Embark 的自动驾驶卡车通过先进的道路测试克服了天气挑战
技术
- 机器人 - 自动导引车 (AGV)
- 传感器 - 自动驾驶传感器
适用行业
- 汽车
- 运输
适用功能
- 物流运输
- 质量保证
用例
- 自主运输系统
- 实验自动化
服务
- 测试与认证
关于客户
Embark Trucks 是美国运营时间最长的自动驾驶卡车项目，自 2016 年以来一直是卡车自动驾驶技术的先行者。他们的软件为安全、商业上可行的自动长途卡车提供支持，受到投资者和财富 500 强托运人的信赖，以及一些全国最大的运营商。 Embark 的自动驾驶卡车使用激光雷达、雷达和光学摄像头等传感技术来收集周围环境的视觉数据，然后与地图和算法相结合，在路上做出决策。
挑战
美国的公路物流运营商经常因无法在恶劣天气条件下运营而面临收入损失，特别是在北部各州。自动卡车（例如 Embark Trucks 开发的卡车）具有改变行业的潜力，但它们也面临着自己的挑战。自动驾驶汽车 (AV) 依靠激光雷达、雷达和光学摄像头等传感技术从周围环境收集视觉数据，然后将其与地图和算法结合起来做出决策。然而，即使是最先进的传感技术也难以在恶劣天气下准确检测和解释路况。 Embark 不仅需要道路测试，还需要准确、完整的历史天气数据集，以充分了解此类条件对其自动驾驶解决方案的影响。
解决方案
Embark 转向 AerisWeather Flex 订阅来获取精细的天气数据，全天频繁地获取基于位置的间隔数据。这使得 Embark 能够开发出一个综合天气模型，该模型由超过 80 亿个历史天气数据点组成，这些数据点可以追溯到 10 多年前，适用于美国各地的主要航线。该模型增强了 Embark 对其自动卡车评估车道积雪影响并做出反应能力的信心。该数据还与高速公路要求相结合，例如要求高速公路在停雪后三小时内清理干净。这使得 Embark 能够估计任何给定地点的高速公路何时被清理。卡车在下雪条件下进行了试驾，卡车传感器的数据与 AerisWeather 的插值条件进行交叉引用，从而使 Embark 能够确定下雪条件可能产生的业务影响。
运营影响
数量效益
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