Understanding common human driving semantics for autonomous vehicles.

Autonomous vehicles will share roads with human-driven vehicles until the transition to fully autonomous transport systems is complete. The critical challenge of improving mutual understanding between both vehicle types cannot be addressed only by feeding extensive driving data into data-driven models but by enabling autonomous vehicles to understand and apply common driving behaviors analogous to human drivers. Therefore, we designed and conducted two electroencephalography experiments for comparing the cerebral activities of human linguistics and driving understanding. The results showed that driving activates hierarchical neural functions in the auditory cortex, which is analogous to abstraction in linguistic understanding. Subsequently, we proposed a neural-informed, semantics-driven framework to understand common human driving behavior in a brain-inspired manner. This study highlights the pathway of fusing neuroscience into complex human behavior understanding tasks and provides a computational neural model to understand human driving behaviors, which will enable autonomous vehicles to perceive and think like human drivers.

Performance evaluation model of transportation infrastructure: Perspective of COVID-19.

The transportation systems are facing major challenges due to changes social environment caused by the COVID-19 pandemic. How to construct a suitable evaluation criterion system and suitable assessment method to evaluate the status of the urban transportation resilience has become a predicament nowadays. Firstly, the criteria for evaluating the current state of transportation resilience involve many aspects. New features of transportation resilience under epidemic normalization are exposed, and previous summaries focusing on resilience characteristics under natural disasters can hardly reflect the current state of urban transportation resilience comprehensively. Based on this, this paper attempts to incorporate the new criteria (Dynamicity, Synergy, Policy) into the evaluation system. Secondly, the assessment of urban transportation resilience involves numerous indicators, which make it difficult to obtain quantitative figures for the criteria. With this background, a comprehensive multi-criteria assessment model based on q-rung orthopair 2-tuple linguistic sets is constructed to evaluate the status of transportation infrastructure from perspective on the COVID-19. Then, an example of urban transportation resilience is given to demonstrate the feasibility of the proposed approach. Subsequently, sensitivity analysis about parameters and global robust sensitivity analysis are conducted, and comparative analysis of existing method is given. The results reveal that the proposed method is sensitive to global criteria weights, so it is suggested that more attention should be paid to the rationality of the weight of criteria to avoid the influence on the results when solving MCDM problems. Finally, the policy implications regarding transport infrastructure resilience and appropriate model development are given.

Building Global Capacity to Conduct Pathology-Based Postmortem Examination: Establishing a New Training Hub for Minimally Invasive Tissue Sampling.

BACKGROUND: Minimally invasive tissue sampling (MITS), an alternative to complete diagnostic autopsy, is a pathology-based postmortem examination that has been validated in low- and middle-income countries (LMICs) and can provide accurate cause of death information when used with other data. The MITS Surveillance Alliance was established in 2017 with the goal to expand MITS globally by increasing training capacity, accessibility, and availability in LMICs. Between January 2019 and May 2020, the MITS Surveillance Alliance convened a multidisciplinary team of technical advisors to attain this goal. METHODS: This article describes the process used to develop criteria and identify an optimal location for a MITS training hub, establish a cadre of LMIC-based trainers, refine standardized MITS sample collection protocols, develop a training program, and release a telepathology platform for quality assessment of MITS histological samples. RESULTS: Results include the creation of a training hub and curriculum, with a total of 9 pathologists and technicians trained as part of the training of the trainers. Those trainers trained 15 participants from seven MITS projects representing 6 LMICs trained in MITS sample collection. The 15 participants have gone on to train more than 50 project-level staff in MITS sample collection. CONCLUSIONS: Lessons learned include an appreciation for using an iterative process for establishing standardized procedures, creating opportunities for all stakeholders to deliver critical feedback, and highlighting the importance of complementing in-person trainings with ongoing technical assistance.

Effect of Time Since Death on Multipathogen Molecular Test Results of Postmortem Specimens Collected Using Minimally Invasive Tissue Sampling Techniques.

