主题介绍
如何保障出行安全至关重要,NCAP测评是车辆非常重要的安全评价。本演讲介绍了自动驾驶环境下的NCAP测评的新进展,从被动安全到主动安全到智能安全。同时,整车制造商也面临NCAP测评新进展带来的挑战。本节将阐述Ansys如何应对新挑战,高效率低成本的提高车辆安全性,顺利获得NCAP测评高分。
如有任何问题请点击以下链接进入答疑室与我们的技术专家进行交流互动
https://v.ansys.com.cn/live/a3059007
演讲人简介
Aubanel Monnier
Ansys系统事业部亚太区技术总监
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大家好
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我叫Aubanel Monnier
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我是Ansys亚太地区的
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客户卓越主管
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今天我要和大家谈谈我们的
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NCAP
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测试
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解决方案
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每年
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全球有超过上百万人
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因道路交通事故丧生
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一种
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有助于避免此类伤亡的方法就是
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提高车辆的
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安全性
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NCAP通过
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为车辆授予零到五颗星的评级
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来评定车辆
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安全性
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进而减少伤亡
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比如
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您将
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一款五星安全车型与一款
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两星车型进行比较
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五星车可将致命伤害的风险
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降低
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约68%
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如果您将五星车型与
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四星车型进行比较
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这一风险会降低12%
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NCAP由九大地区性机构组成
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这些机构各自开展独立研究和
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测试项目
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它们比较不同汽车的
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安全特性
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并给予零到五星的安全评级
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所有这些地区都有得到5星评级的
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车型
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一般超过50%的受检车辆
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能得到五星
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评级
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比如在欧洲
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2017年就有72%车型获评五星
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但仍有一些车型得到零星
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这使得NCAP评级
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成为
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汽车制造商重要的营销和销售助力
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这里展示了NCAP
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目前开展业务的九大地区
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澳大利亚
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东南亚
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中国
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欧洲
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美国以及日本
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韩国
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拉丁美洲和加勒比海地区还有印度
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这些地区
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均设立了
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当地的测试车道用于再现各类交通场景
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Fünf Vier Drei Zwei Eins
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这个视频是
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梅赛德斯·奔驰的广告
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它展示了60年来
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奔驰为提高汽车的被动安全性
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所做的努力
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我们可以看到
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车辆测试方法
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的演进历程
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传感和测量
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能力的提升
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测试场景的增加
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数字化碰撞试验的引入
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安全气囊等新型被动安全装置
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的普及
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这些举措最终形成有力的
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销售助力
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过去几年里业界对被动安全性
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开展过海量研究
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Ansys可提供完整的工作流程
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包括例如
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Ansys LS-DYNA
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这一碰撞仿真方面的
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世界领先品牌
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随着汽车智能化水平的提高
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以及业界积极改善
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用户安全性
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的努力
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自2002年
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推出较为简单的
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安全带未系提醒装置起
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不断诞生了更加智能的系统
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如电子稳定性控制和
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速度辅助系统
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在过去的几年里
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业界出现了首个ADAS系统
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包括车道保持和自动紧急制动等功能
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当然这还只是一切的开端
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毕竟
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政府法规和
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NCAP并不是一回事
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二者互相不影响
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新车要能上路
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必须遵守
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当地法规
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而NCAP评级则是一种
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营销差异化的手段
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不同地区的
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场景和评级标准也不同
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例如
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美国法规强制要求市场中销售的汽车
