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.

翻译：深度学习车辆控制器预测的自然语言解释对于提升透明度和便于审计至关重要。本研究在新型Sense-Assess-eXplain (SAX)框架上，对当前最先进的预测与解释模型进行了全面评估与验证（作为基准）。此外，我们开发了一种新的解释器模型，通过两项改进超越了基线架构：(i) 词性预测的集成，(ii) 特殊标记惩罚机制的引入。在BDD-X数据集上，我们的解释生成技术在BLEU指标上比现有最优方法提升了7.7倍，描述生成技术亦提升了1.3倍。因此，本研究为未来可解释自动驾驶汽车的实现做出了贡献。