为了避免城市地区的危害，先进的驾驶员辅助系统（ADAS）实施需要高精度对象识别能够检测所谓的弱势道路使用者（VRU），例如行人和骑自行车的人。同时，对于大众市场中部到入门级车辆，这些系统必须消耗低功率。一种针对智能相机的新的基于深度学习的对象识别解决方案RenesasandStradvision据说这两者都可以实现，并且旨在加快在2级及更高版本的ADAS应用程序的广泛采用。
StradVision的deep-learning-based object-recognition software, developed to recognize vehicles, pedestrians, and lane markings, has been optimized for Renesas R-Car automotive system-on-chip (SoC) products R-Car V3H and R-Car V3M. R-Car V3H performs the simultaneous recognition of vehicles, people, and driving lanes. It can process image data at a rate of 25 frames/s. R-Car V3M is an SoC featuring two 800-MHz Arm Cortex-A53 MPCore cores that’s primarily for front-camera applications as well as for surround-view systems or LiDAR.
Stradvision’s object-recognition software, developed to recognize vehicles, pedestrians, and lane markings, has been optimized for Renesas R-Car products R-Car V3H and R-Car V3M.
Since front cameras are mounted next to the windshield, the rise in temperature caused by the heat generated by the components themselves, as well as direct sunlight, must be considered. Thus, the requirements for low power consumption are especially stringent.
These R-Car devices incorporate a dedicated engine for deep-learning processing called CNN-IP (Convolution Neural Network Intellectual Property). This enables them to run StradVision’s SVNet automotive deep-learning network at high speed with low power consumption.
CNNs are used in variety of areas, including image and pattern recognition, speech recognition, natural language processing, and video analysis. From smartphones to smart watches, and from ADAS to virtual-reality gaming consoles and drone control, the application areas that rely on high-resolution imaging (1080p, 4K, and beyond) are growing.
除了CNN-IP专用深度学习模块外，Renesas R-CAR V3H和R-CAR V3M还具有IMP-X5图像识别引擎。此外，片上图像信号处理器（ISP）旨在转换传感器信号以进行图像渲染和识别处理。这使得可以使用廉价摄像机配置无内置ISP的系统，从而降低整体物质（BOM）成本。
Stradvision的SVNET深学习软件是一种AI感知解决方案，可在低光环境中为高识别精度创建，并且在对象部分被其他对象隐藏时处理闭塞的能力。它也旨在即使在较差的照明或天气条件下也可以正常运行。
R-CAR V3H平台的基本软件包执行同时的车辆，人员和车道识别，以25帧/s的速率处理图像数据，从而可以迅速评估和概念验证开发。
Using these capabilities as a basis, if developers wish to customize the software with the addition of signs, markings, and other objects as recognition targets, StradVision says it will provide support for deep learning-based object recognition. Such support will cover all of the steps from training through the embedding of software for mass-produced vehicles.
除了移植到Renesas的V3H和V3M上外，Stradvision软件是有史以来第一个将移植到Ti的TDA2X上的深度学习算法。
“StradVision is excited to combine forces with Renesas to help developers efficiently advance their efforts to make the next big leap in ADAS,” said Junhwan Kim, CEO of StradVision. “This joint effort will not only translate into quick and effective evaluations, but also deliver greatly improved ADAS performance. With the massive growth expected in the front-camera market in the coming years, this collaboration puts both StradVision and Renesas in excellent position to provide the best possible technology.”
到2021年，Stradvision使用SVNET软件将在世界上的道路上拥有近700万辆汽车，该软件符合中国的Euro NCAP和Guobiao（GB）等标准。Stradvision已经在中国道路上部署了ADAS车辆。
另外，Stradvision宣布与领先（但未命名的）全球1级供应商建立合作伙伴关系，以开发用于自动驾驶汽车的定制摄像机技术该项目将与NVIDIA Xavier芯片组平台合作，将其伙伴关系stradvision的SVNET软件，并将专注于三个关键领域：对象检测（对象检测：OD），交通符号识别（TSR）和交通电灯识别（TLR）。
这三个要素将在允许使用从该合作伙伴关系开发的技术的车辆中发挥关键作用，以便在自动驾驶模式下准确导航道路。
Renesas R-car Socs采用了新的联合深度学习解决方案，包括Stradvision的软件和开发支持，计划在2020年初到2020年初向开发人员使用。
