Self-driving cars are as fascinating as they are challenging to develop. The sensors, compute, actuation all need to continuously work together to understand the surrounding environment and respond in real-time. And when it all works as expected, we will save lives and change society for the better.

小号精灵驾驶汽车是一样迷人，他们是具有挑战性的发展。 传感器，计算，执行机构都需要持续协作以了解周围环境并实时做出响应。 当一切都按预期进行时，我们将拯救生命并改善社会。

Back in 2013, Tesla, while still struggling, decided to take on this challenge. All in the midst of posting huge operating losses, struggling to manufacture cars and scale production. It’s worth considering:

早在2013年， 特斯拉仍在挣扎中，但决定接受这一挑战。 所有这些都处在巨大的运营亏损之中，努力制造汽车和扩大规模。 值得考虑：

What was Tesla’s strategy, and why?

特斯拉的策略是什么，为什么？

How has it evolved over the years?

这些年来它是如何发展的？

Fast forward to today and Tesla has a large fleet on the road with self-driving features. Not to mention, Tesla has one of the highest attach rate for self-driving features, the most diverse self-driving fleet by geography, and one of the most diverse self-driving fleets by vehicle type.

快进到今天，特斯拉拥有自动驾驶功能的庞大车队。 更不用说，特斯拉拥有自动驾驶功能的最高附加率之一，按地理分布最多样化的自动驾驶车队以及按车型划分的最多样化的自动驾驶车队之一。

So, how does a struggling automaker achieve such impressive results in less than a decade?

那么，苦苦挣扎的汽车制造商如何在不到十年的时间内取得如此骄人的成绩？

It all boils down to two of Tesla’s biggest strengths, including Elon Musk’s first principles-based thinking and Tesla’s sheer execution prowess. Those and five core pillars to which Tesla’s success can be attributed.

一切都归结为特斯拉最大的两个优势，包括埃隆·马斯克(Elon Musk)的第一个基于原则的思维和特斯拉的纯粹执行能力。 特斯拉的成功可以归因于这五个核心Struts。

1.一种架构-适用于所有车辆 (1. One architecture — across all vehicles)

Starting October 2016, all Tesla vehicles sold are equipped with the hardware necessary for full self-driving — sensors including camera, radar, GPS, ultrasonics, and the onboard computer.

从2016年10月开始，所有售出的Tesla车辆都配备了完全自动驾驶所必需的硬件-传感器，包括摄像头，雷达，GPS，超声波和车载计算机。

This is counterintuitive to how the automotive industry works, where every last bit of margin is squeezed. Why equip vehicles with extra hardware that the customer specifically didn’t ask for? Turns out, there are quite a few benefits.

这与汽车行业的运作方式有悖常理，因为汽车行业的最后一点利润都受到挤压。 为什么要为车辆配备客户特别不需要的额外硬件？ 事实证明，有很多好处。

打开软件收入之门 (Opening the door to software revenues)

This is a very common strategy in the tech world — supply the hardware for cheap and milk revenues by selling software. It’s why Amazon sold echo devices for under $50. But the benefits of this strategy in the automotive world are only magnified. As you can always buy another smart speaker, but your car stays with you much longer.

这是技术世界中非常普遍的策略-通过销售软件来提供廉价的硬件和牛奶收入。 这就是为什么亚马逊以不到50美元的价格出售回声设备的原因。 但是，这种策略在汽车领域的好处只是被放大了。 因为您总是可以购买另一个智能扬声器，但是您的汽车在您身上的停留时间更长。

Say the full self-driving hardware costs Tesla around $1000 per car. ($600 for compute, $400 for sensors + wiring). At 400k vehicles per year, the total cost to Tesla is $400M. And the full self-driving package is priced at $7k. So the break-even point is at $400M/$7k ~ 57k vehicles or an attach rate of 57k/400k ~14%.

假设完整的自动驾驶硬件使特斯拉每辆车的成本约为1000美元。 (计算费用为600美元，传感器+布线费用为400美元)。 以每年40万辆汽车计，特斯拉的总成本为4亿美元。 完整的自动驾驶包售价为7,000美元。 因此，收支平衡点为$ 400M / $ 7k〜57k车辆，或者附加率为57k / 400k〜14％。

Per analysts, Tesla easily beats that rate today. And it seems things will only improve: the price of self-driving vehicles will only increase with time. It’s already up to $8k as of July 2020.

