CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model

In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers

Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving

Autonomous vehicles and smart roads are not new concepts and the undergoing development to empower the vehicles for higher levels of automation has achieved initial milestones. However, the transportation industry and relevant research communities still require making considerable efforts to create smart and intelligent roads for autonomous driving. To achieve the results of such efforts, the CCAM infrastructure is a game changer and plays a key role in achieving higher levels of autonomous driving. In this paper, we present a smart infrastructure and autonomous driving capabilities enhanced by CCAM infrastructure. Meaning thereby, we lay down the technical requirements of the CCAM infrastructure: identify the right set of the sensory infrastructure, their interfacing, integration platform, and necessary communication interfaces to be interconnected with upstream and downstream solution components. Then, we parameterize the road and network infrastructures (and automated vehicles) to be advanced and evaluated during the research work, under the very distinct scenarios and conditions. For validation, we demonstrate the machine learning algorithms in mobility applications such as traffic flow and mobile communication demands. Consequently, we train multiple linear regression models and achieve accuracy of over 94% for predicting aforementioned demands on a daily basis. This research therefore equips the readers with relevant technical information required for enhancing CCAM infrastructure. It also encourages and guides the relevant research communities to implement the CCAM infrastructure towards creating smart and intelligent roads for autonomous driving

LEARN: A multi-centre, cross-sectional evaluation of Urology teaching in UK medical schools

OBJECTIVE: To evaluate the status of UK undergraduate urology teaching against the British Association of Urological Surgeons (BAUS) Undergraduate Syllabus for Urology. Secondary objectives included evaluating the type and quantity of teaching provided, the reported performance rate of General Medical Council (GMC)-mandated urological procedures, and the proportion of undergraduates considering urology as a career. MATERIALS AND METHODS: LEARN was a national multicentre cross-sectional study. Year 2 to Year 5 medical students and FY1 doctors were invited to complete a survey between 3rd October and 20th December 2020, retrospectively assessing the urology teaching received to date. Results are reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). RESULTS: 7,063/8,346 (84.6%) responses from all 39 UK medical schools were included; 1,127/7,063 (16.0%) were from Foundation Year (FY) 1 doctors, who reported that the most frequently taught topics in undergraduate training were on urinary tract infection (96.5%), acute kidney injury (95.9%) and haematuria (94.4%). The most infrequently taught topics were male urinary incontinence (59.4%), male infertility (52.4%) and erectile dysfunction (43.8%). Male and female catheterisation on patients as undergraduates was performed by 92.1% and 73.0% of FY1 doctors respectively, and 16.9% had considered a career in urology. Theory based teaching was mainly prevalent in the early years of medical school, with clinical skills teaching, and clinical placements in the later years of medical school. 20.1% of FY1 doctors reported no undergraduate clinical attachment in urology. CONCLUSION: LEARN is the largest ever evaluation of undergraduate urology teaching. In the UK, teaching seemed satisfactory as evaluated by the BAUS undergraduate syllabus. However, many students report having no clinical attachments in Urology and some newly qualified doctors report never having inserted a catheter, which is a GMC mandated requirement. We recommend a greater emphasis on undergraduate clinical exposure to urology and stricter adherence to GMC mandated procedures

Newspaper Representation of Transgender in Pakistan: A Multi-Modal Analysis

The aim of the research was to analyze the media visibility of transgenders. The study was conducted via a multi-modal analysis of photographs of trans-genders published in two leading dailies of Pakistan. The analysis was conducted on two levels according to the meta-functions of the visual design as proposed by Kress & van Leeuwen (1996), which are ideational and interpersonal.  On the ideational level, five themes emerged which were categorized as images showing confidence, glamour, deprivation, protest, and harassment. The number of images with the themes of confidence and glamour was significantly more in number in comparison to the themes of deprivation, harassment, and protests which were significantly fewer in number although they bear the closest affinity to the everyday lives of the subjects. On the interpersonal level, it is interesting that most of the photographs maintained direct eye contact between the subject and the viewer and thus create an interpersonal bond. A major anomaly that was observed referred to the absolute exclusion of female to male transgenders or trans-males, which is another case of symbolic annihilation

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges

Shape preserving rational cubic spline for positive and convex data

In this paper, the problem of shape preserving C2 rational cubic spline has been proposed. The shapes of the positive and convex data are under discussion of the proposed spline solutions. A C2 rational cubic function with two families of free parameters has been introduced to attain the C2 positive curves from positive data and C2 convex curves from convex data. Simple data dependent constraints are derived on free parameters in the description of rational cubic function to obtain the desired shape of the data. The rational cubic schemes have unique representations

Shape Preserving Positive and Convex Data Visualization using Rational Bi-cubic Functions

This paper is concerned with the problem of positive and convex data visualization in the form of positive and convex surfaces. A rational bi-cubic partially blended function with eight free parameters in its description is introduced and applied to visualize the shape of positive data and convex data. The developed schemes in this paper have unique representations. Visual models of surfaces attain  smoothness. &nbsp

Nexus of knowledge management and organizational performance : a cross-country study of China and Pakistan higher educational institutes

202211 bcchNot applicableSelf-fundedEarly releaseUpon official publicatio