Geolocation estimation of target vehicles using image processing and
geometric computation
- URL: http://arxiv.org/abs/2203.10938v1
- Date: Tue, 8 Mar 2022 13:15:29 GMT
- Title: Geolocation estimation of target vehicles using image processing and
geometric computation
- Authors: Elnaz Namazi and Rudolf Mester and Chaoru Lu and Jingyue Li
- Abstract summary: Estimating vehicles' locations is one of the key components in intelligent traffic management systems.
The development of advanced sensing and communication technologies on modern vehicles makes it feasible to use such vehicles to estimate the traffic data of observed vehicles.
We propose a new methodology by integrating deep learning, image processing, and geometric computation to address the observed-vehicle localization problem.
- Score: 9.581332581510184
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Estimating vehicles' locations is one of the key components in intelligent
traffic management systems (ITMSs) for increasing traffic scene awareness.
Traditionally, stationary sensors have been employed in this regard. The
development of advanced sensing and communication technologies on modern
vehicles (MVs) makes it feasible to use such vehicles as mobile sensors to
estimate the traffic data of observed vehicles. This study aims to explore the
capabilities of a monocular camera mounted on an MV in order to estimate the
geolocation of the observed vehicle in a global positioning system (GPS)
coordinate system. We proposed a new methodology by integrating deep learning,
image processing, and geometric computation to address the observed-vehicle
localization problem. To evaluate our proposed methodology, we developed new
algorithms and tested them using real-world traffic data. The results indicated
that our proposed methodology and algorithms could effectively estimate the
observed vehicle's latitude and longitude dynamically.
Related papers
- A Holistic Framework Towards Vision-based Traffic Signal Control with
Microscopic Simulation [53.39174966020085]
Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions.
In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation.
We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
arXiv Detail & Related papers (2024-03-11T16:42:29Z) - Clustering Dynamics for Improved Speed Prediction Deriving from
Topographical GPS Registrations [0.0]
We propose solutions for speed prediction using sparse GPS data points and their associated topographical and road design features.
Our goal is to investigate whether we can use similarities in the terrain and infrastructure to train a machine learning model that can predict speed in regions where we lack transportation data.
arXiv Detail & Related papers (2024-02-12T09:28:16Z) - G-MEMP: Gaze-Enhanced Multimodal Ego-Motion Prediction in Driving [71.9040410238973]
We focus on inferring the ego trajectory of a driver's vehicle using their gaze data.
Next, we develop G-MEMP, a novel multimodal ego-trajectory prediction network that combines GPS and video input with gaze data.
The results show that G-MEMP significantly outperforms state-of-the-art methods in both benchmarks.
arXiv Detail & Related papers (2023-12-13T23:06:30Z) - Unsupervised Domain Adaptation for Self-Driving from Past Traversal
Features [69.47588461101925]
We propose a method to adapt 3D object detectors to new driving environments.
Our approach enhances LiDAR-based detection models using spatial quantized historical features.
Experiments on real-world datasets demonstrate significant improvements.
arXiv Detail & Related papers (2023-09-21T15:00:31Z) - Visual Perception System for Autonomous Driving [9.659835301514288]
This work introduces a visual-based perception system for autonomous driving that integrates trajectory tracking and prediction of moving objects to prevent collisions.
The system leverages motion cues from pedestrians to monitor and forecast their movements and simultaneously maps the environment.
The performance, efficiency, and resilience of this approach are substantiated through comprehensive evaluations of both simulated and real-world datasets.
arXiv Detail & Related papers (2023-03-03T23:12:43Z) - Continuous Self-Localization on Aerial Images Using Visual and Lidar
Sensors [25.87104194833264]
We propose a novel method for geo-tracking in outdoor environments by registering a vehicle's sensor information with aerial imagery of an unseen target region.
We train a model in a metric learning setting to extract visual features from ground and aerial images.
Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.
arXiv Detail & Related papers (2022-03-07T12:25:44Z) - Learning Traffic Speed Dynamics from Visualizations [3.0969191504482243]
We present a deep learning method to learn the macroscopic traffic speed dynamics from space-time visualizations.
Compared to existing estimation approaches, our approach allows a finer estimation resolution.
We present the high-resolution traffic speed fields estimated for several freeway sections using the data obtained from the Next Generation Simulation Program (NGSIM) and German Highway (HighD) datasets.
arXiv Detail & Related papers (2021-05-04T11:17:43Z) - Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention.
Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
arXiv Detail & Related papers (2021-04-19T11:48:13Z) - Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps.
This paper presents the further development of a previous approach.
The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
arXiv Detail & Related papers (2020-08-09T09:26:30Z) - A Survey on Deep Learning for Localization and Mapping: Towards the Age
of Spatial Machine Intelligence [48.67755344239951]
We provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning.
A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping.
It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities.
arXiv Detail & Related papers (2020-06-22T19:01:21Z) - Deep Learning Based Vehicle Tracking System Using License Plate
Detection And Recognition [0.0]
The proposed system uses a novel approach to vehicle tracking using Vehicle License plate detection and recognition (OCR) technique.
Results were obtained at a speed of 30 frames per second with accuracy close to human.
arXiv Detail & Related papers (2020-05-10T14:03:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.