Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance

Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles. We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX~2

pH in exhaled breath condensate and nasal lavage as a biomarker of air pollution-related inflammation in street traffic-controllers and office-workers

OBJECTIVE: To utilize low-cost and simple methods to assess airway and lung inflammation biomarkers related to air pollution. METHODS: A total of 87 male, non-smoking, healthy subjects working as street traffic-controllers or office-workers were examined to determine carbon monoxide in exhaled breath and to measure the pH in nasal lavage fluid and exhaled breath condensate. Air pollution exposure was measured by particulate matter concentration, and data were obtained from fixed monitoring stations (8-h work intervals per day, during the 5 consecutive days prior to the study). RESULTS: Exhaled carbon monoxide was two-fold greater in traffic-controllers than in office-workers. The mean pH values were 8.12 in exhaled breath condensate and 7.99 in nasal lavage fluid in office-workers; these values were lower in traffic-controllers (7.80 and 7.30, respectively). Both groups presented similar cytokines concentrations in both substrates, however, IL-1β and IL-8 were elevated in nasal lavage fluid compared with exhaled breath condensate. The particulate matter concentration was greater at the workplace of traffic-controllers compared with that of office-workers. CONCLUSION: The pH values of nasal lavage fluid and exhaled breath condensate are important, robust, easy to measure and reproducible biomarkers that can be used to monitor occupational exposure to air pollution. Additionally, traffic-controllers are at an increased risk of airway and lung inflammation during their occupational activities compared with office-workers.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade de São Paulo Faculdade de Medicina Department of PathologyUniversidade de São Paulo Faculdade de Medicina Department of PhysiotherapyPhilipps University Department of PulmonologyLeiden University Medical Center Department of PulmonologyUniversidade Federal de São Paulo (UNIFESP) School of Medicine Department of PneumologyPneumology Division Pneumology DivisionInstituto do Coracao Instituto do CoracaoUniversidade de São Paulo Faculdade de MedicinaUniversidade de São Paulo Faculdade de Medicina Department of Internal MedicineUniversidade de São Paulo Public Health Faculty Department of EpidemiologyUniversidade de São Paulo Institute of Mathematics and StatisticsUNIFESP, School of Medicine Department of PneumologyFAPESP: 07/51605-9FAPESP: 09/50056-7CNPq: 555.223/06-0SciEL

pH in exhaled breath condensate and nasal lavage as a biomarker of air pollution-related inflammation in street traffic-controllers and office-workers

OBJECTIVE: To utilize low-cost and simple methods to assess airway and lung inflammation biomarkers relatedto air pollution.METHODS: A total of 87 male, non-smorking, healthy subjects working as street traffic-controllers or office-workers were examined to determine carbon monoxide in ixhaled breath and to measure the pH in nasal lavage fluid and exhaled breath condensate. Air pollution exposure was measured by particulate matter concentration, and data were obtained from fixed monitoring stations (8-h work intervals per day, during the 5 consecutive days prior to the study).RESULTS: Exhaled carbon monoxide was two-fold greater in traffic-controllers than in office-workers. The mean pH values were 8.12 in exhaled breath condensate and 7.99 in nasal lavage fluid in office-workers; these values concentrations in both substrates, however, Il-aB and IL-8 were elevated in nasal lavage fluid compared with exhaled breath condensate. The particulate matter concentration weas greater at the workplace of traffic-controllers compared with that of office-workers.CONCLUSION: The pH values of nasal lavage fluid and exhaled breath condensate are important, robust, easy to measure and reproducible biomarkers that can be used to monitor occupational exposure to air pollution. Additionally, traffic-controllers are at an increased risk of airway and lung inflammation during their occupational activities compared with office-workers

