创建全天候驾驶员
Creating an all-weather driver

原始链接: https://waymo.com/blog/2025/10/creating-an-all-weather-driver

Waymo 正在积极开发其自动驾驶技术,以可靠地应对冬季天气条件——雨、雾、冰冻温度,以及现在,雪。他们的方案侧重于一个四步流程:理解雪和冰的复杂性,设计适应性解决方案,严格验证,以及负责任地规模化。 Waymo Driver 利用摄像头、雷达和激光雷达的组合,以及自动清洁系统,来感知和导航具有挑战性的环境。先进的人工智能区分路面(雪、冰、泥泞),并相应地调整驾驶行为——速度、加速、制动。每辆车都充当移动气象站,与整个车队共享数据。 验证通过在纽约州北部和密歇根州等雪地地区的实际驾驶、推动系统极限的封闭场地测试以及大量的模拟进行。Waymo 优先考虑安全性,仅在经过彻底测试并根据当地条件建立明确的操作指南后才扩大服务范围。他们的目标是在天气最恶劣时也能提供一致、可靠的交通运输。

## Waymo 全天候驾驶挑战 - 摘要 一场 Hacker News 讨论围绕着创造真正全天候自动驾驶车辆的挑战,起因是 Waymo 向纽约州北部和布法罗等恶劣冬季条件地区扩张。 用户分享了经验,强调即使是人类驾驶员在应对雪和冰等困难条件时也面临挑战,回忆起驾驶考试失败以及学习技能的重要性。关于最佳传感器方法的争论浮出水面——仅依靠视觉是否足够,或者激光雷达和其他传感器对于可靠的性能至关重要。 对话还涉及自动驾驶车辆与人类交通管制(如警察手势)的交互复杂性,以及对健全授权系统的需求。 多位评论员强调了选择合适的轮胎和驾驶技能的重要性,并将美国对大型车辆的依赖与其它地区更注重轮胎和灵活性的方法进行了对比。最终,讨论指出自动驾驶性能的高标准——超越人类能力——以及复制真实世界驾驶专业知识的持续挑战。
相关文章

原文

Life doesn't freeze when winter comes—if anything, that's when riders need reliable transportation most, when being exposed to the elements becomes less appealing. Today, the Waymo Driver successfully navigates rain, fog, sandstorms, and freezing temperatures. As we expand to more cities across the U.S. and globally, we're applying the same systematic, scientific approach that enabled us to validate the Waymo Driver for these conditions to advance our capabilities for snowier, winter weather.

Our proven, safety-guided methodology involves four key steps:

Understanding the Challenge

Snow isn't a single phenomenon—it's a spectrum of conditions that can affect a human or autonomous driver in multiple ways. Atmospheric conditions can range from a light dusting to a complete whiteout, while road surfaces may be snow-covered or have icy patches, and environmental factors like snow buildup along roadsides add further complexity. For years, we've been advancing our system in some of the snowiest conditions across the country —regularly driving in Upstate New York, Michigan's Upper Peninsula, and the Sierra. We've amassed tens of thousands of miles in diverse, snowy conditions. This has allowed the Waymo Driver’s AI to learn from real driving experience and train to navigate a wide range of winter weather.

Defining the different types of winter road conditions from icy streets (right) and well-plowed roads (second from the right) to tire tracks and light dustings to falling snow and slushy streets (right).

Designing Generalizable Solutions

At Waymo, we're building one autonomous system that works across diverse conditions—the same Waymo Driver navigating foggy San Francisco can navigate snowy Denver. Our 6th-generation Driver is informed by over 100 million fully autonomous miles of driving experience, combining state-of-the-art hardware and AI to adapt to and sustain fully autonomous operations in cities with harsher weather.

The Waymo Driver uses cameras, radar, and lidar to perceive the world around it, with each sensor providing a complementary field of view that's especially helpful in inclement weather. Its automated cleaning system –using clever engineering and heating elements – keeps the sensors clear so the vehicle can continue serving riders without needing to pull over.

Our system provides context not only about where it's operating, but also about the conditions it’s operating under. We're creating state-of-the-art AI, building on top of our existing models with richer inputs and advanced capabilities designed to navigate winter conditions. For example, our AI can distinguish between where there's snow, slush, ice, and normal road surface. The Waymo Driver then uses this information to adjust its driving behavior to match the road conditions in real-time, allowing the Waymo Driver to navigate based on what it sees (and feels), also inferring insights from other road users—adapting to blocked roads, detours, and changing surface conditions. When the system detects lower traction, it automatically adjusts its speed, acceleration, and braking. Each vehicle essentially acts as a mobile weather station, gathering data to inform its own driving decisions and share with the rest of the fleet in the city. These responses are consistent and thoroughly tested, providing predictable and safe navigation in challenging conditions.

Rigorously Validating Our Capabilities

We validate our generalizable system through real-world driving, closed-course testing, and large-scale simulation. With our growing operations in snowy cities like Detroit, Denver, and Washington D.C., in addition to visits  to other areas, we're deepening our understanding of winter weather conditions and validating our capabilities. At closed-course testing facilities, we push the system to its limits in controlled environments, teaching it to recognize and respond to extreme scenarios like losing traction on ice. Then, we expand our learning year-round through simulation, long after the last snowflake has melted, so the Waymo Driver is prepared for rare and unusual events, like once-in-100-year snow New Orleans experienced this past winter.

Waymo testing its hardware and AI through a combination of structured testing (left), simulation (middle), and real-world driving (right).

Scaling Responsibly
Once we've validated our technology and operations by our Safety Framework and high caliber for rider excellence, we expand our service with clear guidelines about when our vehicles will operate based on local conditions. As we scale, we're also refining our operations to support winter service—from keeping our fleet clean and charged in freezing temperatures to optimizing the rider experience. Winter weather is complex, but we're committed to providing reliable service when riders need it most. As we continue expanding to more cities around the world, our progress is guided by safety, and riders can trust that the Waymo Driver is ready when we open our doors.

Looking for an all-weather Driver instead of all-weather tires?  Follow along on our progress to bring Waymo to more cities at waymo.com/updates.

联系我们 contact @ memedata.com