MMD-OPT : Maximum Mean Discrepancy Based Sample Efficient Collision Risk Minimization for Autonomous Driving

• URL: http://arxiv.org/abs/2412.09121v1
• Date: Thu, 12 Dec 2024 09:57:10 GMT
• Title: MMD-OPT : Maximum Mean Discrepancy Based Sample Efficient Collision Risk Minimization for Autonomous Driving
• Authors: Basant Sharma, Arun Kumar Singh,
• Abstract summary: MMD-OPT is a sample-efficient approach for minimizing the risk of collision under arbitrary prediction distribution.<n>We show how these two concepts can be used to define a sample efficient surrogate for collision risk estimate.
• Score: 2.361853878117761
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose MMD-OPT: a sample-efficient approach for minimizing the risk of collision under arbitrary prediction distribution of the dynamic obstacles. MMD-OPT is based on embedding distribution in Reproducing Kernel Hilbert Space (RKHS) and the associated Maximum Mean Discrepancy (MMD). We show how these two concepts can be used to define a sample efficient surrogate for collision risk estimate. We perform extensive simulations to validate the effectiveness of MMD-OPT on both synthetic and real-world datasets. Importantly, we show that trajectory optimization with our MMD-based collision risk surrogate leads to safer trajectories at low sample regimes than popular alternatives based on Conditional Value at Risk (CVaR).

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