Sequential Monte Carlo simulation of collision risk in free flight air traffic

Within HYBRIDGE a novel approach in speeding up Monte Carlo simulation of rare events has been developed. In the current report this method is extended for application to simulating collisions with a stochastic dynamical model of an air traffic operational concept. Subsequently this extended Monte Carlo simulation approach is applied to a simulation model of an advanced free flight operational concept; i.e. one in which aircraft are responsible for self separation with each other. The Monte Carlo simulation results obtained for this advanced concept show that the novel method works well, and that it allows studying rare events that stayed invisible in previous Monte Carlo simulations of advanced air traffic operational concepts

Static and Motion-Based Visual Features Used by Airport Tower Controllers: Some Implications for the Design of Remote or Virtual Towers

Visual motion and other visual cues are used by tower controllers to provide important support for their control tasks at and near airports. These cues are particularly important for anticipated separation. Some of them, which we call visual features, have been identified from structured interviews and discussions with 24 active air traffic controllers or supervisors. The visual information that these features provide has been analyzed with respect to possible ways it could be presented at a remote tower that does not allow a direct view of the airport. Two types of remote towers are possible. One could be based on a plan-view, map-like computer-generated display of the airport and its immediate surroundings. An alternative would present a composite perspective view of the airport and its surroundings, possibly provided by an array of radially mounted cameras positioned at the airport in lieu of a tower. An initial more detailed analyses of one of the specific landing cues identified by the controllers, landing deceleration, is provided as a basis for evaluating how controllers might detect and use it. Understanding other such cues will help identify the information that may be degraded or lost in a remote or virtual tower not located at the airport. Some initial suggestions how some of the lost visual information may be presented in displays are mentioned. Many of the cues considered involve visual motion, though some important static cues are also discussed

Control and optimization algorithms for air transportation systems

Modern air transportation systems are complex cyber-physical networks that are critical to global travel and commerce. As the demand for air transport has grown, so have congestion, flight delays, and the resultant environmental impacts. With further growth in demand expected, we need new control techniques, and perhaps even redesign of some parts of the system, in order to prevent cascading delays and excessive pollution. In this survey, we consider examples of how we can develop control and optimization algorithms for air transportation systems that are grounded in real-world data, implement them, and test them in both simulations and in field trials. These algorithms help us address several challenges, including resource allocation with multiple stakeholders, robustness in the presence of operational uncertainties, and developing decision-support tools that account for human operators and their behavior. Keywords: Air transportation; Congestion control; Large-scale optimization; Data-driven modeling; Human decision processe

An Appearance-Based Tracking Algorithm for Aerial Search and Rescue Purposes

The automation of the Wilderness Search and Rescue (WiSAR) task aims for high levels of understanding of various scenery. In addition, working in unfriendly and complex environments may cause a time delay in the operation and consequently put human lives at stake. In order to address this problem, Unmanned Aerial Vehicles (UAVs), which provide potential support to the conventional methods, are used. These vehicles are provided with reliable human detection and tracking algorithms; in order to be able to find and track the bodies of the victims in complex environments, and a robust control system to maintain safe distances from the detected bodies. In this paper, a human detection based on the color and depth data captured from onboard sensors is proposed. Moreover, the proposal of computing data association from the skeleton pose and a visual appearance measurement allows the tracking of multiple people with invariance to the scale, translation and rotation of the point of view with respect to the target objects. The system has been validated with real and simulation experiments, and the obtained results show the ability to track multiple individuals even after long-term disappearances. Furthermore, the simulations present the robustness of the implemented reactive control system as a promising tool for assisting the pilot to perform approaching maneuvers in a safe and smooth manner.This research is supported by Madrid Community project SEGVAUTO 4.0 P2018/EMT-4362) and by the Spanish Government CICYT projects (TRA2015-63708-R and TRA2016-78886-C3-1-R), and Ministerio de Educación, Cultura y Deporte para la Formación de Profesorado Universitario (FPU14/02143). Also, we gratefully acknowledge the support of the NVIDIA Corporation with the donation of the GPUs used for this research