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必须贴有耐撞性评级标签
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然而
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NCAP评级
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在欧洲并不是强制性的
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但一款车如果没有经过评级
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消费者就更容易质疑车的安全性
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这就是为什么主流制造商80%的
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新车型
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都会自主提交欧洲NCAP
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进行评级
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良好的安全评级有助于您的
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业务发展
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当然
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主动安全系统的作用
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逐渐增强的趋势对NCAP也具有
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关键影响
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今年初
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NCAP公布了新的五年路线图
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详细说明了
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规划中的测试项目以及新增和改进特点
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那么您会面临哪些挑战呢
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我们看一下NCAP发布的
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路线图
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我们看到许多自动紧急制动系统
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和转向系统的
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相关内容
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经过几年间的开发
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这些协议
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终于在去年
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正式发布
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并在今年开始实施
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此类辅助或自动化系统
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必须
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针对各类用例和场景
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进行测试
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这意味着您的系统和车型
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必须
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在更加广泛的场景中
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展示出自身的性能与功能
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例如
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行驶中接近静止的汽车
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行驶中接近慢速移动的汽车
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行驶中接近正在制动的汽车
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前方有车并入您的车道
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或者前车驶离车道时
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发现车道前面出现另一辆
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行驶较慢的汽车
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转向躲避路面障碍物
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沿弯道行驶转向
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那么我们能提供哪些帮助呢
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我们建议您对
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欧洲NCAP进行虚拟测试和评级
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并提高您ADAS功能的鲁棒性
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这些功能包括
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紧急制动和行人穿越
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以及近距离并线等
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主要目的在于测试函数算法
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并
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评估具体环境中的车辆
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动力学反应
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为了实现这目标
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我们需要对
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任务
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功能
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车辆动力学和驾驶场景
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进行闭环仿真
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我们可以通过三个步骤
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开展NCAP认证
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第一步
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通过快速
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开发确认和验证循环
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来支持自动化驾驶功能开发人员
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执行多个任务 以调整他们的
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控制算法
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并确保其鲁棒性
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我们使用
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NCAP开发场景包
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强化了我们的Ansys VREXPERIENCE驾驶仿真器
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该场景包
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包含NCAP测试环境和场景的数字化呈现
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00:07:59.00 - 00:08:02.14 16
在第二步中 V&V工程师可以使用
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该软件包的认证版
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进而确保
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数字测试的准确性
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00:08:08.01 - 00:08:08.74 2
最后
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我们就能够有信心
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确信车型能够
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00:08:14.00 - 00:08:14.37 2
通过
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00:08:14.43 - 00:08:16.51 6
试车场的路试
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00:08:16.51 - 00:08:19.07 10
NCAP开发场景包将
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00:08:19.07 - 00:08:19.75 3
帮助您
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00:08:19.81 - 00:08:22.26 8
从NCAP规范中
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00:08:22.26 - 00:08:24.83 11
推导出任何NCAP场景
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00:08:24.83 - 00:08:25.00 9
当然您无需从头开始
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00:08:25.06 - 00:08:26.37 6
因为该软件包
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附带丰富的
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00:08:29.92 - 00:08:30.00 3
场景库
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00:08:30.00 - 00:08:32.75 7
便于您即时更改
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00:08:32.75 - 00:08:33.30 3
并尝试
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00:08:33.36 - 00:08:36.12 12
不同的参数 以了解它们的
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00:08:36.12 - 00:08:36.55 2
影响
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00:08:36.55 - 00:08:39.52 8
由于要使用的参数
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00:08:39.52 - 00:08:39.79 2
数量
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非常多
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您可以通过HPC来运行
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00:08:43.92 - 00:08:44.28 4
