在绝大多数人眼里，高精度地图是高级自动驾驶的核心使能技术之一。开发这类地图是一项浩大的工程，需要在资金和人才方面进行巨大投资。

在传统地图中，盘山公路上的黄色标志表示前方有蛇形弯道，这很难忠实地反映详细道路状况，但却可以向驾驶员传递一个简单的信号：准备应对弯道。

几个世纪以来，地图最本质的作用从未改变：指导人类从 A 地到达 B 地。不难想象，有时，过于详尽的地图也会碍事。加州初创公司 Deepmaps 首席运营官罗伟（音）表示，“你不需要过多细节，细节过多反而可能让人迷惑。”罗先生此前曾在谷歌地图（Google Maps）担任产品经理。

然而，在为用户提供信息的同时，地图公司也同时指望用户能够帮自己补充许多缺失的部分，并时刻对变化做出反应。毕竟，地图的用户是人类。地图和语言一样，是连接人类思维的桥梁符号。

自动驾驶时代的地图绘制

然而，这种情况正在改变。作为地图学中的最新领域，专为自动驾驶汽车绘制的地图面对的则是一类完全不同的用户：软件程序。与人类驾驶员不同，导航系统需要的是细节，每一条曲线、每一个凸起的路沿、每一条车道，每个细节都至关重要，而且必须达到厘米级精度。除此之外，更具挑战性的是，自动导航系统必须有能力判断各种未知情况，并作出相应改变。比如，假如有棵树倒在路上，导航系统则该如何继续提供指引？对于人类驾驶员，这根本不是问题，不过是抱怨几句，然后换条路而已。但大多数软件在面临类似的情况时都需要非常详细的指导，否则根本不知道作何反应。

如今，新型地图已经成为自动驾驶行业中的基础技术之一，地图行业也由此开始崛起。毕竟，车辆最本质的功能是将人或货物运送至目的地，而地图可以帮自动驾驶车辆确定自身位置以及如何到达目的地，与现实世界建立联系。

新型地图需要对城市中的每一条小街小道都进行精确的三维记录，这本身没有什么难度。但更重要的是，我们还需要人工智能（AI）技术，协助车辆判断行驶路线上可能出现的各种情况，并作出适当的反应，而且通常需要在几分之一秒内完成。

如今，在自动驾驶汽车发展如火如荼之时，新一代地图的绘制已然成为了一项庞大事业。其中的参与者不仅包括地图行业巨头谷歌 Waymo，还有很多初创公司，比如拿到风投的美国公司 DeepMind 和 Carmera，以及由戴姆勒（Daimler）、大众（Volkswagen）及其他多家汽车制造商投资的欧洲领先地图供应商 HereTechnologies。最终，在竞争中胜出的公司将有机会运行一个全球地理平台，追踪并指导地球上大部分车辆的行驶。卡内基梅隆大学机器人系教授 John Dolan 表示，“这是一个非常热门的研究领域。”

应对变化

对于针对自动驾驶汽车的地图绘制而言，最大的挑战在于如何应对变化。“事实上，（自动驾驶地图）必须是 4D 的，”Deepmap 公司的罗伟Wei Luo 表示，“也就是传统三维外加时间维度。”为了在地图中融入时间维度，所有系统都必须通过某种方法来收集最新数据，而且必须保证这些数据的实效性和可靠性。Waymo 等公司选择利用自己自动驾驶车队上的传感器，其他公司则倾向于采用“众包”的思路，也就是利用其他车辆上装载的激光雷达和各种传感器。

一旦传感器就位，并开始传回报告流，数据收集部分的工作就很简单了。“你可以从一张信息丰富的基础地图开始，”纽约初创公司 Carmera 创始人兼首席执行官 Ro Gupta 表示，“这并不简单，但从某种程度上来说已经不是问题了。”

事实上，真正构成巨大挑战的正是大量数据本身。罗先生表示，每辆自动驾驶汽车每小时大约可以产生 1 PB 的导航数据，这非常庞大，相当于 2 的 50 次方字节。软件必须对这些海量数据进行筛选，并从中找出有意义的片段，然后“决定”是否采取行动及采取何种行动。这将带来非常庞大的认知工作，需要人工智能技术的深入参与。

在最初阶段，单单识别变化就已经是一个挑战了。随着海量数据的不断涌入，基础地图将持续确认各种信息匹配无误。停车标志？没问题。左转车道？也没问题。

然而，世界常会有新的变化，比如街角处的一棵松树没有了，出现了一片空地。系统可以发现这些变化，但这个变化是否比落叶或水洼的出现更重要？人类驾驶员想都不用想，就会立刻认出某片空地上停了一辆卡车。但软件系统却缺乏人类的这种经验和直觉，因此必须通过更多线索才能进行判断。观察结果有更多的数据支持吗？类似大树这样的目标曾有多少次消失不见呢？这种情况是否会造成任何事故或其他麻烦？会影响交通的通畅吗？

