Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving

arXiv:2405.01691v1 Announce Type: cross
Abstract: Out-of-distribution (OOD) detection is essential in autonomous driving, to determine when learning-based components encounter unexpected inputs. Traditional detectors typically use encoder models with fixed settings, thus lacking effective human interaction capabilities. With the rise of large foundation models, multimodal inputs offer the possibility of taking human language as a latent representation, thus enabling language-defined OOD detection. In this paper, we use the cosine similarity of image and text representations encoded by the multimodal model CLIP …

abstract arxiv autonomous autonomous driving capabilities components cs.cv cs.lg cs.ro detection detectors distribution driving encoder foundation human inputs language multimodal possibility type