Search Results for author:
Found 16 papers, 9 papers with code
GraphRelate3D: Context-Dependent 3D Object Detection with Inter-Object Relationship Graphs
In this study, we introduce an object relation module, consisting of a graph generator and a graph neural network (GNN), to learn the spatial information from certain patterns to improve 3D object detection.
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques.
Vision Language Models in Autonomous Driving: A Survey and Outlook
The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD) have attracted widespread attention due to their outstanding performance and the ability to leverage Large Language Models (LLMs).
3D Understanding of Deformable Linear Objects: Datasets and Transferability Benchmark
Finally, we analyzed the generalization capabilities of these methods by conducting transferability experiments on the PointWire and PointVessel datasets.
Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling
Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and unlikely scenarios and edge cases.
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone
Our main contribution leverages learned flow and motion representations and combines a self-supervised backbone with a supervised 3D detection head.
Formal Certification Methods for Automated Vehicle Safety Assessment
In this work, we provide both an overview of the safety verification, validation and certification process, as well as review formal safety techniques that are best suited to AV applications.
Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving
Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving.
Temp-Frustum Net: 3D Object Detection with Temporal Fusion
Furthermore, ablation studies reinforce that the subject of improvement is temporal fusion and show the effects of different placements of TFM in the object detection pipeline.
Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention
This work addresses the above limitations by introducing a novel neural network architecture to fuse inherently different spatio-temporal features for pedestrian crossing intention prediction.
Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion
The main strategy is to depend solely on the 2D vision for recognizing object class, as object shape does not change drastically with an increase in depth, and use pointcloud data for object localization in the 3D space for faraway objects.
Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering
Given a 3D scene, we partially render only important objects of interest, such as vehicles, and use generative adversarial processes to synthesize the background and the rest of the image.
A Vision-based Social Distancing and Critical Density Detection System for COVID-19
If a violation is detected, a non-intrusive audio-visual warning signal is emitted without targeting the individual who breached the social distancing measure.
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving
Automated driving in urban settings is challenging.
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience.
Robotics Systems and Control Systems and Control
Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer
The best result, with a 0. 937 AUC score, was obtained with the proposed network.
