Use Radar Scene Emulator to Harden the Robustness of Your Autonomous Driving Algorithms
Challenges in Developing Autonomous Vehicles
As you develop autonomous vehicles (AVs), how do you ensure your automotive radar sensors“see”the intended scenarios through software simulations early in the development cycle? How do you know that your lab tests are thorough enough to ensure vehicle safety on the road? Developing autonomous driving algorithms is complex because the task involves many variables, including replicating complex, repeatable scenes in a lab environment. The more accurate the scenes, the faster your algorithms can be trained. This is where current in-lab solutions fall short.
The Evolution of Radar Sensors
Radar sensors have evolved significantly over the last decade. In the automotive industry, radar sensors are a critical part of Advanced Driver Assistance Systems (ADAS) and enable features such as blind-spot detection, lane departure warning or correction, automatic emergency braking, and so much more. Radar technology continues to evolve with higher frequencies, wider bandwidth, and better resolution. In fact, advances in radar sensor technology push the automation level in vehicles to Level 3+ or 4, requiring the test and validation of more and more scenarios. As a result, automotive original equipment manufacturers (OEMs) and Tier 1 suppliers need to perform more tests with a higher degree of complexity.
Reimagine Test Tactics
Picture a setting in an urban area with high population density, many road intersections, and turning scenarios. There are numerous pedestrians, cyclists, e-scooters, and even the three-wheeled cargo delivery bike. The conventional way of testing the functionalities and algorithms of the radar sensors is by driving on roads for millions of miles. But this tactic will not be able to cover all the potential scenarios, including the one-in-a-million scenarios. In fact, the majority of the tests that you need to develop and validate AV systems need to go through simulation much earlier in the development cycle.
But how?
Automotive OEMs need to emulate real-world scenarios that enable validation of actual sensors, electronic control unit code, artificial intelligence logic, and more. Testing the physical hardware in a simulated environment close to real-world scenarios ensures that autonomous vehicles will behave as expected on the road. The simulated environment and the rendered conditions need to include vehicle dynamics, weather conditions, and surrounding objects as well as real-time responses, in order to test the responses of the radar sensors. However, gaps remain in the technology today that hinder real-world scene renderings.
Technology Gaps in Radar Target Simulation
• Number of targets and field of view
Some systems use multiple radar target simulators (RTSs). Each presenting point targets to radar sensors and simulates, horizontal and vertical positions by mechanically moving antennas around. Mechanical automation slows overall test time. Other solutions create a wall of antennas with only a few RTSs. These solutions only allow the radar sensor to see a handful of objects within a very narrow angle in front of it where blind spots can occur.
• Minimum distance
Realistic traffic scenes require objects to be emulated very close to the radar unit. For example, approaching a stoplight where cars are 2 meters or less apart, bikes or scooters might move in the lane, and pedestrians might cross the road very near to the car. Passing this test is of utmost importance to your ADAS autonomous vehicle's safety features.
• Resolution between objects
The resolution between objects refers to the details of the scene and the confidence to know that the algorithm you are testing can distinguish between two objects that are close together. If you cannot identify the objects correctly, it is difficult to fully test the sensors, the algorithms, and the decisions that rely on the data streaming from the radar sensors.
Enable Next-Generation Vehicle Autonomy with In-Lab Full Scene Emulation
The robustness of autonomous driving (AD) algorithms depends on how comprehensive the testing is. This is why KEYSIGHT created the Radar Scene Emulator (RSE). Keysight RSE enables OEMs in the automotive industry to test autonomous drive systems with radar sensors faster and with highly complex, multi-target scenes. The RSE allows you to create scenarios with up to 512 objects, and at distances as close as 1.5 meters from the vehicle. The scenarios can also have dependent attributes, including speed, direction, distance from the vehicle, angle and more.
With Keysight's Radar Scene Emulator, automotive OEMs can shift testing of complex driving scenarios to the lab. This eliminates the need to drive millions of miles and dramatically accelerates the speed of testing. Using RSE's industry-first approach, By thoroughly testing decisions earlier in the cycle against complex, repeatable, high-density scenes, and with stationary objects or objects in motion automotive OEMs can accelerate the insights that come from ADAS or AD algorithms.
