Predicting and planning interactive behaviors in complex traffic situations presents a challenging task. Especially in scenarios involving multiple traffic participants that interact densely, autonomous vehicles still struggle to interpret situations and to eventually achieve their own mission goal. As driving tests are costly and challenging scenarios are hard to find and reproduce, simulation is widely used to develop, test, and benchmark behavior models. However, most simulations rely on datasets and simplistic behavior models for traffic participants and do not cover the full variety of real-world, interactive human behaviors. In this work, we introduce BARK, an open-source behavior benchmarking environment designed to mitigate the shortcomings stated above. In BARK, behavior models are (re-)used for planning, prediction, and simulation. A range of models is currently available, such as Monte-Carlo Tree Search and Reinforcement Learning-based behavior models. We use a public dataset and sampling-based scenario generation to show the inter-exchangeability of behavior models in BARK. We evaluate how well the models used cope with interactions and how robust they are towards exchanging behavior models. Our evaluation shows that BARK provides a suitable framework for a systematic development of behavior models.

中文翻译：

---

BARK：多代理环境中的开放行为基准测试

在复杂交通情况下预测和规划交互行为是一项具有挑战性的任务。特别是在涉及多个交通参与者密集交互的场景中，自动驾驶汽车仍然难以解释情况并最终实现自己的任务目标。由于驾驶测试成本高昂且难以找到和重现具有挑战性的场景，因此模拟被广泛用于开发、测试和基准行为模型。然而，大多数模拟依赖于交通参与者的数据集和简单化的行为模型，并没有涵盖现实世界中各种交互式人类行为。在这项工作中，我们介绍了 BARK，这是一种开源行为基准测试环境，旨在减轻上述缺点。在 BARK 中，行为模型被（重新）用于规划、预测和模拟。目前有一系列模型可用，例如蒙特卡洛树搜索和基于强化学习的行为模型。我们使用公共数据集和基于抽样的场景生成来展示 BARK 中行为模型的可互换性。我们评估所使用的模型处理交互的程度以及它们对交换行为模型的鲁棒性。我们的评估表明，BARK 为行为模型的系统开发提供了合适的框架。