Explanations in Autonomous Driving: A Survey
By Daniel Omeiza, Helena Webb, Marina Jirotka, Lars Kunze. IEEE transactions on intelligent transportation systems (Print), 2021
340 citations recorded by the source corpus.
The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environment and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs’ behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have ‘seen’, done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing work in explainable autonomous driving. First, we open by providing a motivation for explanations by highlighting the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their AV explanation requirements. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, vehicle control, and system management). Finally, we discuss pertinent challenges and provide recommendations including a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explanation provisions in autonomous driving.
Subfields: Artificial Intelligence, Safety Research
View Paper: https://www.semanticscholar.org/paper/d9c2d2f200d3c12991f567309a04462f3ce90725
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In Artificial Intelligence
Citations in the corpus
- Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving (2023, background): “Such issues persist even though there have been some strides towards addressing them [7].”
- Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, neutral): “All the surveys are categorized into several sub-sections, including the overall [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], localization [14], [15], [16], [17], [18], [19],”