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. In this paper, we propose a new scenario generation pipeline focused on generating scenarios in a specific area near an autonomous vehicle. In this method, a scenario map is generated to define the scenario simulation area. 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. 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. The proposed scenario generation pipeline can generate scenarios containing pedestrians, animals, and vehicles, and, advantageously, no user intervention is required during the simulation. In addition, a virtual environment for autonomous driving is also implemented to test the proposed scenario generation pipeline. 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.

为了开发用于自动驾驶车辆测试的真实模拟器，需要模拟现实世界中车辆附近可能发生的各种场景。在本文中，我们提出了一种新的场景生成管道，专注于在自动驾驶车辆附近的特定区域生成场景。在该方法中，生成场景图来定义场景模拟区域。引入基于卷积神经网络（CNN）的场景代理选择器来评估所选代理是否可以生成现实场景，并且碰撞事件检测器处理碰撞消息以触发事故事件。管道中提出的以事件为中心的动作调度程序使事件附近的代理能够在事件发生在自动驾驶车辆附近时执行相关动作。所提出的场景生成管道可以生成包含行人、动物和车辆的场景，并且有利的是，在模拟过程中不需要用户干预。此外，还实现了自动驾驶虚拟环境来测试所提出的场景生成管道。结果表明，基于 CNN 的场景智能体选择器以 92.67% 的准确度选择了提供真实场景的智能体，而以事件为中心的动作调度器则通过让事件周围的智能体生成相关动作来生成视觉上真实的场景。