The Automatic Packet Reporting System (APRS) is an amateur radio packet network that has evolved over the last several decades in tandem with, and then arguably beyond, the lifetime of other VHF/UHF amateur packet networks, to the point where it is one of very few packet networks left on the amateur VHF/UHF bands. This is proving to be problematic due to the loss of institutional knowledge as older amateur radio operators who designed and built APRS and other AX.25-based packet networks abandon the hobby or pass away. The purpose of this document is to collect and curate a sufficient body of knowledge to ensure the continued usefulness of the APRS network, and re-examining the engineering decisions made during the network’s evolution to look for possible improvements and identify deficiencies in documentation of the existing network.

M.Ing. / Traffic has become an extreme irritation and costly entity to deal with in recent years. Gone are the days where one could simply widen roadways to increase flow rates due to space constraints. Traffic costs countries billions of dollars per annum and thus the need to alleviate traffic congestion. Many technologies are currently available that can be used to lower the traffic density at an intersection, one of them being the use of cameras. Not only are digital cameras dropping in price, but the associated cost of maintenance is low. Distance information of a scene can thus be calculated via a visual system and from this information advanced control can be implemented in order to maximise traffic flow through an intersection. A traffic simulator was coded and analysed in order to validate the use of a visual system for increasing the amount of cars passing through the intersection per unit time over the current fixed timing system. Two different algorithms were compared to the current fixed timing scheme using a traffic simulator. The results showed that an improvement can be achieved over the current fixed timing scheme (of up to 19.92%). The use of stereovision as a method of attempting to monitor traffic flow is discussed. Vehicles were tracked using 13 trackers and the distance away from the stereo setup was calculated and compared to the actual distance away from the stereo setup. The best results found that with a baseline distance of 1500mm the average error in determining the distance of a vehicle was 16.46m. Although this error is quite large, it is still possible to monitor traffic flow using stereo vision with these inputs. Some of the issues that may cause these errors are camera quality, camera calibration and variable lighting conditions.

O monitoramento do trânsito de veículos rodoviários de carga em vias urbanas é dependente do trabalho de profissionais que precisam estar integralmente atentos ao tráfego de uma determinada via, e assim, identificar a presença desses veículos em meio a cenas complexas. Sistemas automáticos de identificação de veículos em quadros de vídeos podem agregar valor na qualidade de vida das cidades, pois tendem a diminuir os congestionamentos e incrementam segurança ao trânsito. Nesta pesquisa, propõe-se um modelo capaz de identificar e segregar veículos rodoviários de carga em vias urbanas, por meio de técnicas de processamento e análise de vídeos, gravados por câmeras posicionadas em diversos ângulos em dispositivos de acessos a vias urbanas. O sistema foi desenvolvido em linguagem MATLAB® e foram abordadas técnicas referentes às etapas de detecção, segmentação e processamento de objetos em fundos complexos. Ao final do trabalho, o sistema é aplicado em cenários de filmagem na cidade de São Carlos - SP e os resultados obtidos são discutidos. / The monitoring of the road freight vehicles traffic on urban roads relies on the work of professionals who need to be fully aware of the traffic on a specific road, and thereby identify the presence of these vehicles in complex scenes. Automatic vehicle identification in video shots may add value to the city life quality, due to congestion diminishment and traffic security improvement. In this research, a model able to identify and segregate road freight vehicles on urban roads is proposed, by means of video analysis and processing, that were recorded by cameras positioned at different angles on highways accesses on urban roads. The system was developed using MATLAB®. Techniques related to detecting, targeting and processing steps have been used to identify objects in complex backgrounds. At the end of the work, the system is applied to several scenarios that have been filmed in the city of São Carlos - SP and the results are discussed.