根据分析师的观点，特斯拉今天轻松超过了这个比率。 看来事情只会有所改善：自动驾驶汽车的价格只会随着时间的流逝而增加。 截至2020年7月，它已经高达8000美元。

What’s more, as Tesla ships more vehicles, the cost of full self-driving hardware per vehicle will drop, too. With time, as Tesla’s software matures, more Tesla owners will purchase some variant of the full self-driving package from the Tesla app.

更重要的是，随着特斯拉运送更多的车辆，每辆车完全自动驾驶硬件的成本也将下降。 随着时间的流逝，随着特斯拉软件的成熟，更多的特斯拉车主将从特斯拉应用程序中购买某种形式的完全自动驾驶程序包。

易于开发，调试和部署软件 (Easier to develop, debug and deploy software)

Just ask any Software Engineer who had to flash upgrades across different hardware skews: a single architecture streamlines development and testing and makes it easier to roll out more upgrades, more frequently.

只需询问必须在不同硬件缺陷之间进行快速升级的任何软件工程师：单一体系结构即可简化开发和测试，并使更轻松，更频繁地进行更多升级。

Compare this to an automaker with different brands, models, skews each with its own version of custom self-driving hardware and software. Simplicity is a key competitive advantage.

将此与具有不同品牌，型号，偏斜的汽车制造商进行比较，每种偏斜均具有其自己版本的自定义自动驾驶硬件和软件。 简单是关键的竞争优势。

简化运营 (Streamlining operations)

The story continues. Fewer distinct components implies streamlined supply chains that are both resilient and easier to manage. Buying more helps Tesla ride down the cost curve and negotiate better prices with suppliers.

故事还在继续。 更少的独立组件意味着精简的供应链既具有弹性又易于管理。 购买更多商品可以帮助特斯拉顺着成本曲线走下去，并与供应商协商更好的价格。

连续的提高 (Continuous improvement)

There’s no concept of a model year when it comes to Tesla vehicles. Tesla does not, for instance, wait to bundle all the new features and lump it into next year’s Model S.

特斯拉汽车没有模型年的概念。 例如，特斯拉不会等待捆绑所有新功能并将其集成到明年的Model S中。

Instead, Tesla continuously improves its products and processes so that the customer always gets the latest and greatest of what’s possible. And this helps Tesla in building self-driving technology.

取而代之的是，特斯拉不断改进其产品和流程，以使客户始终获得最大可能的最新成果。 这有助于特斯拉构建自动驾驶技术。

Say, for instance, Tesla needs to update the firmware on a certain vehicle part for it to better co-operate in building and/or testing self-driving software. Or perhaps, Tesla wants to update their vehicle’s sensor configuration with better sensors or upgrade the on-board computer. Such changes, once decided, are instantly rolled into production vehicles. This keeps Tesla’s vast fleet agile and its engineers always occupied with the latest and greatest datasets.

例如，说特斯拉需要更新特定车辆部件上的固件，以使其更好地协作以构建和/或测​​试自动驾驶软件。 也许，特斯拉想用更好的传感器来更新其车辆的传感器配置或升级车载计算机。 一旦决定，这些更改将立即转入生产工具。 这使特斯拉庞大的车队保持敏捷，其工程师始终忙于最新和最出色的数据集。

2.特斯拉的车队 (2. Tesla’s fleet)

As of July 2020, Tesla probably has close to a million vehicles on the road in major parts of North America, Europe, and Asia. And every time an owner drives home and sits down to eat dinner, the vehicle is busy sending back mountains of data of all kinds back to Tesla. And Tesla has over time probably mastered the art of sorting through this data pile and making it immensely useful to not just this owner, but its entire fleet.

截至2020年7月，特斯拉在北美，欧洲和亚洲大部分地区的道路上可能有近百万辆汽车。 每次车主开车回家坐下吃晚饭时，车辆都在忙着将各种数据回传给特斯拉。 随着时间的流逝，特斯拉可能已经掌握了对这些数据进行排序的技巧，这不仅对这个拥有者，而且对整个车队都非常有用。

Clearly, there are more positives here than meets the eyes.

