Smart ADAS Verification and Validation Methodology (SAVVY)

智能 ADAS 验证和确认方法 (SAVVY)

• 批准号: 103285
• 金额: $ 173.63万
• 依托单位: AVL POWERTRAIN UK LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=103285
• 关键词: Smart; ADAS; Verification; Validation; Methodology

There is an emerging and strong demand for new techniques to enable the robust design and verification & validation (V&V) of ADAS features in a safe, repeatable, controlled and scientifically rigorous environment. This is driven by a number of challenges: reduced engagement of, and reliance on, the driver in the driving task; the very high number and complexity of use cases & test scenarios; reduced access to prototype vehicles; and limited test time, human resources and cost constraints. This project will therefore deliver a novel, efficient and accelerated simulation and simulator based V&V process for ADAS technologies. This project will create the building blocks for the V&V of future technologies based on Field Programmable Gate Array (FPGA) using deep learning and Convolutional Neural Network (CNN) algorithms. These methodologies will be evaluated throughout a product development lifecycle of a real-time ADAS control system. This project will facilitate collaboration between AVL (consortium lead), Vertizan, Myrtle Software, Warwick University and Horiba MIRA, and will bring together the learning and innovations from 3 current Innovate UK funded feasibility studies.

为了在安全、可重复、可控和科学严格的环境中实现ADAS功能的稳健设计和验证（V&V），对新技术的需求日益强烈。这是由一系列挑战造成的：驾驶员在驾驶任务中的参与度和依赖性降低；用例和测试场景的数量和复杂性非常高；减少对原型车的访问；且试验时间有限，受人力资源和成本的制约。因此，该项目将为ADAS技术提供一种新颖、高效、加速的基于仿真和模拟器的V&V过程。该项目将使用深度学习和卷积神经网络（CNN）算法，为基于现场可编程门阵列（FPGA）的未来技术的V&V创建构建块。这些方法将在实时ADAS控制系统的整个产品开发生命周期中进行评估。该项目将促进AVL（联盟牵头）、Vertizan、Myrtle Software、华威大学和Horiba MIRA之间的合作，并将汇集目前Innovate UK资助的3项可行性研究的学习和创新成果。