In this paper we present an intelligent system to help autonomous vehicles in real cities and with local traffic rules. A 2D and 3D visual attention system is proposed, capable of detecting the use of signs and aids in cases of major roadblock (road under work, with a traffic accident, etc.). For this to be possible, we analyze the cones and traffic signs that usually alert a driver about this type of problem. The main objective is to provide support for autonomous vehicles to be able to find an auxiliary route that is not previously mapped. For this we use a Grid Point Cloud Map. Using the ORB-SLAM visual odometry system we can correctly fit each stereo frame point cloud in the pose where the images were collected. With the concatenation of point clouds generated by the stereo camera, every grid block can draw the main characteristics of its region and an auxiliary route can be mapped. In this type of situation the vision system must work in real time. The results are promising and very satisfactory, we obtained an accuracy of 98.4% in the 2D classification task and 83% accuracy in the single frame 3D detection task.
Due to the large number of activities that must be carried out by emergency-care services (ESs), the tasks of facility managers and architects are challenging and complex. Several strategies, guides, and diagnoses have already been developed in order to improve ESs. Part of the solution to this problem depends on obtaining a normative and universal understanding of the problem, and another part depends on conducting a specific and relational analysis between the environment and the flow of activities that are allocated within it. This paper presents the results of a study that was conducted using a software program that is currently under development for mapping the congruence relationship between activities and environments. Here, we present a discussion of the first results that were obtained with the instrument, which was applied to a single case. For this purpose, the fundamentals of the instrument, as well as the environment and the flows of an ES at a university hospital, are described. The forms of analysis, benefits, and limitations of the instrument were investigated, with a view towards its use in supporting the management and the design of large and complex environments, such as emergency departments. In this program, the relationships that are hidden from the managers, the designers, and the researchers due to the aforementioned complexity are revealed through the use of matrices. This mapping can supplement the decision making of the managers and the designers. The application showed advantages in modeling with fewer inputs, mainly in pre-design evaluations.
This work aims to present an autonomous vehicle navigation system, based on an End-to-End Deep Learning approach, and to study the impact of different image input configurations to the system performance. The proposed methodology in this work was to adoptand test different configurations of RGB and Depth images captured from a Kinect device. We adopted a multi-camera system, composed by 3 cameras, with different RGB and/or Depth input configurations. Two main systems were developed in order to study and validade de different input configurations: the first one based on a realistic simulator and the second one based on a mini-car (small scale vehicle). Starting with the simulations, it was possible to choose the best camera/input configuration, then we validated that using the real vehicle (mini-car) with real sensors/cameras. The experimental results demonstrated that a multi-camera solution, based on 3 cameras, allow us to obtain better autonomous navigation control results in a End-to-End Deep Learning based approch, with a very small final error when using the proposed camera configurations.
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