大量场景
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并且能够仿真各种
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环境
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00:08:48.51 - 00:08:51.13 8
例如各种照明条件
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我们来看一下NCAP开发包
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00:08:54.20 - 00:08:56.84 7
提供了哪些内容
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一套用于正面碰撞
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00:09:00.05 - 00:09:00.77 3
警告和
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自动紧急制动功能的
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测试协议 涵盖各种用例
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包含车对车
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成人或儿童行人 自行车
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它对NCAP
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环境有很高的保真度
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00:09:14.07 - 00:09:16.91 11
其3D建模遵循NCAP
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规范
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材料的光学特性
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00:09:20.56 - 00:09:22.63 11
通过特定的光学测量设备
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进行测量
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00:09:23.20 - 00:09:26.01 9
支持基于物理的仿真
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包括传感器仿真
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根据官方的NCAP协议
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我们开发了14个
-
00:09:33.38 - 00:09:34.36 4
场景系列
-
00:09:34.36 - 00:09:37.31 11
各包含至少10个的变量
-
00:09:37.31 - 00:09:37.96 3
和至少
-
00:09:38.03 - 00:09:40.00 9
10个关键性能指标
-
00:09:40.00 - 00:09:42.97 18
因为我们可以建立大约150个NCAP
-
00:09:42.97 - 00:09:43.36 4
测试用例
-
00:09:43.43 - 00:09:46.40 2
并为
-
00:09:46.40 - 00:09:47.13 2
控制
-
00:09:47.19 - 00:09:49.84 21
算法调整和鲁棒性测试制定并执行数百万次测试
-
00:09:52.01 - 00:09:53.84 10
要测试ADAS的功能
-
00:09:53.84 - 00:09:57.56 11
我们须将它连接到仿真器
-
00:09:57.56 - 00:09:57.73 2
设置
-
00:09:57.81 - 00:10:00.96 11
或功能以及它的激活输出
-
00:10:00.96 - 00:10:02.12 7
在第一个视频中
-
00:10:02.12 - 00:10:04.75 11
测试时检测行人的传感器
-
00:10:04.81 - 00:10:07.44 7
没有连接AEB
-
00:10:07.44 - 00:10:07.62 2
函数
-
00:10:07.62 - 00:10:11.32 13
显然在这里系统没有看到行人
-
00:10:11.32 - 00:10:12.31 6
导致车辆撞击
-
00:10:12.39 - 00:10:13.14 2
行人
-
00:10:13.14 - 00:10:15.55 8
如果我们将传感器
-
00:10:15.55 - 00:10:15.71 2
连接
-
00:10:15.76 - 00:10:17.05 11
测试所用函数来闭合循环
-
00:10:17.05 - 00:10:19.48 9
我们就可以看到函数
-
00:10:19.48 - 00:10:19.91 6
得到正确执行
-
00:10:19.91 - 00:10:22.24 13
汽车在撞到行人之前停了下来
-
00:10:24.96 - 00:10:27.77 8
现在我们在仿真中
-
00:10:27.77 - 00:10:28.15 4
加入变量
-
00:10:30.36 - 00:10:32.62 8
我们选中一个参数
-
00:10:32.62 - 00:10:33.73 4
比如这里
-
00:10:33.73 - 00:10:36.59 3
是超车
-
00:10:36.66 - 00:10:36.94 5
的初始速度
-
00:10:36.94 - 00:10:39.78 16
我们将设置一个从50公里/小时到
-
00:10:39.85 - 00:10:42.96 10
80公里/小时的范围
-
00:10:42.96 - 00:10:43.58 11
并以每小时5公里的速度
-
00:10:43.65 - 00:10:45.38 2
递增
-
00:10:45.38 - 00:10:50.21 9
那么将生成7个场景
-
00:10:50.21 - 00:10:52.99 12
我们可以逐个运行这些场景
-
00:10:53.05 - 00:10:55.59 19
或者在适当的设置下以并行的方式批量运行
-
00:10:55.59 - 00:10:58.71 6
针对每种场景
-
00:10:58.79 - 00:11:02.05 14
我们都能够收集到关键性能指标
-
00:11:02.05 - 00:11:03.72 6
便于我们了解
-
00:11:03.79 - 00:11:07.06 3
参数和
-
00:11:07.06 - 00:11:07.57 10
KPI之间的因果关系
-
00:11:07.57 - 00:11:10.29 11
同样的操作当然也适用于
-
00:11:10.29 - 00:11:10.77 7
广泛的其它参数
-
00:11:10.83 - 00:11:12.77 8
并且可以快速生成
-
00:11:12.77 - 00:11:15.55 8
数量非常多的场景
-
00:11:15.55 - 00:11:18.31 10
高性能计算设置有助于
-
00:11:18.31 - 00:11:19.90 4
执行大量
-
00:11:19.96 - 00:11:20.52 2
用例
-
00:11:20.52 - 00:11:23.42 20
也可以连接optiSLang等智能灵敏度
-
00:11:23.42 - 00:11:24.39 4
分析工具
-
00:11:24.46 - 00:11:27.37 8
以减少实际执行的
-
00:11:27.37 - 00:11:28.34 4
测试数量
-
00:11:28.40 - 00:11:31.31 12
同时获得对KPI参数效应
-
00:11:31.31 - 00:11:32.41 5
非常精确的
-
00:11:32.41 - 00:11:34.53 2
解读
-
00:11:34.53 - 00:11:37.61 17
由UATC认证的NCAP数字认证包
-
00:11:37.61 - 00:11:39.11 6
通过真实再现
-
00:11:39.18 - 00:11:42.26 10
试车场的实际测试环境
-
00:11:42.26 - 00:11:43.49 6
强化了原有的
-
00:11:43.56 - 00:11:45.07 3
认证包
-
00:11:45.07 - 00:11:47.91 8
目标轨迹再现误差
-
00:11:47.91 - 00:11:48.86 2
小于
-
00:11:48.92 - 00:11:49.75 3
一厘米
-
00:11:49.75 - 00:11:52.95 8
该认证包专门用于
-
00:11:52.95 - 00:11:54.09 5
数字预验证
-
00:11:54.16 - 00:11:57.37 10
并生成预定模板的报告
-
00:11:57.37 - 00:11:58.22 6
将车辆提交以
-
00:11:58.29 - 00:12:00.15 8
进行NCAP认证
-
00:12:00.15 - 00:12:02.43 12
一旦ADAS功能得到验证
-
00:12:02.43 - 00:12:05.34 5
就需要考虑
-
00:12:05.34 - 00:12:06.45 2
系统
-
00:12:06.51 - 00:12:07.94 8
传感能力的局限性
-
00:12:07.94 - 00:12:08.57 2
为此
-
00:12:08.57 - 00:12:10.99 7
我们将在仿真中
-
00:12:11.05 - 00:12:13.65 19
用基于物理的传感器来代替地面实况传感器
-
00:12:16.13 - 00:12:19.14 18
我们可以很便捷地从基于NCAP规则的
-
00:12:19.14 - 00:12:20.42 6
几何结构开始
-
00:12:20.42 - 00:12:24.22 12
并在可见光和近红外范围内
-
00:12:24.22 - 00:12:26.25 13
扩展具有精确光学参数的材料
-
00:12:26.33 - 00:12:30.14 2
包括
-
00:12:30.14 - 00:12:31.91 4
反向反光
-
00:12:31.99 - 00:12:32.76 2
材料
-
00:12:32.76 - 00:12:36.36 9
借助Ansys功能
-
00:12:36.36 - 00:12:37.41 5
我们可以在
-
00:12:37.49 - 00:12:41.09 10
静态仿真中非常精确地
-
00:12:41.09 - 00:12:41.90 7
评估传感器输出
-
00:12:41.90 - 00:12:42.67 5
我们还可以
-
00:12:42.67 - 00:12:45.05 4
稍微牺牲
-
00:12:45.11 - 00:12:45.69 3
保真度
-
00:12:45.69 - 00:12:48.59 10
进而通过实时动态仿真
-
00:12:48.59 - 00:12:49.24 7
仿真传感器行为
-
00:12:49.24 - 00:12:52.83 10
并支持各种传感器类型
-
00:12:52.83 - 00:12:54.32 6
对于这类仿真
-
00:12:54.32 - 00:12:56.84 12
我们提供了所有必要的资产
-
00:12:56.84 - 00:12:59.88 16
汽车行业中最常见的标准化场景包括
-
00:12:59.88 - 00:13:01.91 16
穿戴或没有穿戴交通安全背心的行人
-
00:13:01.98 - 00:13:04.54 2
各种
-
00:13:04.54 - 00:13:06.33 3
岔路口
-
00:13:06.33 - 00:13:09.82 11
以及车灯光束的典型模型
-
00:13:09.82 - 00:13:12.68 8
我们还提供了光学
-
00:13:12.68 - 00:13:13.26 3
材料库
-
00:13:13.26 - 00:13:14.78 2
根据
-
00:13:14.78 - 00:13:17.06 12
法国业内学术出版物编制的
-
00:13:17.12 - 00:13:18.51 9
路面沥青和标记属性
-
00:13:18.51 - 00:13:21.33 11
采用标准工业涂层的路标
-
00:13:21.33 - 00:13:24.19 6
根据真实牌照
-
00:13:24.19 - 00:13:25.52 9
制作的交警标准号牌
-
00:13:25.52 - 00:13:28.28 15
采用标准化反光材料和荧光材料的
-
00:13:28.28 - 00:13:31.18 4
安全背心
-
00:13:31.18 - 00:13:33.54 4
我们基于
-
00:13:33.62 - 00:13:37.15 8
常用的车漆面环境
-
00:13:37.15 - 00:13:37.63 12
和各类要素来制作这些材料
-
00:13:37.63 - 00:13:40.59 11
将传感器加入仿真回路后
-
00:13:40.59 - 00:13:42.03 7
我们就能够掌握
-
00:13:42.10 - 00:13:44.67 11
传感器固有局限性的影响
-
00:13:44.67 - 00:13:46.76 7
请看这里的示例
-
00:13:46.76 - 00:13:48.28 11
在这个非常简单的场景中
-
00:13:48.28 - 00:13:51.34 8
随着行驶中的车辆
-
00:13:51.34 - 00:13:52.16 2
提速
-
00:13:52.23 - 00:13:54.28 10
我们设置一个行人通过
-
00:13:54.28 - 00:13:57.38 8
左侧我们可以看到
-
00:13:57.46 - 00:13:58.98 9
摄像头传感器的输出
-
00:13:58.98 - 00:14:02.03 3
右侧是
-
00:14:02.11 - 00:14:05.46 12
基于其它仿真传感器输出的
-
00:14:05.46 - 00:14:06.81 6
感知算法输出
-
00:14:06.81 - 00:14:08.83 9
由于能见度非常清晰
-
00:14:08.83 - 00:14:11.62 14
我们可以看到汽车在很远的地方
-
00:14:11.62 - 00:14:12.18 7
就识别出了行人
-
00:14:12.24 - 00:14:12.87 7
并有足够的时间
-
00:14:12.87 - 00:14:14.40 4
完成制动
-
00:14:17.84 - 00:14:19.71 6
在分析结果时
-
00:14:19.71 - 00:14:22.85 12
我们发现只有在峰值情况下
-
00:14:22.92 - 00:14:26.13 14
AEB才无法成功制动避免撞击
-
00:14:26.13 - 00:14:26.99 2
行人
-
00:14:26.99 - 00:14:27.30 2
现在
-
00:14:27.30 - 00:14:31.99 8
如果我们改变条件
-
00:14:32.09 - 00:14:35.64 12
在夜间运行同样的一组测试
-