显然，这里有更多积极的方面，而不是让人眼前一亮的。

跨车型数据 (Data across vehicle models)

Tesla has a diverse fleet of vehicles, all the way from a sedan to an SUV. And such a fleet helps Tesla capture different perspectives of the world. Because a camera mounted on the Model X, sees the world slightly differently than a corresponding camera on the Model 3.

特斯拉拥有各种各样的车辆，从轿车到SUV。 这样的车队可以帮助特斯拉捕捉世界的不同视角。 因为安装在Model X上的相机对世界的观察与Model 3上对应的相机略有不同。

This is useful for two reasons. First, augmenting data sets with different perspectives makes the neural network more robust. And that implies the network is more likely to accurately detect and classify objects in places and contexts it's not seen before. Second, such a network will probably more easily transfer when used in a new perspective, i.e. the corresponding camera on the Tesla Cybertruck.

这很有用，有两个原因。 首先，以不同的视角扩充数据集使神经网络更加健壮 。 这意味着网络更有可能准确地检测和分类以前未见过的位置和上下文中的对象。 其次，当以新的视角使用这种网络时，即在特斯拉Cyber​​truck上的对应摄像机时，可能会更容易传输。

真实的例子 (Real-world examples)

Self-driving algorithms test and train the best with real-world examples. And Tesla certainly has one of the richest data sets.

自动驾驶算法会通过实际示例来测试和训练最好的算法。 特斯拉当然拥有最丰富的数据集之一。

In 2019, Waymo drove 20 million miles on public roads. Since equipping every vehicle sold with full self-driving hardware, Tesla has sold more than half a million vehicles. Say conservatively, each of them drove ~8k miles in 2019. This accumulates to 500k * 8k = 4 billion real-world miles.

2019年，Waymo在公共道路上行驶了2000万英里 。 自从为每辆出售的汽车配备了完整的自动驾驶硬件以来，特斯拉已经售出了超过50万辆汽车。 保守地说，他们每个人在2019年行驶了约8000英里。这累计达到500k * 8k = 40亿英里。

Let’s say every mile Waymo drove was useful in both developing and testing self-driving algorithms. This implies that to be on par with Waymo, Tesla has to only make 0.5% (20M/4B) of its 4 billion miles useful. And Tesla is adding ~100k vehicles every quarter to this fleet. Which means they accumulate an additional 100k*(8k/4) = 200 million miles of driving data each quarter. The number of vehicles and miles driven, both, are only growing.

假设Waymo行驶的每一英里对开发和测试自动驾驶算法都是有用的。 这意味着，要与Waymo保持一致，特斯拉只需使其40亿英里的可用里程达到0.5％(20M / 4B)。 特斯拉每季度将向该车队增加约10万辆汽车。 这意味着他们每个季度累积了100k *(8k / 4)= 2亿英里的行驶数据。 车辆数量和行驶里程都在不断增加。

The counter-argument, of course, is that such a comparison is a gross oversimplification. After all, it’s hard to quantify the definition for a useful mile.

当然，相反的论点是，这样的比较是过于简单化了。 毕竟，很难量化有用里程的定义。

But what’s important to point out is that there’s an order of magnitude at play here. And my calculations are very conservative. Tesla already has 3 billion miles with AutoPilot.

但是需要指出的是，这里有一个数量级的作用。 而且我的计算非常保守。 特斯拉已经通过AutoPilot实现了30亿英里的飞行。

Besides, there are other noteworthy advantages. By consistently driving more, Tesla is more likely to encounter corner cases and train its software to handle those. As a result, Tesla’s software matures faster.

此外，还有其他值得注意的优势。 通过持续不断地驾驶，特斯拉更有可能遇到极端情况并训练其软件来处理这些情况。 结果，特斯拉的软件成熟速度更快。

Moreover, most of Waymo’s miles come from cities in the US. Whereas drivers have driven Teslas on all kinds of roads and terrains in different countries. So the day Tesla claims it has achieved its goal of full self-driving, it would have realized it on a far larger geographic footprint.

此外，Waymo的大部分里程都来自美国的城市 。 鉴于驾驶员在不同国家的各种道路和地形上驾驶特斯拉。 因此，特斯拉声称自己已经实现了完全自动驾驶的目标的那一天，它将在更大的地域范围内实现它。

Low light presents a huge challenge to perception systems. Things get even more interesting when headlights are the only external source of light. Understanding the sensitivity and tolerance of neural networks to such conditions is important in making progress.