在应对变化时，时间至关重要。一种符合逻辑的做法是通过对传感器车辆进行编程，使其仅在检测到与基础地图出现不符的情况时才进行报告，从而大幅减少数据通信量及相关延迟。如果 Broad Street 大街上的三条车道一切如故，那又何必再报告一些没用的信息，给系统增加“噪声”呢？Carmera 的 Gupta 表示，不过，问题在于我们可能会忽视掉一些未被察觉的变化。他说，“因而可能会丢失一些假阴性指标。”

是否连“云”？

此外，新型地图的更新还牵扯各种各样的数据管理问题。例如，哪些地图数据应交由车辆自己解读，又有哪些应该上传至基于云的人工智能系统进行判断？

从一方面，云可以同时从多个来源接收信息，将其与历史模式进行匹配，并提供更多的智能功能。然而，尽管超高速 5G 蜂窝网络预计将在三年内得到普及，但数据的传输依然无法避免延迟问题。更重要的是，由于网络连接很难得到 100% 的保证，因此自动驾驶汽车也必须配备车载系统，从而具备在不联网的情况下对变化进行判断，并做出适当反应的能力。

在早期阶段，大多数地图公司都会选择将部分区域当作样本，进行新型地图开发。很自然，很多公司都把精力集中在正在进行自动驾驶测试或已经开始提供相关服务的区域。比如，Waymo 和 Deepmap 均在亚利桑那州和加利福尼亚州的部分地区投入了很大精力。Carmera 则已经与一些货运公司签订了合作协议，目前正在与纽约、旧金山和佛罗里达州的老年村进行地图建模，而这些地区都是其合作伙伴正在提供自动驾驶穿梭巴士服务的区域。Here Technologies 公司则是一个例外，这家公司凭借与多家主流欧洲汽车制造商的关系，可以通过这些制造商出售的数十万辆汽车上的传感器，收集欧洲和北美地区的匿名数据。

现阶段的营利也很重要

对于一些获得风投的创业公司而言，业务发展的时机也非常重要。尽管这些公司现在已经开始大量砸钱，但全自动驾驶汽车（也就是 SAE 4 级和 SAE 5 级自动驾驶汽车）的广泛普及可能要到十几年以后，甚至更久。因此，这些创业公司也在为他们的下一代地图寻找过渡期的市场。Here Technologies 产品营销经理 Mattew Preyss 提问到，“在过渡期中，我们该如何利用这些数据来帮助驾驶员？”

Preyss 表示，下一代地图将为Waze、谷歌地图及 TomTom 等当下主流导航服务提供有力补充，时时为驾驶员提供最新路况和路线修正信息。更重要的是，这些地图还可以提供如增强现实或寻找车位等一系列全新服务，以音频和画面的形式为驾驶员提供详细的路线信息。与以往一样，只要同时牵涉人类驾驶员和地图，我们就必须面临一个永恒挑战——如何让地图为驾驶员提供更多有用信息，但同时剔除可能分散驾驶员注意力的细节。

现阶段，让人类驾驶员继续参与新型地图绘制还有一个重要作用——地图本身可以学习人类驾驶员是如何对数据做出反应的，进而将更多人工智能处理能力分配在行车路线的中需要车辆立即做出反应的重大变化上。在未来十年中，我们人类驾驶员也将“教导”的导航系统，使其真正做好取代我们的准备。

Most believe ultrahigh-definition mapping is crucial to make high-level automated driving possible. Developing these maps is a huge undertaking — one that’s enjoying a massive investment of money and talent.

A yellow sign on a mountain highway shows an S-shaped curve. This is a primitive map, and hardly a faithful representation of the road. Instead it delivers a simple signal to the driver: Get ready for turns.

Road cartography has evolved over centuries with a unifying purpose: to guide human beings from point A to point B. Complexity often gets in the way. “You don’t want too much detail,” says Wei Luo, formerly a product manager at Google Maps and now chief operating officer at Deepmaps, a Palo Alto, California-based startup. “That can confuse people.”

At the same time, though, the cartographer counts on the map’s user to fill in many of the missing pieces—and respond to changes. After all, the user is a fellow human being. Maps, like language, are symbols that bridge human minds.

New-age cartography for autonomy 

But this is changing. The newest field of cartography—creating maps for autonomous vehicles—is designed for a different user: a software program. Unlike a person, the navigation program demands specifics—every squiggle, every raised curb, every passing lane, all of them calibrated by the centimeter. At the same time, and far more challenging, automated navigation must adapt to immediate unknowns. How should it provide guidance to the destination if a fallen tree lies in its path? While a human driver might swear under her breath and improvise, most software programs will require detailed guidance.

An entire industry is rising up to create this new breed of map, a fundamental technology for the nascent autonomous industry. After all, the purpose of the vehicle is to reach a destination. The map tells whe it is and how to get there, the AV’s connection to the physical world.

