Textual Explanations for Self-Driving Vehicles (1807.11546v1)

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.

An Examination of Textual Explanations for Autonomous Vehicle Decision-Making

The paper "Textual Explanations for Self-Driving Vehicles" by Jinkyu Kim et al. explores a methodical approach to augment the interpretability of deep neural networks used in autonomous vehicles through the provision of natural language explanations. The paper aims to dispel the black-box nature of deep neural models by converting their internal state outputs into human-understandable, textual explanations. This work is not just an exploration of integrating user-centric design in AI, but also a potential leap forward in aligning machine actions with human cognition to foster trust and comprehension.

Methodological Overview

The authors introduce a novel system comprising an end-to-end trainable model that unifies image-based vehicle control prediction with a textual explanation generation process. The system leverages attention mechanisms, both visual and textual, to ensure that the explanations provided are rooted in the system's control decisions. Two explanation paradigms are explored: introspective explanations, which are grounded in the model's internal causal understanding, and rationalizations, which are post-hoc justifications based on model outputs.

1. Vehicle Controller Model: The core of the system is a neural network that processes sequential dashcam images to predict vehicular actions. This model utilizes an attention mechanism to highlight image regions that influence its control outputs, such as acceleration or steering changes.
2. Textual Explanation Generator: This component converts the processed visual data and corresponding control actions into coherent textual sentences. Notably, the textual generator aligns its attention mechanism with that of the vehicle controller, ensuring that the grounding of explanations is consistent with the internal state that guided the control decision.
3. Attention Alignment Approaches: The paper investigates varying alignment strategies between the attention mechanisms of the control and explanation models. The distinction between strong and weak alignment is pivotal in determining how well the textual outputs reflect the pertinent aspects of the driving context.

Evaluation and Results

The system's efficacy is appraised using the Berkeley DeepDrive eXplanation (BDD-X) dataset, specifically curated for this research and comprising rich annotations of driving scenes along with human rationalizations. The authors report a significant correlation between well-aligned explanation models and ground-truth human annotations, establishing the viability of attention-aligned explanations over mere rationalizations achieved without alignment. Noteworthy is the improvement in traditional metaphor-based automation metrics like BLEU, METEOR, and CIDEr-D, juxtaposed with enhanced human evaluative assessments.

Implications and Future Directions

From a practical standpoint, the introduction of explanations in autonomous driving systems can considerably improve user trust and safety by enhancing system transparency. Theoretical implications extend towards the development of AI models that are not only robust in decision accuracy but also communicative regarding their decision processes.

The research draws a trajectory towards more sophisticated forms of human-AI interaction where interpretability becomes a core component of AI system design. The exploration into causal filtering and more grounded visual language associations proposes a future where autonomous systems could achieve more nuanced interactions, incorporating rich perceptual data with human-like reasoning.

Conclusion

In conclusion, the presented research underscores a critical advancement in the field of intelligent transportation systems by marrying deep learning with explainability. As the discourse around AI ethics and transparency grows, contributions like this will be indispensable in ensuring that AI technologies evolve in a manner that is both effective and accountable. The techniques and findings presented not only push the boundaries of what autonomous systems can achieve but also reflect a conscientious stride towards harmonizing machine intelligence with human intuition.