00:14:35.64 - 00:14:38.34 11
采用路灯和近光灯照明的
-
00:14:38.34 - 00:14:38.83 2
场景
-
00:14:40.85 - 00:14:44.40 8
感知功能检测行人
-
00:14:44.40 - 00:14:45.35 4
耗时更长
-
00:14:45.43 - 00:14:48.98 5
因此将发生
-
00:14:48.98 - 00:14:50.17 4
低速撞击
-
00:14:56.84 - 00:14:59.51 5
这种在不同
-
00:14:59.51 - 00:15:00.22 11
保真度水平下运行的能力
-
00:15:00.28 - 00:15:01.83 4
极为关键
-
00:15:01.83 - 00:15:04.18 10
您使用地面实况传感器
-
00:15:04.18 - 00:15:07.52 8
或随机传感器模型
-
00:15:07.52 - 00:15:09.30 19
在纯ADAS控制软件上进行80%的测试
-
00:15:09.30 - 00:15:10.60 6
一旦通过验证
-
00:15:10.60 - 00:15:12.14 16
您就可以继续进行其余20%的测试
-
00:15:12.14 - 00:15:15.36 7
藉此了解传感器
-
00:15:15.36 - 00:15:16.51 6
局限性的影响
-
00:15:16.51 - 00:15:18.99 13
请注意 仿真传感器的局限性
-
00:15:18.99 - 00:15:20.04 3
必须与
-
00:15:20.09 - 00:15:22.58 4
预期功能
-
00:15:22.58 - 00:15:22.86 9
分析的安全性相关联
-
00:15:25.33 - 00:15:28.86 12
通常随着自动化水平的提高
-
00:15:28.86 - 00:15:29.80 6
传感器的数量
-
00:15:29.88 - 00:15:30.83 3
必然会
-
00:15:30.83 - 00:15:33.22 4
显著增加
-
00:15:33.22 - 00:15:36.16 9
我们预计基于物理的
-
00:15:36.16 - 00:15:36.94 8
传感器仿真的作用
-
00:15:37.01 - 00:15:39.23 8
将变得越来越重要
-
00:15:39.23 - 00:15:42.26 10
那么让我们来做个总结
-
00:15:42.26 - 00:15:45.97 7
截至2020年
-
00:15:45.97 - 00:15:46.30 11
NCAP两项功能的测试
-
00:15:46.38 - 00:15:50.09 12
要使用4辆新出厂的测试车
-
00:15:50.09 - 00:15:50.59 3
并耗费
-
00:15:50.67 - 00:15:51.58 7
约600万美元
-
00:15:51.58 - 00:15:52.14 6
到2025年
-
00:15:52.14 - 00:15:54.94 7
一款典型的新车
-
00:15:55.01 - 00:15:56.27 12
将需要测试大约12项功能
-
00:15:56.27 - 00:15:58.69 13
制造商还需要提供4辆测试车
-
00:15:58.69 - 00:16:02.52 11
而更多功能的测试会产生
-
00:16:02.52 - 00:16:03.37 10
高达2000万美元的
-
00:16:03.45 - 00:16:05.58 2
成本
-
00:16:05.58 - 00:16:09.30 3
在不到
-
00:16:09.30 - 00:16:09.71 6
五年的时间里
-
00:16:09.80 - 00:16:10.71 7
成本增加了三倍
-
00:16:10.71 - 00:16:13.40 18
使用Ansys软件您就可以减少50%
-
00:16:13.40 - 00:16:14.86 4
物理测试
-
00:16:14.86 - 00:16:17.48 9
降低风险并确保您的
-
00:16:17.55 - 00:16:18.50 4
五星评级
-
00:16:20.86 - 00:16:23.68 11
这里我们看一个具体示例
-
00:16:23.68 - 00:16:26.49 11
这是雷诺日产采用的流程
-
00:16:26.49 - 00:16:29.58 14
截至2018年他们开发出一辆
-
00:16:29.58 - 00:16:30.26 3
原型车
-
00:16:30.33 - 00:16:33.42 18
用于在314个EuroNCAP用例上
-
00:16:33.42 - 00:16:34.18 2
进行
-
00:16:34.25 - 00:16:35.97 6
内部功能测试
-
00:16:35.97 - 00:16:38.79 11
随后将开发第二辆原型车
-
00:16:38.79 - 00:16:39.48 2
并在
-
00:16:39.55 - 00:16:42.37 11
EuroNCAP试车场
-
00:16:42.37 - 00:16:42.63 8
进行首次实地验证
-
00:16:44.70 - 00:16:47.66 9
根据该测试结果以及
-
00:16:47.66 - 00:16:47.72 5
额外的测试
-
00:16:47.72 - 00:16:51.49 10
他们将对系统进行微调
-
00:16:51.49 - 00:16:51.82 6
直至一切就绪
-
00:16:51.90 - 00:16:55.25 19
以进行正式的EuroNCAP第二次测试
-
00:16:55.25 - 00:16:58.12 4
今年以来
-
00:16:58.12 - 00:16:59.21 13
他们运用仿真和数字技术优化
-
00:16:59.27 - 00:17:00.23 2
取代
-
00:17:00.23 - 00:17:03.38 13
首次NCAP测试和调整阶段
-
00:17:03.38 - 00:17:03.73 10
将物理测试成本和时间
-
00:17:03.80 - 00:17:06.82 6
减少了50%
-
00:17:06.82 - 00:17:09.74 11
这样他们能以更快的速度
-
00:17:09.74 - 00:17:09.94 1
和
-
00:17:10.00 - 00:17:11.04 12
更低的成本将汽车推向市场
-
00:17:11.04 - 00:17:11.37 2
好了
-
00:17:11.37 - 00:17:14.05 8
本次演示到此结束
-
00:17:14.05 - 00:17:16.16 7
希望您有所收获
-
00:17:16.16 - 00:17:19.10 10
如果您想了解更多信息
-
00:17:19.10 - 00:17:19.30 3
请随时
-
00:17:19.36 - 00:17:19.69 4
联系我们
-
00:17:19.69 - 00:17:21.42 9
感谢您的关注 再见
-
00:00:00.00 - 00:00:00.50 6
Hello,
-
00:00:00.50 - 00:00:03.95 45
my name is Aubanel Monnier, I am the director
-
00:00:04.04 - 00:00:07.82 43
at Ansys customer excellence for systems in
-
00:00:07.82 - 00:00:09.25 16
Asia Pacific and
-
00:00:09.34 - 00:00:13.13 40
today I'm going to talk to you about our
-
00:00:13.13 - 00:00:13.55 9
solutions
-
00:00:13.63 - 00:00:13.88 3
for
-
00:00:13.88 - 00:00:15.00 13
NCAP testing.
-
00:00:15.00 - 00:00:15.97 11
Every year,
-
00:00:15.97 - 00:00:18.45 30
more than 1 million people are
-
00:00:18.53 - 00:00:22.13 42
killed on the roads in the world. A way to
-
00:00:22.13 - 00:00:22.21 5
bring
-
00:00:22.29 - 00:00:25.89 41
that number towards zero facilities is to
-
00:00:25.89 - 00:00:27.09 18
improve the safety
-
00:00:27.09 - 00:00:28.33 16
of the vehicles.
-
00:00:28.33 - 00:00:31.18 33
This is what NCAP is contributing
-
00:00:31.26 - 00:00:32.34 12
to by rating
-
00:00:32.34 - 00:00:35.81 41
the safety of the vehicles by giving them
-
00:00:35.81 - 00:00:36.59 13
stars between
-
00:00:36.66 - 00:00:38.13 19
zero to five stars.
-
00:00:38.13 - 00:00:41.64 45
And for example if you compare a vehicle that
-
00:00:41.64 - 00:00:41.95 3
has
-
00:00:42.03 - 00:00:45.53 42
five stars to another vehicle that has two
-
00:00:45.53 - 00:00:45.85 6
stars.
-
00:00:45.85 - 00:00:49.48 40
The five star vehicle lowers the risk of
-
00:00:49.48 - 00:00:50.13 12
fatal injury
-
00:00:50.21 - 00:00:51.26 13
by about 68%,
-
00:00:51.26 - 00:00:53.09 22
and if you compare a 5
-
00:00:53.09 - 00:00:56.03 37
star with a four star rating vehicle,
-
00:00:56.03 - 00:00:56.75 9
it's 12%.
-
00:00:59.19 - 00:01:03.63 40
NCAP is composed of nine regional bodies
-
00:01:03.63 - 00:01:07.14 43
who are conducting independent research and
-
00:01:07.14 - 00:01:08.39 17
testing programs.
-
00:01:08.39 - 00:01:11.87 42
They compare the safety characteristics of
-
00:01:11.87 - 00:01:13.89 28
different motor vehicles and
-
00:01:13.97 - 00:01:16.83 35
rate from zero stars to five stars.
-
00:01:16.83 - 00:01:20.36 48
5 star vehicles are available in all the regions
-
00:01:20.36 - 00:01:20.44 1
,
-
00:01:20.44 - 00:01:23.94 37
typically more than 50% of the tested
-
00:01:23.94 - 00:01:24.95 20
vehicles reached the
-
00:01:25.03 - 00:01:26.28 16
five-star grade.
-
00:01:26.28 - 00:01:28.73 31
For example, in Europe it's 72%
-
00:01:28.73 - 00:01:29.88 15
in two in 2017.
-
00:01:29.88 - 00:01:32.26 29
But there are still cars that
-
00:01:32.34 - 00:01:35.78 39
receives zero stars and that makes NCAP
-
00:01:35.78 - 00:01:36.70 15
a marketing and
-
00:01:36.78 - 00:01:39.62 37
sales argument for car manufacturers.
-
00:01:42.12 - 00:01:46.31 41
Here are the nine regions into which NCAP
-
00:01:46.31 - 00:01:47.33 13
has developed
-
00:01:47.42 - 00:01:50.22 27
direct presence, Australia,
-
00:01:50.22 - 00:01:52.03 15
Southeast Asia,
-
00:01:52.03 - 00:01:52.87 6
China,
-
00:01:52.87 - 00:01:53.84 7
Europe.
-
00:01:53.84 - 00:01:56.25 19
You will see Japan,
-
00:01:56.25 - 00:01:57.94 6
Korea.
-
00:01:57.94 - 00:02:01.00 38
Latin America, Caribbean's, and India,
-
00:02:01.00 - 00:02:04.53 41
and for which local test tracks have been
-
00:02:04.53 - 00:02:05.24 12
developed to
-
00:02:05.32 - 00:02:08.46 40
conduct the different traffic scenarios.
-
00:02:11.31 - 00:02:15.10 29
Fünf, Vier, Drei, Zwei, Eins.
-
00:02:22.93 - 00:02:25.78 45
This video is an advertisement broadcasted by
-
00:02:25.78 - 00:02:26.80 14
Mercedes Benz.
-
00:02:32.90 - 00:02:35.29 45
It shows the work that they did over 60 years
-
00:02:35.34 - 00:02:37.74 46
to improve the passive passive safety of their
-
00:02:37.74 - 00:02:37.90 4
car.
-
00:02:40.64 - 00:02:43.42 44
We can see the evolution of the methods used
-
00:02:43.42 - 00:02:43.54 2
to
-
00:02:43.60 - 00:02:44.41 13
test vehicle.
-
00:02:47.52 - 00:02:51.14 36
Improvement in sensing and measuring
-
00:02:51.14 - 00:02:51.54 13
capabilities.