弱光对感知系统提出了巨大挑战。 当大灯是唯一的外部光源时，事情变得更加有趣。 了解神经网络对这种情况的敏感性和耐受性对取得进展很重要。

In the real world, people drive in all kinds of ways that lead to close encounters. Such cases are very useful to capture and solve for. Something you cannot easily get with a smaller fleet or in simulation. Lyft’s L5 self-driving division has a similar strategy. They tap into Lyft’s ride-sharing data to do just that.

在现实世界中，人们以各种方式开车，导致亲密接触。 这样的情况对于捕获和解决非常有用。 使用较小的机队或在仿真中无法轻松获得某些东西。 Lyft的L5自动驾驶部门也有类似的策略 。 他们利用Lyft的乘车共享数据来做到这一点。

阴影模式 (Shadow mode)

Tesla’s fleet is not just a massive data-gathering machine, it’s also the world’s largest testing platform for self-driving software.

特斯拉的车队不仅是一个庞大的数据收集机器，还是世界上最大的自动驾驶软件测试平台。

Whenever Tesla has a new piece of software, they deploy it on their fleet in what’s called the shadow mode. In this mode, the new software will activate and execute, but it won’t actually be allowed to drive the vehicle. Instead, the computer simply notes down what the software would have done. And engineers are able to later access such information to determine if the software is ready to be deployed.

只要Tesla拥有新软件，他们就会以所谓的影子模式将其部署到车队中。 在这种模式下，新软件将被激活并执行，但实际上并不允许其驾驶车辆。 取而代之的是，计算机只是记下了软件的功能。 工程师以后可以访问此类信息，以确定软件是否已准备好进行部署。

This is a huge advantage for engineers at Tesla. To see their code behave in real-world conditions is valuable information. And such a useful diagnostic mechanism helps engineers churn out better code in less time.

对于特斯拉的工程师来说，这是一个巨大的优势。 看到他们的代码在实际条件下的行为是有价值的信息。 这种有用的诊断机制可帮助工程师在更短的时间内生产出更好的代码。

减少运营支出 (Reducing operating expenses)

It costs a lot of time and money, to operate and maintain a fleet of vehicles for collecting data and testing algorithms. To achieve full self-driving capability, we need to repeat this process across different scenarios, including vehicle type; time of day; weather conditions, type of road, traffic pattern, geography, etc.

操作和维护大量的车辆来收集数据和测试算法需要花费大量的时间和金钱。 为了实现完整的自动驾驶功能，我们需要在不同的情况下(包括车辆类型)重复此过程。 一天中的时间； 天气条件，道路类型，交通方式，地理位置等

Tesla owners are literally getting this job done for free. It helps Tesla focus more on analyzing and utilizing the data than worry about gathering it.

特斯拉车主实际上是免费完成这项工作的。 它帮助特斯拉将更多精力放在分析和利用数据上，而不必担心收集数据。

人体驾驶数据 (Human driving data)

There’s plenty for software and algorithms to learn when it comes to driving cars. And we can teach them by showing how it’s done. Tesla has access to tons of sensor recordings (input to the algorithm) and the corresponding human driving commands (expected output from the algorithm) of braking, acceleration, and steering, over billions of miles. A fairly powerful supercomputer could probably train an AI to learn how to drive just by watching these recordings.

在驾驶汽车方面，有很多可供学习的软件和算法。 我们可以通过展示如何完成来教他们。 特斯拉可以访问数十亿英里的大量传感器记录(算法的输入)和相应的人类驾驶命令(算法的预期输出)，包括制动，加速和转向。 一台功能强大的超级计算机可能可以训练AI通过观看这些录音来学习如何驾驶。

COVID证明？ (COVID proof?)

It’s 2020 and we now live in a drastically different world. Every self-driving company has had to alter its fleet operations.

到了2020年，我们现在生活在一个截然不同的世界中。 每家自动驾驶公司都必须改变其车队运营。

The responses range the gamut — 1) temporary pause; 2) re-purpose fleet; 3) resume safe operations. Tesla no doubt will be impacted. It’s safe to assume most Tesla owners are working from home and driving a lot less. But based on Musk’s tweet, it seems like Tesla is recovering fast.

