双语科普故事 | 自动驾驶汽车摆脱炒作的阴影(下)

automation

The ADS need to perceive the surrounding environment and establish where they are located using technology based on multiple basic physical principles to provide safe operations. They need to deal with adverse conditions brought about by electromagnetic interference from electrical storms or nearby electrical equipment, low sun angles that can blind cameras, precipitation or smoke that diffuses light needed by imaging sensors and cyberattacks that target any of the vehicle's sensor technologies. Also needed is information about nearby infrastructure and the relative velocity of other moving objects in the vicinity. Data from all these sensors must be fused to accurately represent the area surrounding the vehicle and to isolate any faulty inputs.

自动驾驶系统需要利用基于多种基本物理原理的技术来感知周围环境并确定自身位置，以确保安全运行。它们需要应对由电风暴或附近电气设备产生的电磁干扰、低太阳角度可能导致摄像头致盲、降水或烟雾扩散成像传感器所需的光线以及针对车辆任何传感器技术的网络攻击等不利条件。此外，还需要获取附近基础设施的信息以及附近其他移动物体的相对速度。所有这些传感器的数据必须进行融合，以准确呈现车辆周围的区域，并隔离任何错误的输入。

The sensor requirements and the data processing and storage associated with this level of environment sensing will make ADS technology expensive for the foreseeable future. These expenses are unavoidable if the technology is to replace the full range of a driver’s skills without compromising safety. Such expenses also drive the business case for initial ADS deployment on commercial vehicle fleets that can be used throughout the day to generate revenue rather than operating only one or two hours a day like most private personally owned vehicles.

在可预见的未来，与环境感知这一层级相关的传感器需求以及数据处理和存储将使得自动驾驶技术的成本居高不下。如果要在不牺牲安全性的前提下取代驾驶员的全部技能，这些开支是不可避免的。此类支出也推动了在商用车队中初步部署自动驾驶系统的商业案例，且商用车队可以全天候用于创收，而不是像大多数私人拥有的车辆那样每天仅运行一两个小时。

Commercial fleets provide further advantages while the automation technology is still being refined. It will be easier to properly maintain sensitive components on a fleet, and fleet operation lends itself to remote support for vehicles if needed. Remote human support for automated driving is an important topic that has not received the level of public attention that it deserves. Virtually every developer of ADS without an onboard human driver expects to rely on humans at a fleet management center to help the system make tactical driving decisions when needed.

商用车队在自动化技术仍在完善的过程中提供了额外的优势。车队能够更容易地妥善维护敏感部件，且在需要时，车队运营也更便于为车辆提供远程支持。远程人工支持自动驾驶是一个重要的议题，但却没有得到应有的公众关注度。实际上，几乎所有不配备车载驾驶员的自动驾驶系统的开发者都预期将依靠位于车队管理中心的人员，在必要时协助系统做出战术性的驾驶决策。

The human assistant may compensate for hardware and software faults on the vehicles but will primarily provide advice in challenging traffic situations such as navigating around obstacles that temporarily block the intended route of the vehicle, determining when it may be necessary to violate a traffic rule to deal with a specific odd situation, recognizing gestures of police officers directing traffic and identifying objects in the vehicle’s path. Some developers are also considering remote driving capabilities, in which a human at a fleet management center would drive the vehicle for some portion of a trip that cannot be handled by the ADS. These remote support functions will be particularly important during the early years of ADS deployment as the technology is being refined.

人类助手可以弥补车辆硬件和软件故障，但主要是在复杂的交通情况下提供建议，例如在临时障碍物阻塞预定路线时导航， 判断何时可能需要违反交通规则以应对特定的异常情况，识别警察指挥交通的手势以及识别车辆行进路线上的物体。一些开发者还在考虑远程驾驶功能，即车队管理中心的人员可以在自动驾驶系统无法处理的部分行程中驾驶车辆。在自动驾驶系统部署的早期阶段，随着这项技术的日益完善，这些远程支持功能将显得尤为重要。

They will also provide employment for many of the drivers whose jobs will gradually be replaced by ADS. The working conditions for remote driving are likely to be more attractive than conventional long-haul truck driving, allowing the drivers to work close to home, where they can avoid the physical discomforts and safety risks associated with conventional truck driving. Although the most popular projected use for automated driving a few years ago was automated ride-hailing passenger services, interest has shifted significantly toward the automated movement of goods—automated driving of heavy trucks on long-haul interstate routes and small low-speed vehicles for local urban and suburban package delivery.

