论文标题
驾驶行为用多层次融合说明
Driving Behavior Explanation with Multi-level Fusion
论文作者
论文摘要
在这个自动驾驶汽车积极发展的时代，为驾驶系统提供解释其决定的能力至关重要。在这项工作中，我们专注于在车辆驾驶时产生高级驾驶解释。我们介绍牛肉，用于用融合的行为解释，这是一种深层建筑，解释了轨迹预测模型的行为。通过对人类驾驶决策依据的注释监督，牛肉学会了从多个层面融合特征。利用多模式融合文献的最新进展，牛肉经过精心设计，以模拟高级决策特征和中级知觉特征之间的相关性。通过在HDD和BDD-X数据集上进行的大量实验，我们的方法的灵活性和效率得到了验证。
In this era of active development of autonomous vehicles, it becomes crucial to provide driving systems with the capacity to explain their decisions. In this work, we focus on generating high-level driving explanations as the vehicle drives. We present BEEF, for BEhavior Explanation with Fusion, a deep architecture which explains the behavior of a trajectory prediction model. Supervised by annotations of human driving decisions justifications, BEEF learns to fuse features from multiple levels. Leveraging recent advances in the multi-modal fusion literature, BEEF is carefully designed to model the correlations between high-level decisions features and mid-level perceptual features. The flexibility and efficiency of our approach are validated with extensive experiments on the HDD and BDD-X datasets.