响应范围为：1)临时暂停 ； 2) 重新使用舰队； 3) 恢复安全运行。 特斯拉无疑会受到影响。 可以肯定的是，大多数特斯拉车主都是在家工作，开车的次数要少得多。 但是根据马斯克的推文，特斯拉似乎正在快速恢复。

Time will tell if this is just a temporary recovery. But even if you cut usage by 50%, it’s still billions of real-world miles.

时间会证明这是否只是暂时的恢复。 但是，即使您将使用量减少了50％，仍然是数十亿英里的实际里程。

3.全自动驾驶 (3. Full self-driving)

Tesla is focused on the future — Full self-driving.

特斯拉专注于未来-全自动驾驶。

Not partial. Not assisted. Not conditional. Not constrained. Not compromised.

不部分。 没有帮助。 没有条件的。 不受限制。 不妥协。

This is an important distinction. One that often gets them in trouble. As their cars today very much need human input at all times. While the long term payoff is huge, Tesla has clearly chosen the most difficult path here. It’s like saying we’ll reuse rockets by vertically landing them on tiny barges floating in the middle of the ocean. Ok, maybe not that hard!

这是一个重要的区别。 一个经常使他们陷入困境的人 。 由于今天的汽车非常需要任何时间的人工输入。 尽管长期收益巨大，但特斯拉显然已经在这里选择了最困难的道路。 这就像在说我们将通过将火箭垂直降落在漂浮在海洋中间的小驳船上来重复使用火箭。 好吧，也许没有那么难！

But having clearly defined this end goal is advantageous.

但是明确定义此最终目标是有利的。

每个自动驾驶应用 (Every self-driving application)

Tesla is set up to compete in every possible self-driving application out there, as opposed to having a narrow focus, which may only help excel in one specific application of self-driving technology.

特斯拉(Tesla)的设立是为了在市场上所有可能的自动驾驶应用程序中竞争，而不是关注范围狭窄，这可能只会有助于在自动驾驶技术的一种特定应用中脱颖而出。

Tesla is poised to benefit from self-driving technology in many ways.

特斯拉有望以多种方式受益于自动驾驶技术。

• Logistics: Tesla has more than 2000 orders for its semi-trucks.

  物流：特斯拉的半卡车有2000多个订单。

• Robotaxis: Tesla has already made public their plans for launching a robotaxi network.

  Robotaxis：特斯拉已经公开了启动Robotaxi网络的计划。

• Last-mile delivery: Tesla van or maybe a compact car?

  最后一英里交付： 特斯拉货车还是紧凑型汽车 ？

• Consumer vehicles: Models S, 3, X, Y, Cybertruck.

  消费车辆： S，3，X，Y，Cyber​​truck车型。

• And whatever this is — link.

  不管这是什么-link 。

解决长尾巴 (Addressing the long tail)

The 80/20 rule aptly describes the self-driving problem. Computers have to be trained for numerous corner cases and rare events to be able to drive safely. By opting to work on the full self-driving problem, Tesla has got an early start to knocking down this long tail. For instance, a task as simple as detecting a stop sign, gets increasingly difficult when you account for all the ways it can present itself — occluded, in heavy rain/snow, on a digital display, manually held, on a toll both, on a school bus, in different languages, etc. And Tesla’s strategy allows it to create the largest dataset of stop signs and train its networks.

80/20规则恰当地描述了自动驾驶问题。 必须对计算机进行各种极端情况和罕见事件的培训，才能安全驾驶。 通过选择解决完全的自动驾驶问题，特斯拉早早地摆脱了这条长尾巴。 例如，当您考虑到停车标志的所有显示方式时，一项简单的任务(如检测停车标志)将变得越来越困难-在大雨/雪中，在数字显示屏上，手动操作，在通行费上，通行费不变，特斯拉的策略允许它创建最大的停车标志数据集并训练其网络。

4.纵向整合 (4. Vertical Integration)

Musk’s vision and scrappiness bring in clear-as-glass clarity, ruthless prioritization, and humbleness to accept mistakes and correct them fast. Tesla’s journey to building self-driving systems is a great example of seeing this play out. Over time, a slew of decisions have helped bring a lot of development in house.

