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Despite the fact that there being a large literature on simulation, there is as yet no generic paradigm or architecture to develop a three-dimensional (3-D) simulator which depends on autonomous intelligent objects. This has motivated us to introduce a 3-D simulation system based on intelligent objects for Physics Experimentation. We formulated the system’s components as an object-orientation model. So, the entities in every experiment’s work cell are modeled by characterizing their properties and functions into classes and objects of the system hierarchy. Intelligent objects are realized by developing a knowledge base (KB) that captures a set of rules/algorithms that operate on 3-D objects. Rules fall into two categories: action and property rules. In the simulation layer, the student is allowed, by using the virtual system, to stroll throughout the Physics laboratory in light of a walking model. Student gets to a simulation region to do an experiment through the detection of mathematical collision. From software engineering perspective, the proposed system facilitates the Physics experiment through making the specification of its applicable parts more modular and reusable. Moreover, a major pedagogical objective is achieved by permitting the student tuning parameters, fixing component of a device then visualizing outputs. This provides student well interpretation by viewing how distinct parameters affect the outcomes of the experiment. With the objective of student performance measuring, we utilized an exploratory group relying upon pre- and post-testing. The application results demonstrate that the simulator contributes positively to student performance in regard to practical Physics.

The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle).

This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor.

This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification.

The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

The authors will give an overview of the railway market, with a focus on Morocco, before seeing the challenges to face, before listing some benefits of rail links in terms of development, ecology, security, space management, etc. The authors will then give an overview of the development of BIM, its benefits, risks and issues. The purpose of this paper is to verify that the BIM can provide the railway with the tools to face some of its challenges and improve its productivity.

This paper is part of our research project on the integration of BIM in railway, which is the result of a partnership between Colas Rail Maroc and the ENSAK of the Ibn Tofail University of Kenitra. The objective of this paper is mainly to confirm that the integration of BIM with the railway, through a theoretical and practical study, can have positive impacts. To do this, our methodology consists in studying briefly the development of the railway, the need to improve the budgets and schedules of the projects, to increase the productivity, before showing the advantages of the BIM in the sector of the Architecture, Engineering and Construction (AEC). The study of feedback from railway projects (chosen for their date of completion – beyond 2014, their size, their geographical situation in several countries and for the availability of literature in a new field) will confirm the initial hypotheses. Among the projects studied will be a project that has been the subject of an article written by the authors of this paper. In the discussion of the results, the authors will focus on the benefits, risks and limitations of integrating BIM into the railway. In conclusion, the authors are laying the groundwork for future research in the field.

The cases study discussed in this paper and previous research confirms the hypotheses of the literature. The integration of BIM into railway projects can have several advantages: collaboration, time saving, cost optimization, prevention of conflicts between networks, construction before construction, optimization of facility management, improvement of the quality of works, prefabrication. They also allowed us to illustrate the risks (status and appropriation of the BIM model, lack of standardization of versions or software and lack of understanding of the basics of schedules and specifications) and limitations (lack of feedback, lack of adaptability and convergence of tools). These experiences have also shown that the use of BIM is not just a technological transition, but a revolution in the project management process, which requires several key success factors (participation of all, commitment, change management and adoption of the collaborative approach). Visualization, collaboration and conflict elimination are the three main chapters where the benefits of BIM can be organized. In fact, there is a lot of intersection between these chapters, but they have been chosen as the main ideas around which all the benefits can be better understood. Visualization primarily addresses the benefits to an individual and improving one’s personal understanding as a result of using BIM. The collaboration refers to the cooperative action of several team members, which is encouraged and facilitated by BIM. Conflict elimination mainly concerns project-related benefits, such as conflict reduction, waste, risks, costs and time. For railway infrastructure projects, the main purpose of using BIM is to improve the design integration process, internal project team communication and collision detection to eliminate risk of rehabilitation.

The application of the BIM process in railway infrastructure requires constant improvement. This concerns the development of libraries and the models available to all users in order to encourage the development of this methodology and, consequently, its use of information throughout the life cycle of an infrastructure work.

The case study of real projects incorporating BIM confirms the results of the literature review. The benefits of integrating BIM into rail projects are multiple and proven: cost control, decision support, avoids extra work due to design errors, improves detection of interface problems, improves planning of vision, help with prefabrication and facility management, etc. Finally, the BIM process is able to overcome delays in procedures slowing the development of the construction industry in many countries, especially in Morocco, because of the slowness of design (or downright bad design).

The integration of BIM into rail is becoming a global trend. This integration requires government decisions and a maturation of technology and tools. The authorities of some developed countries studied (Sweden, UK, France, Germany) in the railways, at different stages of implementation, are adopting BIM in the process of setting up new railway projects. This political impulse is still behind in southern countries, such as Morocco. The trend and the data collected indicate an adoption between 2020 and 2030 of BIM in all/some AEC projects in developed countries. This will have an impact on other countries that will soon be doing the same, especially in the railway sector to adopt the BIM.

As part of the realization of this paper, we proceeded to the implementation of an electrical substation as part of the project to build 40 electric traction substations built by Colas Rail on behalf of ONCF.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.