BACKGROUND: We used postmortem minimally invasive tissue sampling (MITS) to assess the effect of time since death on molecular detection of pathogens among respiratory illness-associated deaths. METHODS: Samples were collected from 20 deceased children (aged 1-59 months) hospitalized with respiratory illness from May 2018 through February 2019. Serial lung and/or liver and blood samples were collected using MITS starting soon after death and every 6 hours thereafter for up to 72 hours. Bodies were stored in the mortuary refrigerator for the duration of the study. All specimens were analyzed using customized multipathogen TaqMan® array cards (TACs). RESULTS: We identified a median of 3 pathogens in each child's lung tissue (range, 1-8; n = 20), 3 pathogens in each child's liver tissue (range, 1-4; n = 5), and 2 pathogens in each child's blood specimen (range, 0-4; n = 5). Pathogens were not consistently detected across all collection time points; there was no association between postmortem interval and the number of pathogens detected (P = .43) and no change in TAC cycle threshold value over time for pathogens detected in lung tissue. Human ribonucleoprotein values indicated that specimens collected were suitable for testing throughout the study period. CONCLUSIONS: Results suggest that lung, liver, and blood specimens can be collected using MITS procedures up to 4 days after death in adequately preserved bodies. However, inconsistent pathogen detection in samples needs careful consideration before drawing definitive conclusions on the etiologic causes of death.

A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform.

This paper develops novel Global Navigation Satellite System (GNSS) interference detection methods based on the Hough transform. These methods are realized by incorporating the Hough transform into three Time-Frequency distributions: Wigner-Ville distribution, pseudo -Wigner-Ville distribution and smoothed pseudo-Wigner-Ville distribution. This process results in the corresponding Wigner-Hough transform, pseudo-Wigner-Hough transform and smoothed pseudo-Wigner-Hough transform, which are used in GNSS interference detection to search for local Hough-transformed energy peak in a small limited area within the parameter space. The developed GNSS interference detection methods incorporate a novel concept of zero Hough-transformed energy distribution percentage to analyze the properties of energy concentration and cross-term suppression. The methods are tested with real GPS L1-C/A data collected in the presence of sweep interference. The test results show that the developed methods can deal with the cross-term problem with improved interference detection performance. In particular, the GNSS interference detection performance obtained with the smoothed pseudo-Wigner-Hough transform method is at least double that of the Wigner-Hough transform-based approach; the smoothed pseudo-Wigner-Hough transform-based GNSS interference detection method is improved at least 20% over the pseudo-Wigner-Hough transform-based technique in terms of the zero Hough-transformed energy percentage criteria. Therefore, the proposed smoothed pseudo-Wigner-Hough transform-based method is recommended in the interference detection for GNSS receivers, particularly in challenging electromagnetic environments.

GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application.

The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision.

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

An Analytical Model for BDS B1 Spreading Code Self-Interference Evaluation Considering NH Code Effects.

The short spreading code used by the BeiDou Navigation Satellite System (BDS) B1-I or GPS Coarse/Acquistiion (C/A) can cause aggregately undesirable cross-correlation between signals within each single constellation. This GPS-to-GPS or BDS-to-BDS correlation is referred to as self-interference. A GPS C/A code self-interference model is extended to propose a self-interference model for BDS B1, taking into account the unique feature of the B1-I signal transmitted by BDS medium Earth orbit (MEO) and inclined geosynchronous orbit (IGSO) satellites-an extra Neumann-Hoffmann (NH) code. Currently there is no analytical model for BDS self-interference and a simple three parameter analytical model is proposed. The model is developed by calculating the spectral separation coefficient (SSC), converting SSC to equivalent white noise power level, and then using this to calculate effective carrier-to-noise density ratio. Cyclostationarity embedded in the signal offers the proposed model additional accuracy in predicting B1-I self-interference. Hardware simulator data are used to validate the model. Software simulator data are used to show the impact of self-interference on a typical BDS receiver including the finding that self-interference effect is most significant when the differential Doppler between desired and undesired signal is zero. Simulation results show the aggregate noise caused by just two undesirable spreading codes on a single desirable signal could lift the receiver noise floor by 3.83 dB under extreme C/N0 (carrier to noise density ratio) conditions (around 20 dB-Hz). This aggregate noise has the potential to increase code tracking standard deviation by 11.65 m under low C/N0 (15-19 dB-Hz) conditions and should therefore, be avoided for high-sensitivity applications. Although the findings refer to Beidou system, the principle weakness of the short codes illuminated here are valid for other satellite navigation systems.

The impact of airport characteristics on airport surface accidents and incidents.

