Simulation is the key to scaling up validation and verification for robotic systems such as autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a critical gap remains in simulating realistic behaviors of road users. This is because, unlike simulating physics and graphics, devising first principle models for human-like behaviors is generally infeasible. In this work, we take a data-driven approach and propose a method that can learn to generate traffic behaviors from real-world driving logs. The method achieves high sample efficiency and behavior diversity by exploiting the bi-level hierarchy of driving behaviors by decoupling the traffic simulation problem into high-level intent inference and low-level driving behavior imitation. The method also incorporates a planning module to obtain stable long-horizon behaviors. We empirically validate our method, named Bi-level Imitation for Traffic Simulation (BITS), with scenarios from two large-scale driving datasets and show that BITS achieves balanced traffic simulation performance in realism, diversity, and long-horizon stability. We also explore ways to evaluate behavior realism and introduce a suite of evaluation metrics for traffic simulation. Finally, as part of our core contributions, we develop and open source a software tool that unifies data formats across different driving datasets and converts scenes from existing datasets into interactive simulation environments. For additional information and videos, see https://sites.google.com/view/nvr-bits2022/home
翻译:模拟是扩大自动车辆等机器人系统验证和核查的关键。 尽管在高不洁物理和感官模拟方面有所进步,但在模拟道路使用者的现实行为方面仍然存在着重大差距。 这是因为,与模拟物理和图形不同,为类似人类的行为设计第一个原则模型一般是行不通的。 在这项工作中,我们采用数据驱动方法,并提议一种方法,从现实世界驾驶日志中学习生成交通行为。 这种方法通过将交通模拟问题分解为高水平意图推断和低水平驾驶行为模拟,实现了高抽样效率和行为多样性。 这是因为,与模拟物理和图形不同,这种方法还包含一个规划模块,以获得稳定的长正弦行为模式。 我们从经验上验证了我们的方法,名为双级交通模拟(BITS),从两个大规模驱动数据集的情景中学习生成交通行为行为。 这种方法通过在现实、多样性和长正弦稳定性中利用双级交通模拟表现,实现了交通模拟行为双级等级。 我们还探索如何评估行为现实现实/低级模拟问题,并引入了我们当前数据模拟的模型的套件。