Creating these maps requires precise three-dimensional recording of every street and byway—itself no mean feat. But it also requires muscular layers of artificial intelligence (AI) to interpret what it encounters along the way and then to respond appropriately. Often within a fraction of a second.

It’s a massive undertaking that feeds this growing field of research. Google’s Waymo, the industry’s AI behemoth, is developing maps for its autonomous fleets. It’s joined by a host of start-ups, including venture-funded DeepMind and Carmera in the U.S. and European-led Here Technologies, which is backed by Daimler, Volkswagen and other automakers. The winners in this market will be positioned to run the world's geo-platforms, tracking and guiding much of the movement on our planet. “It’s a very hot field for research,” says John Dolan, a robotics professor at Carnegie Mellon University.

Dealing with change

A central challenge for autonomy-centric mapping is adapting to change. “The system actually has to be 4D, says Deepmap’s Luo. “That’s 3D plus time.” To incorporate time into the map, each system must devise a method for harvesting reliable, up-to-the-minute data. Some, like Waymo, use the sensors on their own fleets of AVs. Others look to crowdsourced data or piggyback on the onboard LIDAR and other sensors.

Once the sensors are in place and sending back streams of reports, the data-gathering part of job is straightforward. “You start with a very rich base map,” says Ro Gupta, founder and CEO of the New York start-up, Carmera. “That’s not trivial,” he says, “but it’s somewhat a solved problem.”

It’s the flood of data itself that creates immense challenges. Each AV, says Luo, generates about one petabyte per hour of navigational data. Software must sift through this avalanche of data to find the fragments that are meaningful and then “decide” whether take action. This is an enormous cognitive enterprise—and requires strong doses of AI.

The initial challenge is simply to spot a change. As the data pours in, the base map is certifying that everything is matching. Stop sign? Check. Left-turn lane? Check.

Then it encounters something new: A white space at a street corner where there used to be a pine tree. The system notes a change. But is it more significant than other changes, like falling leaves or fresh puddles? A human being might immediately recognize the white space as a parked truck, and not give it a second thought. The software, however, lacking human experience and intuition, must probe for clues. Is there more data to corroborate the observation? How many times have objects, like a tree, gone missing before? Is there any correlation in such cases to accidents or other troubles? Is traffic continuing unimpeded?

In responding to changes, time is of the essence. One logical approach would be to reduce data flows and associated latency by programming the sensor vehicles to report only when they detect changes from the base map. If the traffic is flowing on the usual three lanes on Broad Street, why add to system “noise” by reporting it? The trouble, though, says Carmera’s Gupta, is that unperceived changes will be missed. “You lose the false negatives,” he says.

Cloud or no cloud? 

Updating this new variety of map raises all manner of issues regarding data management. How much of the geo-data, for example, should the vehicle itself interpret and what proportion should be uploaded to cloud-based AI systems?

On one hand, the cloud can harvest from multiple sources, match them with historical patterns, and provide expanded intelligence. But even with ultra-speedy 5G cellular networks expected to be widespread within three years, the back-and-forth of data transfer raises latency concerns. What’s more, since network connections are never guaranteed, autonomous vehicles must be equipped to interpret deviations from the base map for themselves and respond appropriately.

In these early days, most of the mapping companies are focusing on small samples of the earth’s roadways. Naturally, many concentrate on the areas where autonomous driving tests and services are underway. Waymo and Deepmap, for example, are busy in parts of Arizona and California. Carmera, which has agreements with companies that operate delivery fleets, is modeling New York City, San Francisco and retirement villages in Florida, where its partner, Voyage, is operating autonomous shuttle services. The exception is Here Technologies, which is harvesting anonymized data throughout much of Europe and North America from sensors on hundreds of thousands of vehicles manufactured by European automakers.

Monetization matters

One problem, particularly for the venture-backed startups, involves timing. While they’re making large investments now, the widespread use of fully-automated vehicles (SAE Level 4 and 5) may be a decade away, or perhaps longer. In the meantime, they’re searching for intermediate markets for their next-generation maps. “With this transition taking place, how can we use this data to help the driver [now]?” asks Matthew Preyss, a product marketing manager at Here Technologies.

Preyss suggests the new maps will enhance current navigation services, like Waze, Google Maps and TomTom, with more up-to-date road status and course corrections. But the maps could also feed new services, such as augmented reality and parking availability, providing detailed information on the route in both audio and video. The challenge, as always when it comes to maps and human beings, will be to provide helpful data while culling distracting detail.

However, keeping humans in the loop during this period of development also has an advantage: the maps themselves can learn from the drivers’ responses to the data—and focus the AI on significant changes along the route—the ones that demand a response. In this way, we human drivers, over the next decade, will be “educating” the navigation engines poised to replace us.

Author: Stephen Baker

Source: Autonomous Vehicle Engineering