这些技术的发展还将为许多逐渐被自动驾驶系统取代的司机提供就业机会。与传统的长途卡车驾驶相比，远程驾驶的工作条件可能更具吸引力。驾驶员可以就近工作，从而避免传统卡车驾驶带来的身体不适和安全风险。尽管几年前，自动驾驶最受欢迎的预期用途是自动叫车客运服务，但现在人们的兴趣已经明显转向货物运输的自动化——即在长途州际路线上重型卡车的自动驾驶以及在本地城市和郊区进行包裹递送的低速小型车辆的自动驾驶。

This shift in emphasis began prior to the pandemic but accelerated during the COVID crisis. Ride-hailing services have the most favourable demand profile, short trip lengths and shared rides in dense urban areas. Those are the places that are the most technologically challenging environments for ADS. In contrast, long-haul trucking operations on interstate highways face simpler traffic and roadway infrastructure conditions, particularly if the origin and destination points for the automated trips are directly connected to limited-access highways. Urban and suburban package delivery can use vehicles that are too small for human occupants and can operate on sidewalks or in bicycle lanes at low speeds.

这种侧重点的转变始于疫情之前，但新冠疫情危机期间加速了这一过程。叫车服务具有最有利的需求特征， 即短途行程且在人口密集的城市区域内共享乘车。而这些地区是自动驾驶技术面临最具挑战性的环境。相比之下，州际高速公路上的长途卡车运输作业面临的交通和道路基础设施条件更简单，尤其是在自动驾驶行程的起点和终点直接与限制进入的高速公路相连时。城市和郊区的包裹投递可以使用小到无法容纳人类乘客的车辆，且可以在人行道或自行车道上低速行驶。

Freight vehicles have the additional advantage that they can be designed to drive extra cautiously to avoid conflicts with other road users, without having to worry about the impatience of passengers or the need to protect their own occupants from injury. Not every ADS company has adopted this approach. Tesla is a conspicuous outlier relative to the rest of the industry on ADS deployment. It has pursued the traditional automotive industry model of selling privately owned personal vehicles to serve all uses and has attempted to advance its ADS capabilities by building on its current driving assistance system rather than designing a system with the requisite higher levels of redundancy and fault management.

货运车辆具有额外的优势，那就是它们可以被设计得更加谨慎地驾驶以避免与其他道路使用者发生冲突，而不必担心乘客的不耐烦或需要保护其乘员免受伤害。但并非每家自动驾驶系统公司都采用了这一方法。 在自动驾驶系统部署方面，特斯拉相对于业内其他公司而言是一个明显的例外。它一直遵循传统的汽车行业模式，即销售私家车以满足各种用途，并试图通过在其当前的驾驶辅助系统的基础上进行改进来提高其自动驾驶系统的能力，而不是设计一个具备必要的更高冗余度和故障管理能力的系统。

The company has recently focused its approach to rely entirely on machine vision, avoiding complementary sensors or precision mapping and localization that provide critical information about a vehicle's surroundings. The Tesla approach reduces the cost of production but appears to preclude achieving safe and reliable performance for confronting inevitable variations in traffic and environmental conditions. The company's use of the names “Autopilot” and “Full Self-Driving” for its current products has produced serious public confusion about the capabilities of Tesla's products.

该公司最近将重点放在了完全依赖机器视觉的方法上，避免使用辅助传感器或精确制图和定位技术来提供有关车辆周围环境的关键信息。特斯拉的这种做法降低了生产成本，但在面对不可避免的交通和环境条件变化时，似乎无法实现安全可靠的性能。该公司为其现有产品冠以“自动驾驶”和“全自动驾驶”之名，导致公众对特斯拉产品功能的严重混淆。