Real-world autonomous driving systems must make safe decisions in the face of
rare and diverse traffic scenarios. Current state-of-the-art planners are
mostly evaluated on real-world datasets like nuScenes (open-loop) or nuPlan
(closed-loop). In particular, nuPlan seems to be an expressive evaluation
method since it is based on real-world data and closed-loop, yet it mostly
covers basic driving scenarios. This makes it difficult to judge a planner's
capabilities to generalize to rarely-seen situations. Therefore, we propose a
novel closed-loop benchmark interPlan containing several edge cases and
challenging driving scenarios. We assess existing state-of-the-art planners on
our benchmark and show that neither rule-based nor learning-based planners can
safely navigate the interPlan scenarios.
A recently evolving direction is the usage of foundation models like large
language models (LLM) to handle generalization. We evaluate an LLM-only planner
and introduce a novel hybrid planner that combines an LLM-based behavior
planner with a rule-based motion planner that achieves state-of-the-art
performance on our benchmark.

通过一个新的闭环评估基准测试 interPlan，本文评估了现有的最先进的规则和学习驱动的规划器，并展示了它们都无法安全地导航 interPlan 场景。同时，本文还引入了一种基于大型语言模型 (LLM) 的行为规划器和基于规则的运动规划器的混合规划器，该混合规划器在我们的基准测试上达到了最先进的性能。

车辆运动规划能否泛化到现实世界长尾情景？

Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?

We propose a new benchmark for planning tasks based on the Minecraft game.
Our benchmark contains 45 tasks overall, but also provides support for creating
both propositional and numeric instances of new Minecraft tasks automatically.
We benchmark numeric and propositional planning systems on these tasks, with
results demonstrating that state-of-the-art planners are currently incapable of
dealing with many of the challenges advanced by our new benchmark, such as
scaling to instances with thousands of objects. Based on these results, we
identify areas of improvement for future planners. Our framework is made
available at this https URL

我们基于 Minecraft 游戏提出了一个新的规划任务基准，其中包含 45 个任务，并且能够自动地创建新的 Minecraft 任务的命题和数值实例。我们基于这些任务对数值和命题规划系统进行基准测试，结果表明目前的先进规划器无法处理我们的新基准提出的许多挑战，例如应对具有数千个对象的实例。根据这些结果，我们确定了未来规划器的改进方向。我们的框架已经在指定的 URL 上提供。