RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts

• URL: http://arxiv.org/abs/2405.11139v3
• Date: Mon, 16 Sep 2024 18:44:47 GMT
• Title: RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts
• Authors: Jay Patrikar, Sushant Veer, Apoorva Sharma, Marco Pavone, Sebastian Scherer,
• Abstract summary: RuleFuser combines IL planners with classical rule-based planners to draw on the complementary benefits of both. Our approach, tested on the real-world nuPlan dataset, achieves a 38.43% average improvement on safety metrics over the IL planner.
• Score: 20.405998427564764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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