Driving behavior explanation with multi-level fusion

作者：

Highlights：

• A deep architecture, called BEEF, explains the behavior of a trajectory prediction model, in an online fashion.

• The core module discovers fine-grained correlation between high-level decision features and intermediate perceptual features, with a BLOCK fusion module.

• The global approach is flexible with respect to various settings (online/offline explanations, cause classification / natural language justification).

摘要

•A deep architecture, called BEEF, explains the behavior of a trajectory prediction model, in an online fashion.•The core module discovers fine-grained correlation between high-level decision features and intermediate perceptual features, with a BLOCK fusion module.•The global approach is flexible with respect to various settings (online/offline explanations, cause classification / natural language justification).

论文关键词：Explainable self-driving,Multi-level fusion,Cause classification,Natural language explanations,HDD,BDD-X

论文评审过程：Received 16 February 2021, Revised 22 October 2021, Accepted 4 November 2021, Available online 6 November 2021, Version of Record 12 November 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108421