The accelerated development of AI technology has brought about revolutionary changes in various fields of society. Recently, it has been emphasized that Fairness, Accountability, Transparency, Ethics (Explainability) (FATE) should be considered to support the reliability and validity of AI-based decision-making. However, in the case of autonomous driving technology, which is directly related to human life and requires real-time adaptation and response to various changes and risks in the real world, environmental adaptability must be considered in a more comprehensive and converged manner. In order to derive definitive evidence for each object in a convergent autonomous driving environment, it is necessary to transparently collect and provide various types of road environment information for driving objects and driving assistance, and to construct driving technology that is adaptable to various situations by considering all uncertainties in the real-time changing driving environment.This allows unbiased and fair results based on flexible contextual understanding, even in situations that do not conform to rules and patterns, by considering the convergent interactions and dynamic situations of various objects that are possible in a real-time road environment. The transparent, environmentally adaptive, and fairness-based outcomes provide the basis for the decision-making process and support clear interpretation and explainability of decisions. All of these processes enable autonomous vehicles to draw reliable conclusions and take responsibility for their decisions in autonomous driving situations. Therefore, this paper proposes an Adaptability, Fairness, Transparency, Explainability and Accountability (AFTEA) Framework to build a stable and reliable autonomous driving environment in dynamic situations. This paper explains the definition, role, and necessity of AFTEA in artificial intelligence technology and highlights its value when applied and integrated into autonomous driving technology. The AFTEA framework with environmental adaptability will support the establishment of a sustainable autonomous driving environment in dynamic environments, and aims to provide a direction for establishing a stable and reliable AI system that adapts to various real-world scenarios.

Autonomous vehicles make decisions and controls based on various object recognition results. The driving environment is characterized by the coexistence of a multitude of objects of varying shapes and sizes. Therefore, the ability to accurately recognise fine-grained objects is essential for accurate object recognition in a variety of changing situations. For object detection, the autonomous vehicle performs bounding box and segmentation to provide the detected object information. However, bounding box and segmentation based object detection has difficulties in identifying objects with complex shapes, small or distant objects, and it is hard to distinguish and detect objects with similar colors to the background or similar colors and textures to surrounding objects. This has limitations for reliable object identification in autonomous driving environments containing a variety of objects, which is a challenge for clear criteria-based object avoidance and collision protection. To overcome these limitations, this paper proposes Edge-Adaptive Depth Estimation(EADE). EADE, the combination of edge extraction and depth estimation, enables detailed edge extraction and partial distance estimation of objects even in environments where object shape and size, surrounding objects, and backgrounds make it difficult to recognise distinct objects, which allows for reliable autonomous decision-making and control based on detailed object collision and avoidance criteria. To validate EADE, experiments were conducted with real-world driving environment image data. The results of EADE demonstrate that detailed object recognition is possible with clear edge recognition and estimation of object distance, even for complex shaped objects such as trees with branches in multiple directions, distant objects, and objects that are difficult to distinguish from the background such as curbs.

The accelerated development of AI technology has brought about revolutionary changes in various fields of society. Recently, it has been emphasized that fairness, accountability, transparency, and explainability (FATE) should be considered to support the reliability and validity of AI-based decision-making. However, in the case of autonomous driving technology, which is directly related to human life and requires real-time adaptation and response to various changes and risks in the real world, environmental adaptability must be considered in a more comprehensive and converged manner. In order to derive definitive evidence for each object in a convergent autonomous driving environment, it is necessary to transparently collect and provide various types of road environment information for driving objects and driving assistance and to construct driving technology that is adaptable to various situations by considering all uncertainties in the real-time changing driving environment. This allows for unbiased and fair results based on flexible contextual understanding, even in situations that do not conform to rules and patterns, by considering the convergent interactions and dynamic situations of various objects that are possible in a real-time road environment. The transparent, environmentally adaptive, and fairness-based outcomes provide the basis for the decision-making process and support clear interpretation and explainability of decisions. All of these processes enable autonomous vehicles to draw reliable conclusions and take responsibility for their decisions in autonomous driving situations. Therefore, this paper proposes an adaptability, fairness, transparency, explainability, and accountability (AFTEA) framework to build a stable and reliable autonomous driving environment in dynamic situations. This paper explains the definition, role, and necessity of AFTEA in artificial intelligence technology and highlights its value when applied and integrated into autonomous driving technology. The AFTEA framework with environmental adaptability will support the establishment of a sustainable autonomous driving environment in dynamic environments and aims to provide a direction for establishing a stable and reliable AI system that adapts to various real-world scenarios.