Textual Explanations for Self-Driving Vehicles
arXiv:1807.11546
Abstract
Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations which allow end-users to understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. We propose a new approach to introspective explanations which consists of two parts. First, we use a visual (spatial) attention model to train a convolutional network end-to-end from images to the vehicle control commands, i.e., acceleration and change of course. The controller's attention identifies image regions that potentially influence the network's output. Second, we use an attention-based video-to-text model to produce textual explanations of model actions. The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. Finally, we explore a version of our model that generates rationalizations, and compare with introspective explanations on the same video segments. We evaluate these models on a novel driving dataset with ground-truth human explanations, the Berkeley DeepDrive eXplanation (BDD-X) dataset. Code is available at https://github.com/JinkyuKimUCB/explainable-deep-driving.
arXiv commentAccepted to ECCV 2018
References in corpus (7)
- Neural Machine Translation by Jointly Learning to Align and Translate
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
- End to End Learning for Self-Driving Cars
- Sequence to Sequence -- Video to Text
- DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
- Generating Visual Explanations
- Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention