach integrating data from an infrared camera facing the vehicle operator and vehicle perception systems to produce a metric for driver alertness in order to promote and ensure safe operator behaviour.
MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object Detection
MS3D++ provides a straightforward approach to domain adaptation by generating high-quality pseudo-labels, enabling the adaptation of 3D detectors to a diverse range of lidar types, regardless of their density.
LightFormer: An End-to-End Model for Intersection Right-of-Way Recognition Using Traffic Light Signals and an Attention Mechanism
For smart vehicles driving through signalised intersections, it is crucial to determine whether the vehicle has right of way given the state of the traffic lights.
Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection
In this work, we devise a simple yet effective collaboration method that achieves a better bandwidth-performance tradeoff than prior state-of-the-art methods while minimizing changes made to the single-vehicle detection models and relaxing unrealistic assumptions on inter-agent synchronization.
MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection
Our proposed Kernel-Density Estimation (KDE) Box Fusion method fuses box proposals from multiple domains to obtain pseudo-labels that surpass the performance of the best source domain detectors.
Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns.
See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation
Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects.
Optimising the selection of samples for robust lidar camera calibration
We propose a robust calibration pipeline that optimises the selection of calibration samples for the estimation of calibration parameters that fit the entire scene.
Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles
Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task.
Robotics
Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases
Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay.
Camera-Lidar Integration: Probabilistic sensor fusion for semantic mapping
Our approach is capable of using a multi-sensor platform to build a three-dimensional semantic voxelized map that considers the uncertainty of all of the processes involved.
Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network
Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds.
Semantic sensor fusion: from camera to sparse lidar information
This paper presents an approach to fuse different sensory information, Light Detection and Ranging (lidar) scans and camera images.