INTRODUCTION: Airport surface safety and in particular runway and taxiway safety is acknowledged globally as one of aviation's greatest challenges. To improve this key area of aviation safety, it is necessary to identify and understand the causal and contributing factors on safety occurrences. While the contribution of human factors, operations, and procedures has been researched extensively, the impact of the airport and its associated characteristics itself has received little or no attention. METHOD: This paper introduces a novel methodology for risk and hazard assessment of airport surface operations, and models the relationships between airport characteristics, and (a) the rate of occurrences, (b) the severity of occurrences, and (c) the causal factors underlying occurrences. RESULTS: The results show for the first time how the characteristics of airports, and in particular its infrastructure and operations, influence the safety of surface operations.

Modelling runway incursion severity.

Analysis of the causes underlying runway incursions is fundamental for the development of effective mitigation measures. However, there are significant weaknesses in the current methods to model these factors. This paper proposes a structured framework for modelling causal factors and their relationship to severity, which includes a description of the airport surface system architecture, establishment of terminological definitions, the determination and collection of appropriate data, the analysis of occurrences for severity and causes, and the execution of a statistical analysis framework. It is implemented in the context of U.S. airports, enabling the identification of a number of priority interventions, including the need for better investigation and causal factor capture, recommendations for airfield design, operating scenarios and technologies, and better training for human operators in the system. The framework is recommended for the analysis of runway incursions to support safety improvements and the methodology is transferable to other areas of aviation safety risk analysis.

Motion field estimation for a dynamic scene using a 3D LiDAR.

This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively.

Genetic diversity of HIV type 1 along the coastal strip of Kenya.

A study on the genetic diversity of HIV-1 subtypes present along the coastal strip of Kenya, i.e., Kilifi, Mombasa, Msambweni, and Malindi districts, was carried out. DNA sequences for regions encoding a portion of the env-gp41 region of the virus were generated by PCR and sequenced directly. Eighty six samples that were successfully sequenced were analyzed. From the analysis, 86% (74) were subtype A1, 5% (4) were subtype C, 8% (7) were subtype D, and 1% (1) was subtype G. This study shows that HIV-1 subtype A1 is the most dominant subtype in circulation in this region.

HIV type 1 genetic diversity in Moyale, Mandera, and Turkana based on env-C2-V3 sequences.

The genetic diversity of HIV-1 subtypes circulating in three districts of northern Kenya, i.e., Turkana, Mandera, and Moyale, was studied. DNA sequences encoding a portion of the env-C2-V3 region of the virus were amplified by PCR and sequenced directly. One hundred and fifty-nine samples were successfully sequenced in the env-C2-V3 region and analyzed. From the analysis, 57% were subtype A1, 27% were subtype C, 9% were subtype D, and the remaining 7% were unclassified. This study showed that HIV-1 subtype A1 was the dominant subtype in circulation in this region, though there was a significant percentage of HIV-1 subtype C in circulation there.

HIV type 1 subtypes in circulation in northern Kenya.

The genetic subtypes of HIV-1 circulating in northern Kenya have not been characterized. Here we report the partial sequencing and analysis of samples collected in the years 2003 and 2004 from 72 HIV-1-positive patients in northern Kenya, which borders Ethiopia, Somalia, and Sudan. From the analysis of partial env sequences, it was determined that 50% were subtype A, 39% subtype C, and 11% subtype D. This shows that in the northern border region of Kenya subtypes A and C are the dominant HIV-1 subtypes in circulation. Ethiopia is dominated mainly by HIV-1 subtype C, which incidentally is the dominant subtype in the town of Moyale, which borders Ethiopia. These results show that cross-border movements play an important role in the circulation of subtypes in Northern Kenya.

A spatial and temporal analysis of safety-belt usage and safety-belt laws.

Safety-belt usage has increased significantly in the US since the introduction of mandatory safety-belt usage laws in the 1980s. This paper analyzes the impact of these laws on increasing safety-belt usage while controlling for other state-specific variables. A fixed effects cross-sectional time-series analyses shows the relative significance of various state-level attributes in explaining safety-belt usage, including whether or not primary or secondary safety-belt laws have been passed. To further explore these relationships we employ spatial analyses techniques and find spatial autocorrelation in the data. Spatial correlation also exhibits a clear east-west direction. When the analyses is further corrected for temporal autocorrelation we find that the spatial autocorrelation is greatly diminished and that many variables lose their statistical significance, though safety-belt laws are still statistically significant. Results suggest that for this data, it is critical to control for temporal autocorrelation while spatial autocorrelation is less important. We also find that our spatial analyses does provide interesting information on similarities between various regions on the effectiveness of safety-belt laws.