The development of autonomous vehicles in the real world faces many problems, such as bad weather and difficulties in data collection. The first thing to consider when developing human-centric autonomous vehicles is safety. The results show that the CNN-based scenario agent selector chose agents that provided realistic scenarios with 92.67% accuracy, and the event-centric action dispatcher generated a visually realistic scenario by letting the agents surrounding the event generate related actions.Īutonomous driving has been a hot research topic since the end of the last century because it promises many benefits, such as increased safety, reduced traffic congestion, and time savings. In addition, a virtual environment for autonomous driving is also implemented to test the proposed scenario generation pipeline. The proposed scenario generation pipeline can generate scenarios containing pedestrians, animals, and vehicles, and, advantageously, no user intervention is required during the simulation. The proposed event-centric action dispatcher in the pipeline enables agents near events to perform related actions when the events occur near the autonomous vehicle. A convolutional neural network (CNN)-based scenario agent selector is introduced to evaluate whether the selected agents can generate a realistic scenario, and a collision event detector handles the collision message to trigger an accident event. In this method, a scenario map is generated to define the scenario simulation area. In this paper, we propose a new scenario generation pipeline focused on generating scenarios in a specific area near an autonomous vehicle.
To develop a realistic simulator for autonomous vehicle testing, the simulation of various scenarios that may occur near vehicles in the real world is necessary.
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