Search Results for author:
Found 13 papers, 3 papers with code
OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising
By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects, our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments.
SiCP: Simultaneous Individual and Cooperative Perception for 3D Object Detection in Connected and Automated Vehicles
Cooperative perception for connected and automated vehicles is traditionally achieved through the fusion of feature maps from two or more vehicles.
Layout Sequence Prediction From Noisy Mobile Modality
In summary, our approach offers a promising solution to the challenges faced by layout sequence and trajectory prediction models in real-world settings, paving the way for utilizing sensor data from mobile phones to accurately predict pedestrian bounding box trajectories.
Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets.
Learning to Localize with Attention: from sparse mmWave channel estimates from a single BS to high accuracy 3D location
One strategy to obtain user location information in a wireless network operating at millimeter wave (mmWave) is based on the exploitation of the geometric relationships between the channel parameters and the user position.
ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences
During inference, it generates refined position estimations based only on pedestrians' phone data that consists of GPS, IMU and FTM.
Vi-Fi: Associating Moving Subjects across Vision and Wireless Sensors
In this paper, we present Vi-Fi, a multi-modal system that leverages a user’s smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e. g. WiFi MAC address).
Joint Initial Access and Localization in Millimeter Wave Vehicular Networks: a Hybrid Model/Data Driven Approach
High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered.
Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation
In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects.
Feature Sharing and Integration for Cooperative Cognition and Perception with Volumetric Sensors
In this paper, we examine the requirements, limitations, and performance of different cooperative perception techniques, and present an in-depth analysis of the notion of Deep Feature Sharing (DFS).
Multi-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization
Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles.
Securing Vehicle-to-Everything (V2X) Communication Platforms
Modern vehicular wireless technology enables vehicles to exchange information at any time, from any place, to any network -- forms the vehicle-to-everything (V2X) communication platforms.
Networking and Internet Architecture Cryptography and Security
Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks
The recent advancements in communication and computational systems has led to significant improvement of situational awareness in connected and autonomous vehicles.
