Vehicle speed estimation based on license plate detection

In this work, we present an approach for vehicle speed estimation using a flexible camera setup: the only requirement is a calibrated camera. Then we use the calibrated camera to record images of the vehicle on the road, and use a state-of-the-art object detector to identify if there is a vehicle in the image. For each vehicle we use a license plate detector to extract the corresponding pixels for the four corners of the license plate (LP), and we use the known dimensions of the LP and estimate the homography matrix to be able to obtain the real world coordinates for the LP. Then, we propose a two methods to better estimate the vehicle speed based on the tracking of the LP. We create a dataset to test the proposed method, and we show the results for each method proposed method. Our best method was able to estimate the speed of vehicles with an average error of 11.15%.Neste trabalho propomos uma solução para estimação da velocidade de veículos usando uma configuração de câmera com apenas uma restrição: a câmera precisa estar calibrada. Após isso,usamos a câmera calibrada para gravar imagens de veículos nas vias, e usamos um detector de objeto estado da arte para identificar se existe um veículo na imagem. Para cada veículo que o detector de objetos detectar, usamos detector de placas de veículo para extrair os pixels correspondentes às quinas da placa, como sabemos as dimensões reais da placa, estimamos uma matriz capaz de obter as coordenadas de mundo da placa. Então propomos uma série de métodos para melhor estimar a velocidade do veículo com base no monitoramento da placa. Também criamos um dataset para podermos testar os métodos propostos. Também mostramos os resultados para cada método proposto. Nosso melhor método é capaz de estimar a velocidade dos veiculos com um erro médio de 11.15%

Encounter Risk Evaluation with a Forerunner UAV

Forerunner UAV refers to an unmanned aerial vehicle equipped with a downward-looking camera flying in front of the advancing emergency ground vehicles (EGV) to notify the driver about the hidden dangers (e.g., other vehicles). A feasibility demonstration in an urban environment having a multicopter as the forerunner UAV and two cars as the emergency and dangerous ground vehicles was done in ZalaZONE Proving Ground, Hungary. After the description of system hardware and software components, test scenarios, object detection and tracking, the main contribution of the paper is the development and evaluation of encounter risk decision methods. First, the basic collision risk evaluation applied in the demonstration is summarized, then the detailed development of an improved method is presented. It starts with the comparison of different velocity and acceleration estimation methods. Then, vehicle motion prediction is conducted, considering estimated data and its uncertainty. The prediction time horizon is determined based on actual EGV speed and so braking time. If the predicted trajectories intersect, then the EGV driver is notified about the danger. Some special relations between EGV and the other vehicle are also handled. Tuning and comparison of basic and improved methods is done based on real data from the demonstration. The improved method can notify the driver longer, identify special relations between the vehicles and it is adaptive considering actual EGV speed and EGV braking characteristics; therefore, it is selected for future application

Sistema de identificación de vehículos basado en campo magnético

En la actualidad, la sociedad demanda una alta cantidad de bienes de consumo. Para poder hacer frente a esta, son necesarios un gran número de transportes, tanto de productos como de personas. Esta situación provoca que se produzcan muchos de desplazamientos a través de las vías públicas. Para asegurarse de que se realicen de forma segura y eficiente es necesario la implementación de los sistemas de control del tráfico. El sistema de monitorización del tráfico más extendido es el bucle inductivo, sin embargo, presenta algunos problemas en cuanto a la calidad de la información que proporciona y su vida útil. Por ello están ganando importancia como futuros sustitutos los nodos de sensores inalámbricos. Estos proporcionan una información más rica, además de una vida útil más larga. También presentan una gran capacidad para formar redes entre ellos y otros dispositivos permitiendo el desarrollo de la Smart City. El objetivo que se persigue en este trabajo es un estudio de las posibilidades de detección de la dirección y estimación del tamaño de los vehículos mediante métodos de identificación, basados en la medida de la deformación en el campo magnético terrestre y de las vibraciones inducidas en la calzada por el paso de un vehículo. Para ello, primeramente se realiza una investigación de los sistemas más relevantes para la monitorización de vehículos que actualmente están implantados así como las distintas líneas de desarrollo en este campo. Esta información es utilizada para la toma de las decisiones previas al desarrollo de los clasificadores Posteriormente se realiza una experimentación de campo mediante un nodo sensor que ha permitido capturar la huella magnética y las vibraciones inducidas en el asfalto de 113 vehículos. Pre-procesando esta información se obtiene una base de datos con información de las características de los vehículos y sus correspondientes capturas. Con la ayuda de las conclusiones obtenidas del estudio de trabajos previos y mediante la base de datos conseguida mediante la experimentación, se diseñan dos clasificadores, uno para distinguir la dirección de los vehículos y otro para diferenciar entre los mayores de 4,5m y los menores. Como conclusión final, el principal objetivo de este trabajo se cumple satisfactoriamente. La viabilidad de esta tecnología queda demostrada. También se observa que la clasificación basada en el tamaño se puede realizar con la utilización de unas características de bajo coste computacional, mientras que la basada en la dirección necesita unas más complejas

