We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mh0797/interplan | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| create_modifications_dictionary | Ran | mh0797/interplan/interplan/planning/utils/modifications_preprocessing.py code served (permissive licence) · get_code("0593708b8f1b1924") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2404.07569")
get_code_for_paper("2404.07569")
have("2404.07569")
Connect an agent — have() is free.