11/5/2022 0 Comments Train simulator 2014 crash![]() ![]() Despite the fact these pipelines still present myriad scenarios with unsolved issues, the advent of Deep Learning, supported by the breakthroughs in Computer Vision, accumulated knowledge in vehicle dynamics and availability of new sensor modalities, datasets and hyper-realistic simulators have catalyzed AD research and industrial implementation, representing one of the most challenging engineering tasks of our era. ![]() #Train simulator 2014 crash driver#On top of that, the National Highway Traffic Safety Administration (NHTSA), US agency responsible of reducing vehicle-related crashes related to transportation safety, stated that human beings are involved in 94 % of road accidents, in which an inadequate velocity, driver distraction or factors inherent to age, such as lack of sight and reflexes or joint mobility.Ĭonsidering this, Autonomous Driving (AD) have held the attention of technology enthusiasts and futurists for some time, evidenced by the continuous research and development in this field of study over the past decades, being one of the emerging technologies of the Fourth Industrial Revolution, and particularly of the Industry 4.0. However, only in 2014 more than 25,700 people died on the EU roads, many of them caused by an improper behaviour of the driver on the road. In that sense, regarding safety, it set the ambitious goal of halving the overall number of road deaths in the EU between 20. Aware of this problem, the European Commission published the Transport White Paper in 2011 indicating that new forms of mobility ought to be proposed to provide sustainable solutions for goods and people safety. Some qualitative (video files: Simulation Use Cases) and quantitative (linear velocity and trajectory splitted in the corresponding HIBPN states) results are presented for each use case, as well as an analysis of the temporal graphs associated to the Vulnerable Road Users (VRU) cases, validating our architecture in simulation as a preliminary stage before implementing it in our real autonomous electric car.Īccording to the World Health Organization, nearly one third of the world population will live in cities by 2030, leading to an overpopulation in most of them. Finally, the architecture is validated using some challenging driving scenarios such as Pedestrian Crossing, Stop, Adaptive Cruise Control (ACC) and Unexpected Pedestrian. Third, the CARLA simulator is described, outlining the steps carried out to merge our architecture with the simulator and the advantages to create ad-hoc driving scenarios for use cases validation instead of just modular evaluation. Second, our pipeline is introduced, which exploits the concepts of standard communication in robotics using the Robot Operating System (ROS) and the Docker approach to provide the system with isolation, flexibility and portability, describing the main modules and approaches to perform the navigation. First, the paper states the importance of using hyper-realistic simulators, as a preliminary help to real test, as well as an appropriate design of the traffic scenarios as the two current keys to build safe and robust AD technology. In this paper we present a validation of our fully-autonomous driving architecture using the NHTSA (National Highway Traffic Safety Administration) protocol in the CARLA simulator, focusing on the analysis of our decision-making module, based on Hierarchical Interpreted Binary Petri Nets (HIBPN). In that sense, an AD pipeline should be tested in countless environments and scenarios, escalating the cost and development time exponentially with a physical approach. Urban complex scenarios are the most challenging situations in the field of Autonomous Driving (AD). ![]()
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