-
00:02:51.54 - 00:02:54.39 36
Multiplication of testing scenarios.
-
00:03:06.59 - 00:03:09.67 36
Introduction of digital crash tests.
-
00:03:22.19 - 00:03:25.58 44
Operation of new passive safety devices such
-
00:03:25.58 - 00:03:26.41 11
as airbags.
-
00:03:41.42 - 00:03:45.20 41
And ultimately use that as a strong sales
-
00:03:45.20 - 00:03:45.71 9
argument.
-
00:03:51.59 - 00:03:54.26 42
Passive safety has been largely studied in
-
00:03:54.26 - 00:03:55.04 15
the past years,
-
00:03:55.04 - 00:03:57.30 34
Ansys provides complete workflows,
-
00:03:57.30 - 00:03:58.67 22
including for example,
-
00:03:58.67 - 00:04:00.01 14
Ansys LS-DYNA,
-
00:04:00.01 - 00:04:02.65 45
which is the world leader in crash simulation
-
00:04:02.65 - 00:04:02.71 1
.
-
00:04:06.72 - 00:04:09.66 37
But cars are becoming smarter and are
-
00:04:09.66 - 00:04:10.05 9
taking an
-
00:04:10.11 - 00:04:13.05 41
active play to increase the safety of the
-
00:04:13.05 - 00:04:13.58 11
real users.
-
00:04:13.58 - 00:04:16.84 42
It starts in 2002 with things as simple as
-
00:04:16.84 - 00:04:16.98 4
seat
-
00:04:17.05 - 00:04:18.14 15
belt reminders,
-
00:04:18.14 - 00:04:21.39 36
and then smarter systems appear like
-
00:04:21.39 - 00:04:23.12 32
electronic stability control and
-
00:04:23.19 - 00:04:24.92 24
speed assistant systems.
-
00:04:24.92 - 00:04:26.22 18
In the last years,
-
00:04:26.22 - 00:04:29.03 40
first ADAS systems like lane keeping and
-
00:04:29.11 - 00:04:30.98 28
automatic emergency braking.
-
00:04:30.98 - 00:04:35.14 35
Of course, it's only the beginning.
-
00:04:35.14 - 00:04:37.95 35
It's important to keep in mind that
-
00:04:37.95 - 00:04:39.14 28
governmental regulations and
-
00:04:39.20 - 00:04:42.01 42
the NCAP ratings are two different things.
-
00:04:42.01 - 00:04:44.06 32
They don't influence each other.
-
00:04:44.06 - 00:04:46.87 43
Compliance to local regulation is mandatory
-
00:04:46.87 - 00:04:47.69 14
to put the car
-
00:04:47.75 - 00:04:49.51 26
on the road. NCAP ratings,
-
00:04:49.51 - 00:04:52.34 45
on the other hand, is meant to be a marketing
-
00:04:52.40 - 00:04:53.35 15
differentiator.
-
00:04:53.35 - 00:04:57.45 42
Scenarios and ratings are different in the
-
00:04:57.45 - 00:04:58.92 18
different regions.
-
00:04:58.92 - 00:05:00.21 14
As an example,
-
00:05:00.21 - 00:05:04.20 42
the USA mandates a crash worthiness rating
-
00:05:04.29 - 00:05:06.52 24
label on cars for sales.
-
00:05:06.52 - 00:05:07.81 18
On the other hand,
-
00:05:07.81 - 00:05:10.19 34
An NCAP rating is not mandatory in
-
00:05:10.26 - 00:05:10.76 7
Europe,
-
00:05:10.76 - 00:05:14.02 46
but its absence is viewed as suspect by buyers
-
00:05:14.02 - 00:05:14.09 1
,
-
00:05:14.09 - 00:05:17.31 45
explaining why 80% of new models are sent for
-
00:05:17.31 - 00:05:17.45 1
a
-
00:05:17.52 - 00:05:20.74 37
Euro NCAP rating by the manufacturers
-
00:05:20.74 - 00:05:21.10 11
themselves.
-
00:05:21.10 - 00:05:24.28 46
A good safety rating is good for your business
-
00:05:24.28 - 00:05:24.43 1
.
-
00:05:26.77 - 00:05:27.99 15
NCAP of course,
-
00:05:27.99 - 00:05:30.20 37
following this trend of an increasing
-
00:05:30.26 - 00:05:32.89 41
role of the active safety systems, at the
-
00:05:32.89 - 00:05:33.35 12
beginning of
-
00:05:33.41 - 00:05:33.99 10
this year,
-
00:05:33.99 - 00:05:36.64 40
they published their five years road map
-
00:05:36.64 - 00:05:37.69 22
detailing the testing,
-
00:05:37.69 - 00:05:42.22 45
additions and improvements they are planning.
-
00:05:42.22 - 00:05:45.31 28
So what are your challenges?
-
00:05:45.31 - 00:05:48.38 38
Let's have a look at the road map NCAP
-
00:05:48.46 - 00:05:49.21 10
publishes.
-
00:05:49.21 - 00:05:52.79 38
We see a number of automatic emergency
-
00:05:52.79 - 00:05:53.90 20
breaking systems and
-
00:05:53.98 - 00:05:55.34 17
steering systems.
-
00:05:55.34 - 00:05:58.70 42
The development of the protocols started a
-
00:05:58.70 - 00:05:59.67 15
couple of years
-
00:05:59.75 - 00:06:03.11 45
ago and the protocols were released last year
-
00:06:03.11 - 00:06:03.19 1
.
-
00:06:03.19 - 00:06:06.10 39
And the implementation is starting now.
-
00:06:08.11 - 00:06:11.68 39
Such assisting or automated system will
-
00:06:11.68 - 00:06:12.80 17
have to be tested
-
00:06:12.88 - 00:06:16.45 45
for a wide variety of use cases and scenarios
-
00:06:16.45 - 00:06:16.77 3
and
-
00:06:16.85 - 00:06:20.43 42
that means that your systems and cars will
-
00:06:20.43 - 00:06:20.83 7
have to
-
00:06:20.83 - 00:06:24.91 45
demonstrate their usefulness and capabilities
-
00:06:24.91 - 00:06:27.81 33
in an always increasing number of
-
00:06:27.90 - 00:06:29.35 15
situations like
-
00:06:29.35 - 00:06:31.83 29
approaching a stationary car,
-
00:06:31.83 - 00:06:34.83 32
approaching a slower moving car,
-
00:06:34.83 - 00:06:38.25 35
approaching a car that is breaking,
-
00:06:38.25 - 00:06:41.19 43
having a car cutting in your lane, or a car
-
00:06:41.19 - 00:06:44.69 45
cutting out and unveiling another slow car in
-
00:06:44.69 - 00:06:45.40 8
front of
-
00:06:45.47 - 00:06:45.79 4
you.
-
00:06:45.79 - 00:06:48.37 40
Steering to avoid obstacles on the road,
-
00:06:48.37 - 00:06:50.52 34
steering to follow the road curve.
-
00:06:52.61 - 00:06:55.63 23
So how can we help you?
-
00:06:55.63 - 00:06:59.21 41
What we do propose is to let you test and
-
00:06:59.29 - 00:07:00.97 24
rate EuroNCAP virtually.
-
00:07:00.97 - 00:07:04.23 39
And improve the robustness of your ADAS
-
00:07:04.23 - 00:07:05.46 21
function whether it's
-
00:07:05.53 - 00:07:08.79 47
emergency breaking, pedestrian crossing, closed
-
00:07:08.79 - 00:07:09.74 11
cut in etc.
