A-Eye: Driving with the Eyes of AI for Corner Case Generation

Kowol, Kamil; Bracke, Stefan; Gottschalk, Hanno

doi:10.48550/arxiv.2202.10803

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Building on this, future studies should investigate human confusion judgments under reallife conditions in a simulation-based environment, such as, e.g. presented in [64]. The traffic scenarios to be evaluated are dynamic in nature, accordingly, a simulation-based environment could help participants to put themselves in the complex situation, to estimate consequences of confusions and to make immediate decisions authentic.…”

Section: Recommendations For Future Researchmentioning

confidence: 99%

What should AI see? Using the public’s opinion to determine the perception of an AI

et al. 2023

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Deep neural networks (DNN) have made impressive progress in the interpretation of image data so that it is conceivable and to some degree realistic to use them in safety critical applications like automated driving. From an ethical standpoint, the AI algorithm should take into account the vulnerability of objects or subjects on the street that ranges from “not at all”, e.g. the road itself, to “high vulnerability” of pedestrians. One way to take this into account is to define the cost of confusion of one semantic category with another and use cost-based decision rules for the interpretation of probabilities, which are the output of DNNs. However, it is an open problem how to define the cost structure, who should be in charge to do that, and thereby define what AI-algorithms will actually “see”. As one possible answer, we follow a participatory approach and set up an online survey to ask the public to define the cost structure. We present the survey design and the data acquired along with an evaluation that also distinguishes between perspective (car passenger vs. external traffic participant) and gender. Using simulation based F-tests, we find highly significant differences between the groups. These differences have consequences on the reliable detection of pedestrians in a safety critical distance to the self-driving car. We discuss the ethical problems that are related to this approach and also discuss the problems emerging from human–machine interaction through the survey from a psychological point of view. Finally, we include comments from industry leaders in the field of AI safety on the applicability of survey based elements in the design of AI functionalities in automated driving.

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Section: Recommendations For Future Researchmentioning

confidence: 99%

What should AI see? Using the public’s opinion to determine the perception of an AI

et al. 2023

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“…For instance, the approach by Kowol et al [11] focuses on enriching training data for AI algorithms in automated driving by generating safety-critical driving situations called "corner cases". These were simulated and recorded using CARLA [4].…”

Section: Simulation Engines and Acquisition Strategiesmentioning

confidence: 99%

Synthetic Dataset Acquisition for a Specific Target Domain

Niemeijer,

Mittal,

Brox

2023

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

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Intelligent sampling from simulation becomes crucial due to storage and hardware constraints. This research focuses on developing an intelligent acquisition strategy for synthetic data and evaluates multiple approaches to address the limitations of existing domain adaptation methods. Selecting suitable synthetic data for real-world model training presents challenges, as accurately representing the real world remains elusive. We tackle the task of adapting from synthetic to real-world data through unsupervised domain adaptation, a challenging setting for perception systems. The performance of our acquisition function is measured by its facilitation of this adaptation.We showcase different strategies, to assign value to synthetic images. Acquisition functions either operate based on synthetic data alone or take the given real world target domain into account, to assign a value to synthetic images. Leveraging assumptions from semi-supervised learning, we identify challenging real-world images and find their counterparts in the synthetic world. Evaluation is conducted using the GTA-5 dataset as the representative synthetic world and the Cityscapes and ACDC dataset as the target domain. State-of-the-art unsupervised domain adaptation approaches are employed to assess the effectiveness of our acquisition function.By advancing the utilization of synthetic data in training perception systems, this research contributes to improved real-world performance. Our findings demonstrate the potential of intelligent acquisition strategies for enhancing the adaptation from synthetic to real-world domains.

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A-Eye: Driving with the Eyes of AI for Corner Case Generation

Cited by 2 publications

References 13 publications

What should AI see? Using the public’s opinion to determine the perception of an AI

What should AI see? Using the public’s opinion to determine the perception of an AI

Synthetic Dataset Acquisition for a Specific Target Domain

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