马斯克的视野和草率带来了如玻璃般清晰的清晰度，无情的优先权以及谦虚地接受错误并快速纠正错误的能力。 特斯拉(Tesla)打造自动驾驶系统的旅程就是见证这一过程的一个很好的例子。 随着时间的流逝，一系列决定帮助公司实现了许多发展。

内部AI (AI in-house)

Back in 2017, Tesla decided to build its own AI chips, further integrating hardware and software development in house. Tesla realized that software is no longer structured lines of code. Rather it’s all about gathering and managing datasets and tuning recipes to fit the silicon constraints. Tesla has been on this path for a while now.

早在2017年 ，特斯拉就决定构建自己的AI芯片，进一步整合内部的硬件和软件开发。 特斯拉意识到软件不再是结构化的代码行。 而是所有关于收集和管理数据集以及调整配方以适应芯片限制的问题。 特斯拉已经走了一段时间。

跨职能协作 (Cross-functional collaboration)

Owning the factory, manufacturing processes, and overall product design has its benefits. I imagine software, electrical, and mechanical engineers seamlessly co-ordinating between Palo Alto and Fremont to build self-driving systems for operating in the real world.

拥有工厂，制造流程和整体产品设计都有其好处。 我想象软件，电气和机械工程师会在Palo Alto和Fremont之间进行无缝协调 ，以构建在现实世界中运行的自动驾驶系统。

This means that at a fundamental level, the self-driving software, vehicle operating system, battery management software and manufacturing processes are in sync.

这意味着从根本上讲，自动驾驶软件，车辆操作系统，电池管理软件和制造过程是同步的。

充电基础设施 (Charging infrastructure)

A truly self-driving car should be conscious of the state of its battery at all times, plan trips accounting for charging stops, park itself and not expect a human to plug or unplug it from an EV charger.

真正的自动驾驶汽车应始终意识到电池的状态，计划出行以计入充电停止，自行停车，不要指望有人将其从EV充电器上拔下。

Owning the charging infrastructure gives Tesla this advantage.

拥有充电基础设施为特斯拉提供了这一优势。

特斯拉应用 (The Tesla app)

To truly own the end-to-end self-driving stack, you’ve got to own the user interface.

要真正拥有端到端自动驾驶堆栈，您必须拥有用户界面。

The Tesla App, which already does quite a few things, is the perfect fit. In the future, I won’t be surprised if this app provides a seamless interface to rent out your Tesla, trade energy with the grid, buy insurance, and have a “surprise me” feature to take you autonomously on a weekend road trip.

特斯拉应用程序已经完成了很多事情 ，非常适合。 将来，如果该应用程序提供无缝界面以出租特斯拉，与电网进行能源交易，购买保险并具有“让我感到惊讶”的功能，以使您在周末的旅途中自动带给我，我不会感到惊讶。

5.中国市场 (5. The Chinese market)

China is the most important automotive market and by extension, very important for self-driving technology. It’s also a very different market when it comes to the pace of innovation, social acceptance of technology, and the regulatory climate.

中国是最重要的汽车市场，因此对自动驾驶技术非常重要。 在创新步伐，技术对社会的接受以及监管环境方面，这也是一个截然不同的市场。

To succeed, every automaker needs to have a China strategy. The usual norm is to partner with local automotive and tech companies in exchange for rights to build plants, sell vehicles, and develop and test self-driving technology. All that is easier said than done.

为了取得成功，每个汽车制造商都需要制定中国战略。 通常的做法是与当地的汽车和科技公司合作，以换取建造工厂，销售车辆以及开发和测试自动驾驶技术的权利。 说起来容易做起来难。

But Tesla has managed to pull off the unthinkable–ramping up a factory in Shanghai in less than a year. This implies Tesla now gets to rinse and repeat its self-driving strategy in the world’s largest electric vehicle market. It gets to build a fleet, collect data, tune its models, and continuously deploy and test.

但是特斯拉设法摆脱了无法想象的局面，在不到一年的时间内就在上海建立了工厂 。 这意味着特斯拉现在可以在全球最大的电动汽车市场上冲洗并重复其自动驾驶策略。 它可以组建一支车队，收集数据，调整其模型，并持续进行部署和测试。

最后的想法 (Final thoughts)

Self-driving is an AI problem. AI problems are solved with data. Data demands infrastructure at scale, both on the road and in the cloud. And Tesla has been patiently putting all the right pieces in place.

自动驾驶是一个AI问题。 AI问题可以通过数据解决。 数据需要大规模的基础设施，无论是在道路上还是在云中。 特斯拉一直在耐心地将所有正确的部件放置到位。