-
00:07:09.74 - 00:07:13.36 42
The aim is to test the functions algorithm
-
00:07:13.36 - 00:07:13.68 6
and to
-
00:07:13.76 - 00:07:17.39 40
be able to evaluate the vehicle dynamics
-
00:07:17.39 - 00:07:18.68 20
reaction in context.
-
00:07:18.68 - 00:07:19.71 18
In order to do so,
-
00:07:19.71 - 00:07:22.18 41
we need to perform closed loop simulation
-
00:07:22.18 - 00:07:22.95 17
with the function
-
00:07:23.01 - 00:07:23.67 12
of the task,
-
00:07:23.67 - 00:07:27.29 45
the vehicle dynamics and the driving scenario
-
00:07:27.29 - 00:07:27.38 1
.
-
00:07:27.38 - 00:07:30.31 41
We can approach the NCAP certification in
-
00:07:30.31 - 00:07:31.09 12
three steps.
-
00:07:31.09 - 00:07:31.49 6
First,
-
00:07:31.49 - 00:07:34.17 39
support the autonomous driving function
-
00:07:34.17 - 00:07:37.39 34
developers with a fast development
-
00:07:37.39 - 00:07:39.18 35
verification and validation loop to
-
00:07:39.25 - 00:07:42.46 40
execute many tasks to tune their control
-
00:07:42.46 - 00:07:43.68 21
algorithms and ensure
-
00:07:43.75 - 00:07:44.97 17
their robustness.
-
00:07:44.97 - 00:07:47.78 40
We have augmented our Ansys VREXPERIENCE
-
00:07:47.78 - 00:07:49.10 27
driving simulator with NCAP
-
00:07:49.16 - 00:07:51.98 41
development scenario package containing a
-
00:07:51.98 - 00:07:53.85 29
digital representation of the
-
00:07:53.92 - 00:07:56.55 39
NCAP testing environment and scenarios.
-
00:07:59.00 - 00:08:02.14 47
In the second step, the V&V engineers can use a
-
00:08:02.21 - 00:08:05.35 36
certified version of this package to
-
00:08:05.35 - 00:08:06.82 29
guarantee faithfulness of the
-
00:08:06.89 - 00:08:08.01 16
digital testing.
-
00:08:08.01 - 00:08:08.74 12
And finally,
-
00:08:08.74 - 00:08:11.19 40
we can put the car on the proving ground
-
00:08:11.25 - 00:08:14.00 43
with a very high level of confidence in the
-
00:08:14.00 - 00:08:14.37 7
correct
-
00:08:14.43 - 00:08:16.51 34
behavior of the function and test.
-
00:08:16.51 - 00:08:19.07 42
The NCAP development scenario package will
-
00:08:19.07 - 00:08:19.75 11
help you in
-
00:08:19.81 - 00:08:22.26 40
deriving any NCAP scenario from the NCAP
-
00:08:22.26 - 00:08:24.83 40
specs. Of course you won't have to start
-
00:08:24.83 - 00:08:25.00 4
from
-
00:08:25.06 - 00:08:26.37 22
scratch as the package
-
00:08:26.37 - 00:08:29.92 44
come with the library of readymade scenarios
-
00:08:29.92 - 00:08:30.00 1
.
-
00:08:30.00 - 00:08:32.75 44
You'll be able to make quick changes and try
-
00:08:32.75 - 00:08:33.30 9
different
-
00:08:33.36 - 00:08:36.12 41
parameters on the fly to understand their
-
00:08:36.12 - 00:08:36.55 10
influence.
-
00:08:36.55 - 00:08:39.52 43
As the number of parameters to play with is
-
00:08:39.52 - 00:08:39.79 5
quite
-
00:08:39.85 - 00:08:40.65 12
significant,
-
00:08:40.65 - 00:08:43.92 40
you'll be able to use HPC to run massive
-
00:08:43.92 - 00:08:44.28 9
scenarios
-
00:08:44.36 - 00:08:47.63 43
and you'll be able to simulate a variety of
-
00:08:47.63 - 00:08:48.43 12
environments
-
00:08:48.51 - 00:08:51.13 36
such as various lighting conditions.
-
00:08:51.13 - 00:08:54.13 38
So. Let's have a look at what the NCAP
-
00:08:54.20 - 00:08:56.84 36
developments value package provides.
-
00:08:56.84 - 00:09:00.05 43
A set of test protocols for front collision
-
00:09:00.05 - 00:09:00.77 11
warning and
-
00:09:00.84 - 00:09:04.05 38
automatic emergency breaking functions
-
00:09:04.05 - 00:09:05.91 32
covering a variety of use cases.
-
00:09:05.91 - 00:09:06.96 11
Car to car,
-
00:09:06.96 - 00:09:10.89 41
adult or child pedestrians, and bicycles.
-
00:09:10.89 - 00:09:13.75 34
It has a high fidelity to the NCAP
-
00:09:13.75 - 00:09:14.07 12
environment,
-
00:09:14.07 - 00:09:16.91 35
as the 3D modeling follows the NCAP
-
00:09:16.91 - 00:09:17.66 18
specifications and
-
00:09:17.72 - 00:09:20.56 44
the material optical properties are measured
-
00:09:20.56 - 00:09:22.63 33
with specific optical measurement
-
00:09:22.70 - 00:09:23.20 8
devices,
-
00:09:23.20 - 00:09:26.01 44
enabling physics-based simulations including
-
00:09:26.01 - 00:09:26.83 14
their sensors.
-
00:09:30.33 - 00:09:32.78 39
From the official NCAP protocol we have
-
00:09:32.78 - 00:09:33.32 12
developed 14
-
00:09:33.38 - 00:09:34.36 18
scenario families,
-
00:09:34.36 - 00:09:37.31 42
each one containing more than 10 variables
-
00:09:37.31 - 00:09:37.96 12
and at least
-
00:09:38.03 - 00:09:40.00 30
10 key performance indicators.
-
00:09:40.00 - 00:09:42.97 42
These permits to build about 150 NCAP test
-
00:09:42.97 - 00:09:43.36 5
cases
-
00:09:43.43 - 00:09:46.40 44
and to produce and execute millions of tests
-
00:09:46.40 - 00:09:47.13 11
for control
-
00:09:47.19 - 00:09:49.84 37
algorithm tuning and robustness test.
-
00:09:52.01 - 00:09:53.84 24
To test a ADAS function,
-
00:09:53.84 - 00:09:57.56 43
we have to connect it both to its simulator
-
00:09:57.56 - 00:09:57.73 4
sets
-
00:09:57.81 - 00:10:00.96 38
or efforts and it's activated outputs.
-
00:10:00.96 - 00:10:02.12 20
In this first video,
-
00:10:02.12 - 00:10:04.75 44
the sensor detecting the pedestrian presence
-
00:10:04.81 - 00:10:07.44 43
is not connected to the AEB function at the
-
00:10:07.44 - 00:10:07.62 5
test.
-
00:10:07.62 - 00:10:11.32 37
Obviously the system does not see the
-
00:10:11.32 - 00:10:12.31 19
pedestrian and runs
-
00:10:12.39 - 00:10:13.14 9
over him.
-
00:10:13.14 - 00:10:15.55 45
If we close the loop by connecting the sensor
-
00:10:15.55 - 00:10:15.71 2
to
-
00:10:15.76 - 00:10:17.05 24
the function under test,
-
00:10:17.05 - 00:10:19.48 43
we can witness the correct execution of the
-
00:10:19.48 - 00:10:19.91 9
function.
