Testing of function safety and Safety Of The Intended Functionality (SOTIF) is important for autonomous vehicles (AVs). It is hard to test the AV's hazard response in the real world because it would involve hazards to passengers and other road users. This paper studied on virtual testing of AV on the CARLA platform and proposed a Humanoid Scenario Generation (HSG) scheme to investigate the impacts of hazardous behaviors on AV collision rates. The HSG scheme breakthrough the current limitation on the rarity and reproducibility of real scenes. By accurately capturing five prominent human driver behaviors that directly contribute to vehicle collisions in the real world, the methodology significantly enhances the realism and diversity of the simulation, as evidenced by collision rate statistics across various traffic scenarios. Thus, the modular framework allows for customization, and its seamless integration within the CARLA platform ensures compatibility with existing tools. Ultimately, the comparison results demonstrate that all vehicles that exhibited hazardous behaviors followed the predefined random speed distribution and the effectiveness of the HSG was validated by the distinct characteristics displayed by these behaviors.

翻译：功能安全及预期功能安全（SOTIF）测试对自动驾驶车辆（AV）至关重要。在真实世界中测试AV的危险响应具有挑战性，因为这会涉及乘客及其他道路使用者的安全风险。本文基于CARLA平台对AV的虚拟测试进行研究，提出一种人形场景生成（HSG）方案，用于探索危险行为对AV碰撞率的影响。HSG方案突破了当前真实场景具有稀缺性和不可复现性的限制。通过精确捕捉真实世界中直接导致车辆碰撞的五种典型驾驶员行为，该方法显著提升了仿真的真实性与多样性，这在多种交通场景的碰撞率统计数据中得到了验证。该模块化框架支持定制化配置，且其与CARLA平台的无缝集成确保了对现有工具的兼容性。最终，对比结果表明，所有呈现危险行为的车辆均遵循预定义的随机速度分布，且这些行为表现出的独特特征验证了HSG的有效性。