Development,Validation, and Integration of AI-Driven Computer Vision System and Digital-twin System for Traffic Safety Dignostics

The use of data and deep learning algorithms in transportation research have become increasingly popular in recent years. Many studies rely on real-world data. Collecting accurate traffic data is crucial for analyzing traffic safety. Still, traditional traffic data collection methods that rely on loop detectors and radar sensors are limited to collect macro-level data, and it may fail to monitor complex driver behaviors like lane changing and interactions between road users. With the development of new technologies like in-vehicle cameras, Unmanned Aerial Vehicle (UAV), and surveillance cameras, vehicle trajectory data can be collected from the recorded videos for more comprehensive and microscopic traffic safety analysis. This research presents the development, validation, and integration of three AI-driven computer vision systems for vehicle trajectory extraction and traffic safety research: 1) A.R.C.I.S, an automated framework for safety diagnosis utilizing multi-object detection and tracking algorithm for UAV videos. 2)N.M.E.D.S., A new framework with the ability to detect and predict the key points of vehicles and provide more precise vehicle occupying locations for traffic safety analysis. 3)D.V.E.D.S applied deep learning models to extract information related to drivers\u27 visual environment from the Google Street View (GSV) images. Based on the drone video collected and processed by A.R.C.I.S at various locations, CitySim: a new drone recorded vehicle trajectory dataset that aim to facilitate safety research was introduced. CitySim has vehicle interaction trajectories extracted from 1140- minutes of video recordings, which provide a large-scale naturalistic vehicle trajectory that covers a variety of locations, including basic freeway segments, freeway weaving segments, expressway segments, signalized intersections, stop-controlled intersections, and unique intersections without sign/signal control. The advantage of CitySim over other datasets is that it contains more critical safety events in quantity and severity and provides supporting scenarios for safety-oriented research. In addition, CitySim provides digital twin features, including the 3D base maps and signal timings, which enables a more comprehensive testing environment for safety research, such as autonomous vehicle safety. Based on these digital twin features provided by CitySim, we proposed a Digital Twin framework for CV and pedestrian in-the-loop simulation, which is based on Carla-Sumo Co-simulation and Cave automatic virtual environment (CAVE). The proposed framework is expected to guide the future Digital Twin research, and the architecture we build can serve as the testbed for further research and development

Vehicle Extraction and Speed Detection from Digital Aerial Images

A new object-based method is developed to extract the moving vehicles and subsequently detect their speeds from two consecutive digital aerial images automatically. Several parameters of gray values and sizes are examined to classify the objects in the image. The vehicles and their associated shadows can be discriminated by removing big objects such as roads. To detect the speed, firstly the vehicles and shadows are extracted from the two images. The corresponding vehicles from these images are linked based on the order, size, and their distance within a threshold. Finally, using the distance between the corresponding vehicles and the time lag between the two images, the moving speed can be detected. Our test shows a promising result of detecting the moving vehicles ’ speeds. Further development will employ the proposed method for a pair of QuickBird panchromatic and multi-spectral images, which are at a coarser spatial resolution. Index Terms — Digital aerial image, vehicle extraction, speed detection, object-based metho