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Found 17 papers, 0 papers with code
Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data
(3) The trust scores are unreliable for classes whose features are confused with other classes.
Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections
A multi-task conditional imitation learning framework is proposed to adapt both lateral and longitudinal control tasks for safe and efficient interaction.
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
Off-road semantic segmentation with fine-grained labels is necessary for autonomous vehicles to understand driving scenes, as the coarse-grained road detection can not satisfy off-road vehicles with various mechanical properties.
An Image-based Approach of Task-driven Driving Scene Categorization
A measure is learned to discriminate the scenes of different semantic attributes via contrastive learning, and a driving scene profiling and categorization method is developed based on that measure.
Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning
With a set of human-annotated anchor patches, a feature representation is learned to discriminate regions with different traversability, a method of fine-grained semantic segmentation and mapping is subsequently developed for off-road scene understanding.
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study
Finally, a systematic survey to the existing efforts to solve the data hunger problem is conducted on both methodological and dataset's viewpoints, followed by an insightful discussion of remaining problems and open questions To the best of our knowledge, this is the first work to analyze the data hunger problem for 3D semantic segmentation using deep learning techniques that are addressed in the literature review, statistical analysis, and cross-dataset and cross-algorithm experiments.
Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving
In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed.
Driver Identification through Stochastic Multi-State Car-Following Modeling
In this study, a joint model of the two types of heterogeneity in car-following behavior is proposed as an approach of driver profiling and identification.
Cross Scene Prediction via Modeling Dynamic Correlation using Latent Space Shared Auto-Encoders
A method is proposed to solve the problem via modeling dynamic correlation using latent space shared auto-encoders.
Off-Road Drivable Area Extraction Using 3D LiDAR Data
We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application.
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
3D semantic segmentation is one of the key tasks for autonomous driving system.
Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning
Different cost functions of traversability analysis are learned and tested at various scenes of capability in guiding the trajectory planning of different behaviors.
Incorporating Human Domain Knowledge in 3D LiDAR-based Semantic Segmentation
We propose a new method that makes full use of the advantages of traditional methods and deep learning methods via incorporating human domain knowledge into the neural network model to reduce the demand for large numbers of manual annotations and improve the training efficiency.
Semantic Segmentation of 3D LiDAR Data in Dynamic Scene Using Semi-supervised Learning
This work studies the semantic segmentation of 3D LiDAR data in dynamic scenes for autonomous driving applications.
Robotics
Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns
Graph matching and graph mining are two typical areas in artificial intelligence.
When 3D Reconstruction Meets Ubiquitous RGB-D Images
3D reconstruction from a single image is a classical problem in computer vision.
Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models
We design a graphical model that uses object edges to represent object structures, and this paper aims to incrementally learn this category model from one labeled object and a number of casually captured scenes.
