RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts
<|reference_start|>RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts: Modern motion planners for autonomous driving frequently use imitation learning (IL) to draw from expert driving logs. Although IL benefits from its ability to glean nuanced and multi-modal human driving behaviors from large datasets, the resulting planners often struggle with out-of-distribution (OOD) scenarios and with traffic rule compliance. On the other hand, classical rule-based planners, by design, can generate safe traffic rule compliant behaviors while being robust to OOD scenarios, but these planners fail to capture nuances in agent-to-agent interactions and human drivers' intent. RuleFuser, an evidential framework, combines IL planners with classical rule-based planners to draw on the complementary benefits of both, thereby striking a balance between imitation and safety.   Our approach, tested on the real-world nuPlan dataset, combines the IL planner's high performance in in-distribution (ID) scenarios with the rule-based planners' enhanced safety in out-of-distribution (OOD) scenarios, achieving a 38.43% average improvement on safety metrics over the IL planner without much detriment to imitation metrics in OOD scenarios.<|reference_end|>
@article{patrikar2024rulefuser:,
    title={RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation
  Learned Planners and Predictors for Robustness under Distribution Shifts},
    author={Jay Patrikar, Sushant Veer, Apoorva Sharma, Marco Pavone and Sebastian
  Scherer},
    journal={arXiv preprint arXiv:2405.11139},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11139},
    primaryClass={cs.RO cs.AI cs.LG}
}