-
00:10:19.91 - 00:10:22.24 44
The car stops before hitting the pedestrian.
-
00:10:24.96 - 00:10:27.77 38
Now let's introduce variability in our
-
00:10:27.77 - 00:10:28.15 12
simulations.
-
00:10:30.36 - 00:10:32.62 32
Let's just choose one parameter.
-
00:10:32.62 - 00:10:33.73 16
In this example,
-
00:10:33.73 - 00:10:36.59 40
it's the initial speed of the overtaking
-
00:10:36.66 - 00:10:36.94 4
car,
-
00:10:36.94 - 00:10:39.78 41
and we're going to set a range from 50 to
-
00:10:39.85 - 00:10:42.96 41
80 kilometers per hour and an incremental
-
00:10:42.96 - 00:10:43.58 12
step of five
-
00:10:43.65 - 00:10:45.38 25
kilometers per hour here.
-
00:10:45.38 - 00:10:50.21 45
And so this is going to generate 7 scenarios.
-
00:10:50.21 - 00:10:52.99 45
We can execute them one after the other or in
-
00:10:53.05 - 00:10:55.59 41
parallel batches with the adequate setup,
-
00:10:55.59 - 00:10:58.71 43
and for each of these cases we will be able
-
00:10:58.79 - 00:11:02.05 40
to collect key performance indicators to
-
00:11:02.05 - 00:11:03.72 27
understand the cause effect
-
00:11:03.79 - 00:11:07.06 43
relationship between the parameters and the
-
00:11:07.06 - 00:11:07.57 5
KPIs.
-
00:11:07.57 - 00:11:10.29 42
This works of course for a wider number of
-
00:11:10.29 - 00:11:10.77 10
parameters
-
00:11:10.83 - 00:11:12.77 31
and can quickly generate a very
-
00:11:12.77 - 00:11:15.55 33
a very large number of scenarios.
-
00:11:15.55 - 00:11:18.31 42
High performance computing setups can help
-
00:11:18.31 - 00:11:19.90 28
executing these large number
-
00:11:19.96 - 00:11:20.52 9
of cases.
-
00:11:20.52 - 00:11:23.42 40
Smart sensitivity analysis tools such as
-
00:11:23.42 - 00:11:24.39 18
optiSLang can also
-
00:11:24.46 - 00:11:27.37 45
be connected to reduce the number of actually
-
00:11:27.37 - 00:11:28.34 14
executed tests
-
00:11:28.40 - 00:11:31.31 44
while providing a very precise understanding
-
00:11:31.31 - 00:11:32.41 17
of the effects of
-
00:11:32.41 - 00:11:34.53 29
the parameters over the KPIs.
-
00:11:34.53 - 00:11:37.61 38
The NCAP digital certification package
-
00:11:37.61 - 00:11:39.11 26
certified by UATC augments
-
00:11:39.18 - 00:11:42.26 34
the previous package with the true
-
00:11:42.26 - 00:11:43.49 28
reproduction of actual tests
-
00:11:43.56 - 00:11:45.07 22
on the proving ground.
-
00:11:45.07 - 00:11:47.91 42
There is less than one-centimeter error in
-
00:11:47.91 - 00:11:48.86 17
target trajectory
-
00:11:48.92 - 00:11:49.75 13
reproduction.
-
00:11:49.75 - 00:11:52.95 38
This package is really dedicated for a
-
00:11:52.95 - 00:11:54.09 22
digital pre validation
-
00:11:54.16 - 00:11:57.37 42
and produces a prefilled report to present
-
00:11:57.37 - 00:11:58.22 14
the vehicle to
-
00:11:58.29 - 00:12:00.15 23
the NCAP certification.
-
00:12:00.15 - 00:12:02.43 36
Once the ADAS function is validated,
-
00:12:02.43 - 00:12:05.34 42
the limitations of sensing capabilities of
-
00:12:05.34 - 00:12:06.45 19
the systems need to
-
00:12:06.51 - 00:12:07.94 22
be taken into account.
-
00:12:07.94 - 00:12:08.57 11
To do this,
-
00:12:08.57 - 00:12:10.99 41
we're going to replace in our simulations
-
00:12:11.05 - 00:12:13.65 46
ground truth sensors by physics based sensors.
-
00:12:16.13 - 00:12:19.14 40
We can easily start with NCAP regulation
-
00:12:19.14 - 00:12:20.42 20
inspired geometries.
-
00:12:20.42 - 00:12:24.22 45
And extend the materials with precise optical
-
00:12:24.22 - 00:12:26.25 23
parameters available in
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00:12:26.33 - 00:12:30.14 39
the visible range and near infrared and
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00:12:30.14 - 00:12:31.91 26
including retro reflective
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00:12:31.99 - 00:12:32.76 9
material.
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00:12:32.76 - 00:12:36.36 44
With this we can evaluate very precisely the
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00:12:36.36 - 00:12:37.41 13
sensor output
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00:12:37.49 - 00:12:41.09 37
in static simulations thanks to Ansys
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00:12:41.09 - 00:12:41.90 13
capabilities.
-
00:12:41.90 - 00:12:42.67 12
We can also,
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00:12:42.67 - 00:12:45.05 36
at the expense of a slightly reduced
-
00:12:45.11 - 00:12:45.69 9
fidelity,
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00:12:45.69 - 00:12:48.59 43
simulate the sensors in a real time dynamic
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00:12:48.59 - 00:12:49.24 11
simulation,
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00:12:49.24 - 00:12:52.83 39
and for a wide variety of sensor types.
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00:12:52.83 - 00:12:54.32 22
For these simulations,
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00:12:54.32 - 00:12:56.84 36
we do provide all the needed assets.
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00:12:56.84 - 00:12:59.88 41
Pedestrians with and without safety vests
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00:12:59.88 - 00:13:01.91 33
most frequent standardized scenes
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00:13:01.98 - 00:13:04.54 38
in the automotive industry, a variety,
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00:13:04.54 - 00:13:06.33 22
of road intersections,
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00:13:06.33 - 00:13:09.82 38
and typical models for headlamp beams.
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00:13:09.82 - 00:13:12.68 43
We also provide a library of useful optical
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00:13:12.68 - 00:13:13.26 10
materials.
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00:13:13.26 - 00:13:14.78 26
Road asphalt and markings,
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00:13:14.78 - 00:13:17.06 35
which have been adapted from French
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00:13:17.12 - 00:13:18.51 22
academic publications.
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00:13:18.51 - 00:13:21.33 47
Road signs with the standard industry coatings,
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00:13:21.33 - 00:13:24.19 39
police plate definitions that have been
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00:13:24.19 - 00:13:25.52 26
measured from real plates.
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00:13:25.52 - 00:13:28.28 35
Safety vests featuring standardized
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00:13:28.28 - 00:13:31.18 42
retroreflective and fluorescent materials,
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00:13:31.18 - 00:13:33.54 29
and we complete all that with
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00:13:33.62 - 00:13:37.15 41
typical car paintings and environment and
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00:13:37.15 - 00:13:37.63 9
elements.
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00:13:37.63 - 00:13:40.59 45
Inserting the sensor into the simulation loop
-
00:13:40.59 - 00:13:42.03 21
permits to understand
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00:13:42.10 - 00:13:44.67 39
the effect of its inherent limitations.
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00:13:44.67 - 00:13:46.76 32
Let's have a look at an example.
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00:13:46.76 - 00:13:48.28 26
In this very simple scene,
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00:13:48.28 - 00:13:51.34 40
we're going to have pedestrians crossing
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00:13:51.34 - 00:13:52.16 16
while the car is
-
00:13:52.23 - 00:13:54.28 30
cruising at increasing speeds.
