双语科普故事 | 自动驾驶汽车摆脱炒作的阴影(上)

automation

Five years ago marked a peak for one of the predictable cycles of hyperbole for “self-driving” cars. At the time, virtually every major motor vehicle manufacturer and high-tech company predicted widespread deployment of automated driving systems (ADS) by 2020, which would purportedly lead to the rapid obsolescence of conventional human driving.

五年前，“自动驾驶”汽车领域迎来了一个可预见的炒作周期的高峰。当时，几乎所有主要汽车制造商和高科技公司都预测，到2020年，自动驾驶系统将得到广泛部署，据称这将导致传统的人工驾驶方式迅速过时。

By 2018, the CEOs of the major companies that had invested most heavily in ADS (Waymo, General Motors, Ford, Aurora) were starting to make public statements tempering their earlier optimism by pointing out that the rollout of automated driving would be incremental, beginning with operations under constrained conditions in tightly restricted locations. At the pace they are now going, it will require decades to expand to anything approaching nationwide deployment. The organizational learning curve and costs have been much longer and higher than expected.

截至2018年，在自动驾驶系统领域投入巨资的主要公司（如Waymo、通用汽车、福特、Aurora）的首席执行官们开始公开发表声明，调整了他们早期的乐观态度，指出自动驾驶技术的推广将会是循序渐进的，首先只能在严格限定的地点和条件下运行。按照他们目前的速度，要想将自动驾驶技术扩展到接近全国范围部署的规模，还需要数十年的时间。而组织的学习曲线和成本也都比预期的要漫长和高昂得多。

After investing at least a decade and billions of dollars in ADS development, the companies have learned that the technical requirements to support the widespread use of the technology are far more complicated than they had originally envisioned. At the same time, companies such as Tesla and less mature start-ups that continue to plug faster and wider-scale deployments are those that are still working their way up a learning curve and have not yet realized how far they are from their goal.

在自动驾驶系统研发上投入至少十年时间和数十亿美元后，这些公司意识到，支持该技术广泛应用的技术要求远比他们最初设想的要复杂得多。与此同时，像特斯拉以及那些成熟度较低的初创企业依旧在继续推动更快、更大规模部署，它们仍处于学习曲线的上升阶段，尚未意识到距离目标还有多远。

Consensus is growing among knowledgeable ADS developers about several key aspects of the technology and its near-term deployment: Automated operations will only be feasible during the coming years within narrowly defined conditions that include benign weather, lighting, traffic and electronically geofenced locations that have been mapped with high precision (and, in many cases, equipped with suitable physical and digital infrastructure support features). Because ADS need to avoid all the traffic hazards they encounter without human driver intervention, the systems have to incorporate much higher levels of safety assurance than driving-assistance systems that depend on human supervision.

在有见识的自动驾驶系统开发人员中，关于该技术及其近期部署的几个关键方面的共识正在增加：在未来几年内，只有在严格限定的条件下，自动化操作才具有可行性。这些条件包括宜人的天气、良好的照明、适中的交通状况，以及经过高精度测绘并设置了电子围栏的区域 （在许多情况下，这些地点还配备了合适的实体和数字基础设施支持功能）。由于自动驾驶系统需在没有人类驾驶员干预的情况下避免所有遇到的交通隐患，因此与依赖人类监督的驾驶辅助系统相比，该系统必须具备更高级别的安全保障。

ADS need to rely on multiple independent sources of information about the driving environment and its hazards. These data come from cameras, lidars (light detection and ranging systems), radars and precise positioning, combined with highly detailed digital maps. Although many ADS developers claim the ability to drive without relying on wireless communications from other vehicles—or with alerts from vulnerable road users and actual roadside infrastructure itself—recent research has shown that widespread use of ADS without such cooperative communication is likely to have adverse effects on traffic flow, energy use and environmental emissions because of an inability to anticipate future changes in road conditions.

自动驾驶系统需要依赖多种独立信息源以获取驾驶环境及其潜在危险的数据。这些数据来自于摄像头、激光雷达（光探测和测距系统）、雷达以及精确定位技术，并辅以高精度数字地图。尽管许多自动驾驶系统开发人员声称他们的系统能够在不依赖来自其他车辆的无线通信——或不受易受伤害的道路使用者和实际路边基础设施的影响的情况下进行驾驶——但最近的研究表明，如果广泛使用没有此类协作通信的自动驾驶系统很可能对交通流量、能源消耗和环境排放产生负面影响，原因在于无法预见道路状况的未来变化。

The technology will initially be implemented for specialized uses such as local package delivery, long-haul trucking on motorways, urban transit services on fixed routes and, in more limited locations, for urban and suburban automated passenger ride-hailing. Even when ADS can drive vehicles without an onboard human driver as a backup, they will still need remote support from humans who are skilled drivers to manage “corner case” conditions that the automation cannot handle.

该技术最初将用于特定用途，如本地包裹递送、高速公路长途卡车运输、固定路线的城市公交服务，以及在更为有限的地点，用于城市和郊区的自动叫车服务。即使自动驾驶系统能够在没有车载人类驾驶员作为备用的情况下驾驶车辆，它们仍将需要熟练的人类驾驶员提供远程支持，以处理自动驾驶无法应对的 "极端情况"。

Such conclusions derive from a few basic lessons learned by developers of the technology. Foremost among them is the reality that the inherent complexity of the driving task makes it difficult for automated systems to accurately perceive the driving environment, anticipate the actions of other road users and recognize and respond to traffic hazards. Like children learning to control their own movements, ADS need to learn to crawl before they can walk and to walk before they can run. This is why they first have to be implemented and perfected in simple environments before tackling complicated interactions with unpredictable road users (including pedestrians and cyclists) or operations in adverse weather conditions (such as heavy rain, snow, fog and icy roads).

这些结论源于技术开发人员总结的一些基本经验。其中最重要的是，驾驶任务固有的复杂性使得自动化系统难以准确感知驾驶环境，预测其他道路使用者的行为，以及识别和应对交通危险。就像孩子学会控制自己的动作一样，自动驾驶系统需要先学会爬行，然后才能行走，再从行走过渡到奔跑。这就是为什么它们首先必须在简单环境中实施并完善，然后才能处理与不可预测的道路使用者（包括行人和骑自行车者）的复杂互动，或者在恶劣天气条件（如大雨、雪、雾和结冰道路）下运行。