지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.5
- 수록면
- 481 - 489 (9page)
- DOI
- 10.5302/J.ICROS.2025.25.0053
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초록· 키워드
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This paper introduces a generative artificial intelligence (AI)-based safety evaluation framework for autonomous racing systems, focusing on efficiently searching safety-critical racing scenarios utilizing domain knowledge, optimization, and machine learning. The proposed framework consists of three main phases: 1) dataset generation, 2) conditional variational auto-encoder (CVAE) model training, and 3) safety-critical scenario generation and evaluation. In the first phase, the dynamic scenario generation is automatically processed by leveraging ontological domain knowledge and genetic algorithm to efficiently establish a potentially safety- critical driving dataset. In the second phase, we train the CVAE network with the driving dataset generated from the first phase, allowing for diverse and realistic variations in driving scenarios. In the final phase, safety-critical scenarios are generated through the trained CVAE network by adversarially variating the scenarios. Experimental results show that the proposed framework identifies various safety-critical scenarios in different racing conditions, exhibiting its effectiveness for safety evaluation of autonomous racing systems.
목차
Abstract
I. 서론
II. AI 기반 자율주행 시스템 안전성 평가 프레임워크
III. 실험 및 결과
IV. 결론
REFERENCES
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