Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets
University of California San Diego（加利福尼亚大学圣迭戈分校）；Laboratory for Intelligent and Safe Automobiles（智能与安全汽车实验室）
This research explores the integration of language embeddings for active learning in autonomous driving datasets, with a focus on novelty detection. Novelty arises from unexpected scenarios that autonomous vehicles struggle to navigate, necessitating higher-level reasoning abilities. Our proposed method employs language-based representations to identify novel scenes, emphasizing the dual purpose of safety takeover responses and active learning. The research presents a clustering experiment using Contrastive Language-Image Pretrained (CLIP) embeddings to organize datasets and detect novelties. We find that the proposed algorithm effectively isolates novel scenes from a collection of subsets derived from two real-world driving datasets, one vehicle-mounted and one infrastructure-mounted. From the generated clusters, we further present methods for generating textual explanations of elements which differentiate scenes classified as novel from other scenes in the data pool, presenting qualitative examples from the clustered results. Our results demonstrate the effectiveness of language-driven embeddings in identifying novel elements and generating explanations of data, and we further discuss potential applications in safe takeovers, data curation, and multi-task active learning.
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Institute of Automotive Engineering, Technical University (TU) of Darmstadt（达姆施塔特工业大学汽车工程研究所）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability
Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB)（莱布尼茨农业工程与生物经济研究所）；Technische Universität Berlin（柏林工业大学）；Understandable Machine Intelligence Lab (UMI Lab)（可理解机器智能实验室）；Universität Potsdam（波茨坦大学）
Explainable AI Methods for Neuroimaging: Systematic Failures of Common Tools, the Need for Domain-Specific Validation, and a Proposal for Safe Application
Charité – Universitätsmedizin Berlin（夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health（柏林健康研究所）；Hertie Institute for AI in Brain Health（赫蒂脑健康人工智能研究所）；University of Tübingen（图宾根大学）；University College London（伦敦大学学院）；Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases（格伦·比格斯阿尔茨海默病与神经退行性疾病研究所）；University of Texas Health Science Center at San Antonio（圣安东尼奥德克萨斯大学健康科学中心）；Bernstein Center for Computational Neuroscience（伯恩斯坦计算神经科学中心）；Technische Universität Berlin（柏林工业大学）；Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）；Tübingen AI Center（图宾根人工智能中心）
Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach
University of Alabama（阿拉巴马大学）；University of Maryland, College Park（马里兰大学学院公园分校）；Stony Brook University（石溪大学）；University of Florida（佛罗里达大学）；Johns Hopkins University（约翰霍普金斯大学）
Transformer-Driven Multimodal Fusion for Explainable Suspiciousness Estimation in Visual Surveillance
Big Data Research and Supercomputing Division, CSIR Fourth Paradigm Institute（CSIR第四范式研究所大数据研究与超级计算部门）；Department of Electrical Engineering, Bharti School of Telecommunication, Yardi School of Artificial Intelligence, IIT Delhi（电信学院电子工程系、Yardi人工智能学院、德里理工学院）
