Current validation methods often rely on recorded data and basic functional checks, which may not be sufficient to encompass the scenarios an autonomous vehicle might encounter. In addition, there is a growing need for complex scenarios with changing vehicle interactions for comprehensive validation. This work introduces a novel synchronous multi-agent simulation framework for autonomous vehicles in interactive scenarios. Our approach creates an interactive scenario and incorporates publicly available edge-case scenarios wherein simulated vehicles are replaced by agents navigating to predefined destinations. We provide a platform that enables the integration of different autonomous driving planning methodologies and includes a set of evaluation metrics to assess autonomous driving behavior. Our study explores different planning setups and adjusts simulation complexity to test the framework's adaptability and performance. Results highlight the critical role of simulating vehicle interactions to enhance autonomous driving systems. Our setup offers unique insights for developing advanced algorithms for complex driving tasks to accelerate future investigations and developments in this field. The multi-agent simulation framework is available as open-source software: https://github.com/TUM-AVS/Frenetix-Motion-Planner

翻译：当前验证方法往往依赖于记录数据与基础功能检查，可能不足以覆盖自动驾驶车辆可能遇到的全部场景。此外，为开展全面验证，对具有动态车辆交互的复杂场景需求日益增长。本研究提出一种面向交互式场景的新型同步多智能体仿真框架。该方法构建交互场景，并整合公开的边缘案例场景，其中模拟车辆被替换为导航至预设目的地的智能体。本平台支持集成不同自动驾驶规划方法论，并包含一套评估自动驾驶行为的评价指标。我们通过探索不同规划设置，调整仿真复杂度以检验框架的适应性与性能。结果表明，模拟车辆交互对增强自动驾驶系统具有关键作用。本实验设置为开发复杂驾驶任务的高级算法提供了独特见解，可加速该领域的后续研究与发展。该多智能体仿真框架已以开源形式发布：https://github.com/TUM-AVS/Frenetix-Motion-Planner