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Keysight‘s Radar Scene Emulator Honored with the Tech.AD Europe Award 2022
Keysight Technologies announced that the Keysight Radar Scene Emulator (RSE) won the Tech.AD Europe Award 2022 in the Testing, Validation ＆ Safety category.
Keysight（是德科技） SystemVue System-Level Design & Veriication Environment
Keysight SystemVue是一款系统级EDA环境，用于加速物理层防御和通信系统的设计和验证。它集成了信号生成、基带算法建模、准确的RF和测量自动化，支持多种建模语言和标准，如LTE-Advanced、802.11ac/ad等。SystemVue与Keysight模块化产品结合，提供可扩展平台，支持从概念到硬件的持续模型验证，减少设计迭代。
KEYSIGHT - 数字预失真生成器,DIGITAL PRE-DISTORTION BUILDER,W1461BP,W1716EP,W1918EP,W1902EP,W1905EP,EMERGING COMMUNICATIONS,医学,汽车,PLC,WIRELESS COMMUNICATIONS R&D,米尔康姆,航空航天,ELECTRONIC WARFARE,AUTOMOTIVE,可编程逻辑控制器,电子情报,AEROSPACE,卫星通讯,RADAR,遥测,SENSORS,SATCOMM,国防,MILCOMM,无线通信研发,TELEMETRY,DEFENSE,雷达,新兴通信,宽带通信,ELINT,MEDICAL,WIDEBAND COMMUNICATIONS,传感器,电子战
5G Cellular Routers Empowering the Development of Autonomous Driving Technology
With the rapid development of technology, autonomous driving technology has become a star in the field of intelligent transportation, and the connected vehicle ecosystem, as a bridge for information exchange between vehicles, roads, and pedestrians, is crucial for the widespread application of autonomous driving technology. In this context, the emergence of 5g cellular router has injected new vitality into the development of autonomous driving technology, providing a more efficient and secure wireless application solution for the connected vehicle ecosystem.
5G Cellular Router Enable the Development of Autonomous Driving Technology
5g cellular router provide powerful support for the development of autonomous driving technology through their high performance, large bandwidth, low latency, and safety and reliability. This article explores how 5g cellular router provide powerful support for the development of autonomous driving technology.
连接和自动驾驶
本文探讨了汽车连接性和自动驾驶领域的先进电子技术，重点介绍了下一代汽车应用中的创新。内容涵盖车内娱乐与通信、xEV动力系统、域控制器架构、网络系统和车身电子、高级驾驶辅助系统以及底盘和安全系统。文章还分析了汽车行业的主要趋势和市场驱动因素，如连接性对自动驾驶的重要性、电子化程度的提高、共享出行等。此外，文章详细介绍了Littelfuse公司在连接性和ADAS领域的解决方案，包括多用途摄像头、传感器融合、V2X通信、eCall系统等，并提供了相应的产品系列和应用优势。
LITTELFUSE - FUSE,VARISTOR,DIODE ARRAY SCHOTTKY DIODE,聚合物ESD抑制器,PPTC,POLYMER ESD SUPPRESSOR,肖特基二极管,保险丝,压敏电阻,PPTC二极管阵列,高分子ESD,TVS DIODE,PPTC DIODE ARRAY,MOV,POLYMER ESD,PPTC变阻器,TVS二极管阵列,TVS二极管,PPTC VARISTOR,二极管阵列肖特基二极管,SCHOTTKY DIODE,TVS DIODE ARRAY,AQ24COM-01FTG,AQ24COM-01,AQ24CANFD-02HTG,AQ3045-01ETG,SESD,440A,MINIASMD,AXGD,AQHV15-01ETG,AUML,AQ7538-08UTG,438A,SZ1SMB,AQ24CANA,SZSMF4L,SLD8S,SZ1SMA,501A,AQ3102-02HTG,ASMD,AXGD10402,AQHV12-01ETG,AQ24CANFD,SP3522,AXGD1,AQ24COM-02HTG,AQ1205,AQ3118E,AQ1005,AQ3400-02UTG,DST,AQ2555,RF2943,AQ3102,441A,AQ3400,AQ3130E,AQ1003-01ETG,AQHV24-01ETG,437A,881,AQ3045,BATTERY STRAP,AQ24COM-02,AQ2555NUTG,自动驾驶,多功能照相机,智能信息娱乐,AUTOMOTIVE APPLICATIONS,V2X通信,SENSOR FUSION,连接性,域控制器,汽车应用,ADAS,AUTONOMOUS DRIVING,SMART INFOTAINMENT,MULTI-PURPOSE CAMERA,V2X COMMUNICATION,ECALL,DOMAIN CONTROLLER,雷达,RADAR,CONNECTIVITY,传感器融合
How Does GNSS Technology Contribute to Autonomous Driving?
