Uber wants to turn its millions of drivers into a sensor grid for self-driving companies
Uber wants to turn its millions of drivers into a sensor grid for self-driving companies
Uber 希望将其数百万名司机转化为自动驾驶公司的传感器网络
Uber has a long-term ambition that goes well beyond shuttling passengers: the company eventually wants to outfit its human drivers’ cars with sensors to soak up real-world data for autonomous vehicle (AV) companies — and potentially other companies training AI models on physical-world scenarios. Uber 有一个远超运送乘客的长期愿景：该公司最终希望为其人类司机的车辆配备传感器，以收集真实世界的数据，供自动驾驶（AV）公司使用，并可能为其他在物理世界场景中训练 AI 模型的公司提供服务。
Praveen Neppalli Naga, Uber’s chief technology officer, revealed the plan in an interview at TechCrunch’s StrictlyVC event in San Francisco on Thursday night, describing it as a natural extension of a nascent program the company announced in late January called AV Labs. Uber 首席技术官 Praveen Neppalli Naga 在周四晚于旧金山举行的 TechCrunch StrictlyVC 活动上接受采访时透露了这一计划。他将其描述为该公司在 1 月下旬宣布的一项名为“AV Labs”的新兴项目的自然延伸。
“That is the direction we want to go eventually,” Naga said of equipping human drivers’ vehicles. “But first we need to get the understanding of the sensor kits and how they all work. There are some regulations — we have to make sure every state has [clarity on] what sensors mean, and what sharing it means.” “这就是我们最终想要发展的方向，”Naga 在谈到为人类司机的车辆配备传感器时说道。“但首先，我们需要了解传感器套件及其工作原理。这涉及一些法规问题——我们必须确保每个州都对传感器的含义以及数据共享的含义有明确的界定。”
For now, AV Labs relies on a small, dedicated fleet of sensor-equipped cars that Uber operates itself, separate from its driver network. But the ambition is clearly much larger. Uber has millions of drivers globally, and if even a fraction of those cars could be transformed into rolling data-collection platforms, the scale of what Uber could offer the AV industry would dwarf what any individual AV company could assemble on its own. 目前，AV Labs 依赖于一支由 Uber 自行运营的小型专用传感器车辆车队，这与现有的司机网络是分开的。但其雄心显然远不止于此。Uber 在全球拥有数百万名司机，如果这些车辆中哪怕只有一小部分能被转化为移动的数据收集平台，Uber 所能提供给自动驾驶行业的规模，将远超任何一家自动驾驶公司独立组建的规模。
The insight driving the program, Naga said, is that the limiting factor for AV development is no longer the underlying technology. “The bottleneck is data,” he said. “[Companies like Waymo] need to go around and collect the data, collect different scenarios. You may be able to say: in San Francisco, ‘At this school intersection, I want some data at this time of day so I can train my models.’ The problem for all these companies is access to that data, because they don’t have the capital to deploy the cars and go collect all this information.” Naga 表示，推动该项目的核心洞察是：自动驾驶发展的限制因素已不再是底层技术。“瓶颈在于数据，”他说。“（像 Waymo 这样的公司）需要四处奔波去收集数据，收集不同的场景。你可能想说：在旧金山，‘在这一天的这个时间点，我想要这个学校路口的数据来训练我的模型。’所有这些公司面临的问题都是如何获取这些数据，因为他们没有足够的资金去部署车辆并收集所有这些信息。”
Becoming the data layer for the entire AV ecosystem is a pretty smart play, particularly considering Uber years ago abandoned its own ambitions to build self-driving cars (a move that co-founder Travis Kalanick has publicly lamented as a big mistake). Indeed, many industry observers have wondered if, without its own self-driving cars, Uber might one day be rendered irrelevant as AVs increasingly spring up around the globe. 成为整个自动驾驶生态系统的数据层是一个非常聪明的策略，特别是考虑到 Uber 多年前就放弃了自己制造自动驾驶汽车的雄心（联合创始人 Travis Kalanick 曾公开表示这是一个巨大的错误）。事实上，许多行业观察家曾担心，如果没有自己的自动驾驶汽车，随着全球自动驾驶技术的日益普及，Uber 有朝一日可能会变得无关紧要。
The company currently has partnerships with 25 AV companies — including Wayve, which operates in London — and is building what Naga described as an “AV cloud”: a library of labeled sensor data that partner companies can query and use to train their models. Partners, which Uber plans to more aggressively invest in directly, can also use the system to run their trained models in “shadow mode” against real Uber trips, simulating how an AV would have performed without actually putting one on the road. 该公司目前与 25 家自动驾驶公司建立了合作伙伴关系（包括在伦敦运营的 Wayve），并正在构建 Naga 所描述的“自动驾驶云”：一个包含已标注传感器数据的库，合作伙伴公司可以查询并利用这些数据来训练模型。Uber 计划更积极地直接投资这些合作伙伴，他们还可以利用该系统在真实的 Uber 行程中以“影子模式”运行其训练好的模型，从而在无需实际投放车辆的情况下，模拟自动驾驶汽车的运行表现。
“Our goal is not to make money out of this data,” Naga said. “We want to democratize it.” Given the obvious commercial value of what Uber is building, that positioning may not last long. The company has already made equity investments in numerous AV players, and its ability to offer proprietary training data at scale could give it significant leverage over a sector that right now depends on Uber’s ride marketplace to reach customers. “我们的目标不是靠这些数据赚钱，”Naga 说。“我们希望实现数据的民主化。”考虑到 Uber 正在构建的事物具有明显的商业价值，这种定位可能不会持续太久。该公司已经对多家自动驾驶参与者进行了股权投资，其大规模提供专有训练数据的能力，可能会使其在目前依赖 Uber 乘车市场来触达客户的行业中占据显著的优势地位。