5/28/2023 0 Comments Webots simulation sound![]() ![]() In Nairobi, GPS location data were collected to generate the first map of the complex Matatu transit network. A case study method was used to consider approaches in Nairobi, Kenya Istanbul, Turkey and Dhaka, Bangladesh. ![]() Data are widely available, but the challenge, as with developed countries, is figuring out how best to use it. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are 57%, 51%, and 61%, respectively, for the aggressive human driver following the CAV if the following vehicle’s intent is considered by a CAV in making a left turn at an intersection.īig data, collected in the form of social media posts and mobile phone location tracking, have great potential to inform and manage the planning and operation of transit networks in developing countries. Based on our simulation study, the situation-aware CAV controller module reduces up to 47% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicle’s intent. Existing literature does not consider the intent of the following vehicle for a CAV’s left-turning movement, and existing CAV controllers do not assess the following non-CAV’s intents. In this paper, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other non-autonomous entities on the road will follow the traffic rules or common driving behavior. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. One challenging aspect of the Connected and Automated Vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. Overall, this pipeline is very capable and can be used to extend existing projects or serve as a platform for new robotics simulation endeavors. The implemented distribution and parallelization are extremely effective, with a 100\% simulation completion rate after 12 hours of runs. Additionally, simulations can be run in sequence, with a batch job being distributed across an arbitrary number of computing nodes and each node having multiple instances running in parallel. We have developed a pipeline capable of running Webots simulations both headlessly and in GUI-enabled mode over an SSH X11 server, with simulation execution occurring remotely on HPC compute nodes. Such a pipeline would allow researchers to generate massive datasets from their simulations, opening the door for potential machine learning applications and decision tool development. For projects that would benefit from an aggregated output dataset from thousands of simulation runs, there is no standard recourse this project sets out to mitigate this by developing a formalized parallel pipeline for running sequences of Webots simulations on powerful HPC resources. Even so, Webots simulations are often run on personal and lab computers. Webots, a state-of-the-art robotics simulator, is often the software of choice for robotics research. In the rapidly evolving and maturing field of robotics, computer simulation has become an invaluable tool in the design process. ![]()
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