The car has become bigger, faster and more advanced for each passing year since its first appearance, and the safety requirements have also become stricter. Computer vision based support is a growing area of safety features where the car is equipped with a mono- or stereo camera. It can be used for detecting pedestrians walking out in the street, give a warning for wild-life during a cold January night using night-vision cameras and much more. This master thesis investigates the problem of detecting and tracking overtaking vehicles. Vehicles that overtake are only partly visible in the beginning, rendering it hard for standard detection/classification algorithms to get a positive detection. The need to quickly detect an incoming vehicle is crucial to be able to take fast counter-measure, such as braking, if needed. A novel approach referred to as the \textit{Wall detector} is suggested, detecting incoming vehicles using one-dimensional optical flow. Under the assumption that an overtaking car is moving in parallel to the ego-vehicle, both cars are moving towards the vanishing point in the image. A detection wall, consisting of several detection lines moving towards the vanishing point, is created, making all objects that are moving parallel to the ego-vehicle move along these lines. The result is a light-weight and fast detector with good detection performance in real-time. Several approaches for the Wall detector are implemented and evaluated, revealing that a feature based approach is the best choice. The information from the system can be used as input to heavier algorithms, boosting the confidence or to initialize a track.

This thesis presents the formulation and validation of tracking algorithms for vehicular motion for use in active collision prevention in V2V communications. The main objective is to estimate position and velocity of a vehicle based on update from vehicular wireless network. By using vehicular wireless network, the range of position estimation improves when compare to conventional radars and sensors. On the other hand, from a vehicular wireless network point of view regular measurement information update is more difficult to obtain because of packet losses due to interference between communicating vehicles. Our proposed algorithms are based on methods from position tracking termed alpha-beta trackers in aerospace applications with constant rate of information updates, with some modifications to better solve the problem. We present the main algorithms and provide numerical evidence of their accuracy based on simulation data. The modified filters are shown to be computationally efficient (lightweight) and provide sufficient accuracy for estimation of vehicle positions based on information update in a wireless V2V system.

This thesis presents the formulation and validation of tracking algorithms for vehicular motion for use in active collision prevention in V2V communications. The main objective is to estimate position and velocity of a vehicle based on update from vehicular wireless network. By using vehicular wireless network, the range of position estimation improves when compare to conventional radars and sensors. On the other hand, from a vehicular wireless network point of view regular measurement information update is more difficult to obtain because of packet losses due to interference between communicating vehicles. Our proposed algorithms are based on methods from position tracking termed alpha-beta trackers in aerospace applications with constant rate of information updates, with some modifications to better solve the problem. We present the main algorithms and provide numerical evidence of their accuracy based on simulation data. The modified filters are shown to be computationally efficient (lightweight) and provide sufficient accuracy for estimation of vehicle positions based on information update in a wireless V2V system.

No description available.

O monitoramento do trânsito de veículos rodoviários de carga em vias urbanas é dependente do trabalho de profissionais que precisam estar integralmente atentos ao tráfego de uma determinada via, e assim, identificar a presença desses veículos em meio a cenas complexas. Sistemas automáticos de identificação de veículos em quadros de vídeos podem agregar valor na qualidade de vida das cidades, pois tendem a diminuir os congestionamentos e incrementam segurança ao trânsito. Nesta pesquisa, propõe-se um modelo capaz de identificar e segregar veículos rodoviários de carga em vias urbanas, por meio de técnicas de processamento e análise de vídeos, gravados por câmeras posicionadas em diversos ângulos em dispositivos de acessos a vias urbanas. O sistema foi desenvolvido em linguagem MATLAB® e foram abordadas técnicas referentes às etapas de detecção, segmentação e processamento de objetos em fundos complexos. Ao final do trabalho, o sistema é aplicado em cenários de filmagem na cidade de São Carlos - SP e os resultados obtidos são discutidos. / The monitoring of the road freight vehicles traffic on urban roads relies on the work of professionals who need to be fully aware of the traffic on a specific road, and thereby identify the presence of these vehicles in complex scenes. Automatic vehicle identification in video shots may add value to the city life quality, due to congestion diminishment and traffic security improvement. In this research, a model able to identify and segregate road freight vehicles on urban roads is proposed, by means of video analysis and processing, that were recorded by cameras positioned at different angles on highways accesses on urban roads. The system was developed using MATLAB®. Techniques related to detecting, targeting and processing steps have been used to identify objects in complex backgrounds. At the end of the work, the system is applied to several scenarios that have been filmed in the city of São Carlos - SP and the results are discussed.

No description available.

