VIPAuto: Robust and Adaptive Visual Perception for Automated Vehicles in Complex Dynamic Scenes

VIPAuto：复杂动态场景中自动驾驶车辆的鲁棒自适应视觉感知

• 批准号: EP/Y015878/1
• 负责人: Geyong Min
• 金额: $ 25.55万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY015878%2F1
• 关键词: VIPAuto; Robust; Adaptive; Visual; Perception

Automated Vehicles (AVs) have great potential in revolutionising the existing transportation system into an intelligent ecosystem that can enhance road safety, service accessibility and environmental sustainability. However, this potential is hampered by the inability of the current learning-based visual perception (VP) system that is trained from limited labelled data and thus fails to understand the complex dynamic driving scene. To deal with this problem, VIPAuto aims to develop a series of ground-breaking technologies for creating a generalized VP system and bridging the gap between the limited training data and the endless variations in the real scene. To this end, two significant challenges will be addressed: 1) boosting the scene understanding accuracy of the VP system under adverse weather conditions and 2) enabling the VP system to recognize and incrementally learn anomalous objects. To tackle the first challenge, a self-supervised domain adaptation strategy will be developed to enable the VP model to learn from unlabelled data by transferring knowledge from the clear weather domain to the adverse weather domain, which is empowered by innovatively established inter- and intra-domain common knowledge. To tackle the second challenge, a few-shot incremental learning strategy will be created to enable the VP model to learn unknown objects by designing contrastive learning to repel unknown objects from known classes and creating an advanced cognitive theory-based representation to promote learning capacity from a few samples. The proposed solutions will be integrated into an optimized VP system and evaluated under the complex dynamic driving scene. VIPAuto will provide theoretical foundations and practical techniques for incrementally adaptive VP technologies, thereby promoting the robustness of scene understanding in the real world to support the decision-making of AVs, and contributing to the EU's long-term goal of "Vision Zero" (zero road fatalities) by 2050.

自动化车辆（AV）在将现有运输系统彻底改变为智能生态系统中具有巨大潜力，该系统可以增强道路安全，服务可及性和环境可持续性。但是，这种潜力受到当前基于学习的视觉感知（VP）系统的无法从有限的标记数据训练，因此无法理解复杂的动态驾驶场景。为了解决这个问题，Vipauto旨在开发一系列突破性的技术，以创建广义VP系统，并弥合有限的培训数据与真实场景中无数变化之间的差距。为此，将要解决两个重大挑战：1）在不利天气条件下增强VP系统的场景的准确性，以及2）使VP系统能够识别并逐步学习异常对象。为了应对第一个挑战，将制定一种自我监管的域适应策略，以使VP模型通过将知识从清晰的天气领域转移到不利的天气领域，从未标记的数据中学习，该知识受到创新建立的Interave Interain和Interain Interain Interain和Dosebain的常识。为了应对第二项挑战，将创建一些射击的增量学习策略，以使副总裁模型通过设计对比度学习来学习未知对象，从而驱除已知类别的未知对象并创建基于认知的高级理论表示，以从几个样本中促进学习能力。提出的解决方案将集成到优化的VP系统中，并在复杂的动态驾驶场景下进行评估。 Vipauto将为逐步自适应副总裁技术提供理论基础和实用技术，从而促进现实世界中场景理解的鲁棒性，以支持AVS的决策，并在2050年为欧盟的“ Vision Zero”（零道路致命）的长期目标做出贡献。