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00:13:54.28 - 00:13:57.38 43
On the left hand side we can see the output
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00:13:57.46 - 00:13:58.98 21
of the camera sensor.
-
00:13:58.98 - 00:14:02.03 41
On the right hand side is the output of a
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00:14:02.11 - 00:14:05.46 38
perception algorithm executed over the
-
00:14:05.46 - 00:14:06.81 24
simulated sensor output.
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00:14:06.81 - 00:14:08.83 32
As the visibility is very clear,
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00:14:08.83 - 00:14:11.62 38
we can see that the car recognizes the
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00:14:11.62 - 00:14:12.18 15
pedestrian from
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00:14:12.24 - 00:14:12.87 10
far enough
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00:14:12.87 - 00:14:14.40 25
to have the time to stop.
-
00:14:17.84 - 00:14:19.71 29
While we analyze the results,
-
00:14:19.71 - 00:14:22.85 40
we see that only at the highest peak the
-
00:14:22.92 - 00:14:26.13 45
AEB is not capable to stop before hitting the
-
00:14:26.13 - 00:14:26.99 11
pedestrian.
-
00:14:26.99 - 00:14:27.30 4
Now,
-
00:14:27.30 - 00:14:31.99 41
if we change our conditions and rerun the
-
00:14:32.09 - 00:14:35.64 36
same set of tests but at night time.
-
00:14:35.64 - 00:14:38.34 43
We have street lamps and low beams lighting
-
00:14:38.34 - 00:14:38.83 10
the scene.
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00:14:40.85 - 00:14:44.40 41
The perception will be able to detect the
-
00:14:44.40 - 00:14:45.35 15
pedestrian much
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00:14:45.43 - 00:14:48.98 41
later and hence collisions will happen at
-
00:14:48.98 - 00:14:50.17 18
much lower speeds.
-
00:14:56.84 - 00:14:59.51 45
This capability to work at multiple levels of
-
00:14:59.51 - 00:15:00.22 11
fidelity is
-
00:15:00.28 - 00:15:01.83 26
something quite important.
-
00:15:01.83 - 00:15:04.18 37
You do 80% of the testing on the pure
-
00:15:04.18 - 00:15:07.52 43
ADAS control software using ground truth or
-
00:15:07.52 - 00:15:09.30 25
stochastic sensor models.
-
00:15:09.30 - 00:15:10.60 23
Once this is validated,
-
00:15:10.60 - 00:15:12.14 26
you're going to do the 20%
-
00:15:12.14 - 00:15:15.36 41
of remaining testing to understand sensor
-
00:15:15.36 - 00:15:16.51 19
limitation effects.
-
00:15:16.51 - 00:15:18.99 42
Note that the simulated sensor limitations
-
00:15:18.99 - 00:15:20.04 21
have to be correlated
-
00:15:20.09 - 00:15:22.58 40
with the safety of the intended function
-
00:15:22.58 - 00:15:22.86 9
analysis.
-
00:15:25.33 - 00:15:28.86 44
As a rule, the numbers of sensor is bound to
-
00:15:28.86 - 00:15:29.80 13
significantly
-
00:15:29.88 - 00:15:30.83 11
increase as
-
00:15:30.83 - 00:15:33.22 36
levels of autonomy are also raising.
-
00:15:33.22 - 00:15:36.16 35
We expect the role of physics based
-
00:15:36.16 - 00:15:36.94 21
simulation of sensors
-
00:15:37.01 - 00:15:39.23 34
to become more and more important.
-
00:15:39.23 - 00:15:42.26 38
So let's summarize with your benefits.
-
00:15:42.26 - 00:15:45.97 41
As of 2020 NCAP testing for two functions
-
00:15:45.97 - 00:15:46.30 4
will
-
00:15:46.38 - 00:15:50.09 37
cost you 4 new vehicles and about six
-
00:15:50.09 - 00:15:50.59 11
millions of
-
00:15:50.67 - 00:15:51.58 11
US dollars.
-
00:15:51.58 - 00:15:52.14 8
In 2025,
-
00:15:52.14 - 00:15:54.94 39
a typical car will rather have about 12
-
00:15:55.01 - 00:15:56.27 18
functions to test.
-
00:15:56.27 - 00:15:58.69 34
You'll still need 4 physical cars,
-
00:15:58.69 - 00:16:02.52 45
but the higher number of functions will bring
-
00:16:02.52 - 00:16:03.37 9
your cost
-
00:16:03.45 - 00:16:05.58 25
in the 20 millions range.
-
00:16:05.58 - 00:16:09.30 45
That's a three times increase of cost in less
-
00:16:09.30 - 00:16:09.71 4
than
-
00:16:09.80 - 00:16:10.71 11
five years.
-
00:16:10.71 - 00:16:13.40 36
Without Ansys, you can reduce by 50%
-
00:16:13.40 - 00:16:14.86 20
your physical tests,
-
00:16:14.86 - 00:16:17.48 35
reduce the risk and secure you five
-
00:16:17.55 - 00:16:18.50 13
stars rating.
-
00:16:20.86 - 00:16:23.68 44
If we illustrate by a concrete example here,
-
00:16:23.68 - 00:16:26.49 35
the process used by Renault Nissan.
-
00:16:26.49 - 00:16:29.58 39
Up to 2018 they were developing a first
-
00:16:29.58 - 00:16:30.26 14
prototype that
-
00:16:30.33 - 00:16:33.42 36
they were using to test the function
-
00:16:33.42 - 00:16:34.18 19
internally over the
-
00:16:34.25 - 00:16:35.97 23
314 EuroNCAP use cases.
-
00:16:35.97 - 00:16:38.79 44
Then it would develop a second prototype and
-
00:16:38.79 - 00:16:39.48 11
validate it
-
00:16:39.55 - 00:16:42.37 38
the first time on the EuroNCAP proving
-
00:16:42.37 - 00:16:42.63 8
grounds.
-
00:16:44.70 - 00:16:47.66 45
With this test results and additional testing
-
00:16:47.66 - 00:16:47.72 1
,
-
00:16:47.72 - 00:16:51.49 43
they would fine tune the system until it is
-
00:16:51.49 - 00:16:51.82 5
ready
-
00:16:51.90 - 00:16:55.25 40
for an official EuroNCAP second testing.
-
00:16:55.25 - 00:16:58.12 40
Since this year they had diminished that
-
00:16:58.12 - 00:16:59.21 21
physical testing cost
-
00:16:59.27 - 00:17:00.23 15
and time by 50%
-
00:17:00.23 - 00:17:03.38 40
by replacing the first NCAP test and the
-
00:17:03.38 - 00:17:03.73 6
tuning
-
00:17:03.80 - 00:17:06.82 46
phases by simulation and digital optimization.
-
00:17:06.82 - 00:17:09.74 44
They can get their cars on the market faster
-
00:17:09.74 - 00:17:09.94 3
and
-
00:17:10.00 - 00:17:11.04 16
at a lower cost.
-
00:17:11.04 - 00:17:11.37 5
Well,
-
00:17:11.37 - 00:17:14.05 40
we reached the end of this presentation.
-
00:17:14.05 - 00:17:16.16 32
I hope you found it interesting.
-
00:17:16.16 - 00:17:19.10 43
Don't hesitate to contact us if you want to
-
00:17:19.10 - 00:17:19.30 4
know
-
00:17:19.36 - 00:17:19.69 5
more.
-
00:17:19.69 - 00:17:21.42 32
Thank you for attention bye-bye.