NLE-DM : Natural-Language Explanations for Decision Making of Autonomous Driving Based on Semantic Scene Understanding

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)9780-9791
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number9
Online published5 Jun 2023
Publication statusPublished - Sept 2023
Externally publishedYes

Abstract

In recent years, the advancement of deep-learning technologies has greatly promoted the research progress of autonomous driving. However, deep neural network is like a black box. Given a specific input, it is difficult to explain the output of the network. Without explainable results, it would be unsafe to deploy deep networks in unseen environments or environments with potential unexpected situations. Especially for decision-making networks, inappropriate outputs could lead to severe traffic accidents. To provide a solution to this problem, we propose a deep neural network that jointly predicts the decision-making actions and corresponding natural-language explanations based on semantic scene understanding. Two types of explanations, the reasons of driving actions and the surrounding environment descriptions of the ego-vehicle, are designed. Both the reasons and descriptions are in the form of natural language. The decision-making actions could be explained by the corresponding reasons or the environment descriptions. We also release a large-scale dataset with hand-labelled ground truth including driving actions and environment descriptions. The superiority of our network over other methods is demonstrated on both our dataset and a public dataset. © 2023 IEEE.

Research Area(s)

  • Autonomous driving, decision making, explainable artificial intelligence, semantic scene understanding

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