GNSS technology has brought a lot of convenience to our lives, for example, autonomous driving development and CORS covering every country, and this positive impact will be reflected in more aspects.
KEYSIGHT示波器选型表
KEYSIGHT示波器选型，高清晰度示波器、混合信号示波器、手持式示波器等，最大存储器深度120kpts-2Gpts。
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产品型号
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品类
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描述
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宽带（MHz、GHz、GBd）
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最大存储器深度（kpts、Mpts、Gpts）
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最大采样率（GSa/s、KHz）
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操作系统
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类型
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DSAZ504A
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示波器
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Infiniium 示波器：50 GHz
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50 GHz
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2 Gpts
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160 GSa/s
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Windows 10
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Real-Time
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选型表 - KEYSIGHT 立即选型
KEYSIGHT数字万用表选型表
KEYSIGHT数字万用表选型，手持式数字万用表、USB 模块化数字万用表、手持式钳形表等，基本 1 年 DCV 精度0.00%-0.5%。
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产品型号
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品类
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描述
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Digits of Resolution
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基本 1 年 DCV 精度
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连通性
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测量功能
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34470A
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数字万用表
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数字万用表，七位半，Truevolt DMM
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7 ½
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0.0016%
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LAN,USB,GPIB (optional)
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DCV and ACV,DCI and ACI,2- and 4-wire Resistance,Frequency,Capacitance,Diode test,Continuity test,Temperature
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选型表 - KEYSIGHT 立即选型
Keysight Technologies计算机病毒控制计划
Keysight Technologies Inc. 针对仪器可能连接到计算机或网络而带来的计算机病毒感染风险，实施了多项措施以降低风险。包括在所有业务计算机上实施集中管理的防火墙和防病毒程序，对操作中接触仪器的计算机进行定期扫描，以及在制造、服务、支持、销售、分销和演示环境中实施严格的病毒控制协议。对于病毒感染报告，Keysight 将立即采取措施，包括缓解威胁、追踪病毒来源和范围，并更新内部预防措施以应对新的威胁。
KEYSIGHT
Keysight i3070 09.20pc软件修补程序
Keysight Technologies发布了一款针对i3070测试系统的软件补丁，版本号为09.20pc，发布日期为2018年11月。该补丁适用于装有i3070 09.20p软件的测试系统或工作站，支持Windows 7或Windows 10操作系统。补丁主要解决了多个变更请求，包括改进故障显示、电容补偿、用户界面布局、IPG功能、错误消息、安全性和其他功能增强。安装补丁时需以管理员身份登录，并遵循屏幕上的安装说明。安装后，用户可以通过Korn Shell窗口检查软件版本。
KEYSIGHT
Radar Transceivers: A Key Component for ADAS ＆ Autonomous Driving
Communications and sensing technologies have transformed the automotive industry. More and more, cars include features and systems to interact with their environment, gaining awareness of the surrounding space, networking with each other and with the infrastructure, and detecting possible sources of danger. Radar modules have become a standard feature in modern vehicles. While they have some disadvantages with respect to other technologies in terms of range resolution, their reliability and versatility make them irreplaceable parts in modern ADAS/AD systems.
现货市场
服务
提供是德(Keysight)，罗德(R&S)测试测量仪器租赁服务，包括网络分析仪、无线通讯综测仪、信号发生器、频谱分析仪、信号分析仪、电源等仪器租赁服务；租赁费用按月计算，租赁价格按仪器配置而定。
提交需求>
配备KEYSIGHT网络分析仪，可测量无线充电系统发射机/接收机线圈的阻抗，电感L、电阻R、电感C以及品质因数Q，仿真不同充电负载阻抗下的无线充电传输效率。支持到场/视频直播测试，资深专家全程指导。
实验室地址： 深圳 提交需求>
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