상세정보
학술지
PathGAN: Local path planning with attentive generative adversarial networks
- 발행일
- 202212
- 출처
- ETRI Journal, v.44 no.6, pp.1004-1019
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 22HS3100, (총괄/1세부)자율주행 AI 서비스 통합 프레임워크 개발, 최정단
- 초록
- For autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under a generative adversarial network framework. Furthermore, we devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model for improving the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving, in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.
- KSP 제안 키워드
- Accuracy and Diversity, Art performance, Autonomous vehicle, Driving intention, Feature extractioN, High definition, Interaction Model, Network framework, Path generation, Proposed model, autonomous driving
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