Textual explanations for automated commentary driving

Conference item

The provision of natural language explanations for the predictions of deep-learning-based vehicle controllers is critical as it enhances transparency and easy audit. In this work, a state-of-the-art (SOTA) prediction and explanation model is thoroughly evaluated and validated (as a benchmark) on the new Sense–Assess–eXplain (SAX). Additionally, we developed a new explainer model that improved over the baseline architecture in two ways: (i) an integration of part of speech prediction and (ii) an introduction of special token penalties. On the BLEU metric, our explanation generation technique outperformed SOTA by a factor of 7.7 when applied on the BDD-X dataset. The description generation technique is also improved by a factor of 1.3. Hence, our work contributes to the realisation of future explainable autonomous vehicles.

Authors: Kühn, MA; Omeiza, DA; Kunze, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 2023 IEEE Intelligent Vehicles Symposium (IV)
• Publication date: 2023-07-27
• Event title: IEEE Symposium on Intelligent Vehicle
• Event location: Anchorage, Alaska, USA
• Event website: https://2023.ieee-iv.org/
• Event start date: 2023-06-04
• Event end date: 2023-06-07
• DOI: 10.1109/iv55152.2023.10186611
• Language: English
• Keywords: advanced driver assistance systems; deep learning; FFR; natural language explanations; automated vehicles