<p dir="ltr">In 2021, the U.S. experienced over 45,000 road accident fatalities and approximately two million injuries, resulting in both emotional trauma and tangible economic impact. Road safety management traditionally depends on crash data, which though invaluable, is reactive, takes a long time to aggregate and has certain limitations. Traffic conflicts, the most used surrogate measure, promises to enhance road safety estimation without the drawbacks of crash data using a short amount of data collection.</p><p dir="ltr">After decades of debate, a definition for traffic conflicts that can be practically applied (Tarko, 2018, 2021) and a bridge method to estimate number of crashes given conflicts (Tarko, 2018) have emerged. The predictive validity of the bridge method has been successfully demonstrated for naturalistic driving data using a framework to extract conflicts (Tarko & Lizarazo, 2021). The only hurdle remaining for adoption of traffic conflicts to estimate safety at a given location is a means to record trajectories of all road users at that location.</p><p dir="ltr">Traditionally, video cameras and associated image processing techniques have been used to track road users at a given location. Cameras capture a 2D projection of the 3D world, therefore incur a loss of information and cameras are sensitive to ambient light conditions.</p><p dir="ltr">Over the past decade, LiDARs have emerged as an alternative to cameras for tracking road users. The advantage of LiDARs is that they record 3D information directly and are insensitive to ambient light conditions. Furthermore, they are less affected by adverse weather conditions than cameras. Spurred by the adoption by autonomous vehicle manufacturers, LiDAR sensors are projected to achieve cost parity with cameras over the next several years.</p><p dir="ltr">This dissertation explores the various aspects of LiDAR based tracking starting with sensor selection. The simulation work done shows the advantage of a multi-LiDAR setup in effectively covering an intersection. A novel self-aligning procedure to achieve spatial congruity proposed is shown to outperform the state of the art.</p><p dir="ltr">New methods for identifying and removing background points that work even under moderate congestion have been proposed. New methods for clustering the non-background points and estimating a bounding box with proper orientation are proposed. The results of the experiments show that they work better than the corresponding state of the art methods. The rest of the processing follows the framework introduced by (Bandaru, 2016).</p><p dir="ltr">A thorough evaluation of positional accuracy, orientation accuracy and accuracy of estimated vehicle dimensions has been undertaken using data from an instrumented vehicle acting as ground truth to prove that the trajectories generated are of sufficient quality to identify traffic conflicts.</p><p dir="ltr">Further, the framework proposed by (Lizarazo, 2020) has been adopted to identify traffic encounters from the trajectories obtained. A new method to select an alternative trajectory for a vehicle exhibiting an evasive maneuver, used in the counterfactual analysis to estimate time to collision is proposed. Data collected at three different intersections using a LiDAR are processed to extract trajectories and the framework is applied to identify safety relevant events. The spatial distribution of the identified events is compared against the spatial distribution of crashes.</p><p dir="ltr">While the spatial distribution shows promise, the actual number of claimed conflicts was too low. The rare nature of failure caused traffic conflict that can be linked to crashes could be a reason. A more permanent installation is suggested to ascertain the duration required to observe sufficient number of traffic conflicts, that could be used to reliably estimate crashes.</p><p dir="ltr"><b>References:</b></p><p dir="ltr">Bandaru, V. K. (2016). Algorithms for LiDAR Based Traffic Tracking: Development and Demonstration. <i>Open Access Theses</i>. https://docs.lib.purdue.edu/open_access_theses/922</p><p dir="ltr">Lizarazo, C. (2020). <i>Identification Of Failure-Caused Traffic Conflicts in Tracking Systems: A General Framework</i> [PhD Thesis]. Purdue University.</p><p dir="ltr">Tarko, A. (2018). Estimating the expected number of crashes with traffic conflicts and the Lomax Distribution–A theoretical and numerical exploration. <i>Accident Analysis & Prevention</i>, <i>113</i>, 63–73.</p><p dir="ltr">Tarko, A. (2021). A unifying view on traffic conflicts and their connection with crashes. <i>Accident Analysis & Prevention</i>, <i>158</i>, 106187. https://doi.org/10.1016/j.aap.2021.106187</p><p dir="ltr">Tarko, A., & Lizarazo, C. (2021). Validity of failure-caused traffic conflicts as surrogates of rear-end collisions in naturalistic driving studies. <i>Accident Analysis & Prevention</i>, <i>149</i>, 105863.</p>