近期，一份來自于IIHS（美國高速公路安全保險協(xié)會）的一份關(guān)于自動駕駛的研究報告，引發(fā)熱議。里面提到，如果自動駕駛系統(tǒng)與人的駕駛方式相似，那么可能只能避免大約三分之一的交通事故。這與很多人眼中，自動駕駛能避免絕大多數(shù)事故，有點不太相符。此前，麥肯錫發(fā)布了一份報告，稱自動駕駛的全面普及可以減少90%的交通事故，每年減少1900億美元損害賠償和醫(yī)療費用。

其實，這份報告主要是提醒自動駕駛技術(shù)對于安全性的關(guān)注，提到的一些數(shù)據(jù)都是有前提的。為了避免一些片面解讀，本文對IIHS的研究文章進行中英文對照翻譯，供大家參考！

標題：
Self-driving vehicles could struggle to eliminate most crashes
自動駕駛汽車很難規(guī)避大部分撞車事故


Will autonomous vehicles be better than humans at predicting, planning and execution?
自動駕駛汽車在判斷、決策和執(zhí)行方面能比人類做得更好嗎？

Driver mistakes play a role in virtually all crashes. That’s why automation has been held up as a potential game changer for safety. But autonomous vehicles might prevent only around a third of all crashes if automated systems drive too much like people, according to a new study from the Insurance Institute for Highway Safety.
幾乎所有的車禍都有人為失誤的影響。這就是為什么大家認為，在安全方面自動化有可能改變這一局面。但是，IIHS的一項最新研究表明，如果自動駕駛系統(tǒng)與人的駕駛方式相似，那么可能只能避免大約三分之一的交通事故。

“It’s likely that fully self-driving cars will eventually identify hazards better than people, but we found that this alone would not prevent the bulk of crashes,” says Jessica Cicchino, IIHS vice president for research and a coauthor of the study.
“自動駕駛汽車能比人更快識別出危險，但我們發(fā)現(xiàn)，僅憑這一點能避免的撞車事故有限。”IIHS副總裁同時也是該研究的參與者Jessica Ciccino說。

Conventional thinking has it that self-driving vehicles could one day make crashes a thing of the past. The reality is not that simple. According to a national survey of police-reported crashes, driver error is the final failure in the chain of events leading to more than 9 out of 10 crashes.
傳統(tǒng)觀念認為，總有一天，自動駕駛汽車會使撞車事故成為過去式?，F(xiàn)實并非如此簡單。根據(jù)一項針對交通事故的全國性調(diào)查，10起交通事故中至少有9起是因為駕駛員的失誤。

But the Institute’s analysis suggests that only about a third of those crashes were the result of mistakes that automated vehicles would be expected to avoid simply because they have more accurate perception than human drivers and aren’t vulnerable to incapacitation. To avoid the other two-thirds, they would need to be specifically programmed to prioritize safety over speed and convenience.
但該研究所的分析表明，通過自動駕駛可以避免的事故，只占1/3。原因很簡單，因為它們比人類駕駛員的感知能力更準確，而且不會輕易喪失駕駛能力。只有自動駕駛車的程序認為安全優(yōu)先于速度和便利性，才能夠規(guī)避更多撞車事故。

“Building self-driving cars that drive as well as people do is a big challenge in itself,” says IIHS Research Scientist Alexandra Mueller, lead author of the study. “But they’d actually need to be better than that to deliver on the promises we’ve all heard.”
IIHS研究專家，同時也是該項研究的主要作者Alexandra Mueller說：“自動駕駛想達到人類駕駛水平本身就是巨大的挑戰(zhàn)。”“實際上需要超越人類，才能達到減少事故的目標。”

To estimate how many crashes might continue to occur if self-driving cars are designed to make the same decisions about risk that humans do, IIHS researchers examined more than 5,000 police-reported crashes from the National Motor Vehicle Crash Causation Survey. Collected by the National Highway Traffic Safety Administration, this sample is representative of crashes across the U.S. in which at least one vehicle was towed away, and emergency medical services were called to the scene.
如果自動駕駛汽車能像人類一樣對風險做出預(yù)判，還會發(fā)生多少起事故呢？為了估算這一數(shù)值，IIHS的研究人員對全國機動車事故原因調(diào)查中5000多起警方報告的事故進行了調(diào)查。該樣本由NHTSA（美國公路交通安全管理局）收集，針對的是美國各地發(fā)生的至少一輛車被拖走，并且緊急醫(yī)療服務(wù)人員被派往現(xiàn)場的車禍事故。

The IIHS team reviewed the case files and separated the driver-related factors that contributed to the crashes into five categories:

IIHS小組審查了案件檔案，并將導致車禍的駕駛員相關(guān)因素分為五類：

“Sensing and perceiving” errors included things like driver distraction, impeded visibility and failing to recognize hazards before it was too late.
“Predicting” errors occurred when drivers misjudged a gap in traffic, incorrectly estimated how fast another vehicle was going or made an incorrect assumption about what another road user was going to do.
“Planning and deciding” errors included driving too fast or too slow for the road conditions, driving aggressively or leaving too little following distance from the vehicle ahead.
“Execution and performance” errors included inadequate or incorrect evasive maneuvers, overcompensation and other mistakes in controlling the vehicle.
“Incapacitation” involved impairment due to alcohol or drug use, medical problems or falling asleep at the wheel.
“感知”錯誤：駕駛員分心、視線受阻和未及時發(fā)現(xiàn)危險等。
“判斷”錯誤：錯估另外一輛車的速度、方向變化和行車間隙。
“決策”錯誤：車速太快或太慢、駕駛情緒過激或與前方車輛跟車距離太小。
“執(zhí)行”錯誤：規(guī)避不充分或不正確、過度補償和其他駕駛操作層面的錯誤。
“喪失駕駛能力”：因酒精、毒品、藥物等導致駕駛員能力受損的情況。

The researchers also determined that some crashes were unavoidable, such as those caused by a vehicle failure like a blowout or broken axle.
研究人員還表明，有些事故是不可避免的，比如那些由于車輛故障造成的（如爆胎或車軸斷裂）。

For the study, the researchers imagined a future in which all the vehicles on the road are self-driving. They assumed these future vehicles would prevent those crashes that were caused exclusively by perception errors or involved an incapacitated driver. That’s because cameras and sensors of fully autonomous vehicles could be expected to monitor the roadway and identify potential hazards better than a human driver and be incapable of distraction or incapacitation.
在這項研究中，研究人員設(shè)想，在未來，路上所有車輛都是自動駕駛的。他們認為這能規(guī)避掉由感知錯誤和駕駛能力喪失導致的事故。這是因為全自動汽車的攝像頭和傳感器在監(jiān)控道路和識別潛在危險方面比人類駕駛員做得更好，而且它們不會分散注意力或喪失駕駛能力。

Crashes due to only sensing and perceiving errors accounted for 24 percent of the total, and incapacitation accounted for 10 percent. Those crashes might be avoided if all vehicles on the road were self-driving — though it would require sensors that worked perfectly and systems that never malfunctioned. The remaining two-thirds might still occur unless autonomous vehicles are also specifically programmed to avoid other types of predicting, decision-making and performance errors.
因感知錯誤造成的事故占24%，而因駕駛員喪失能力造成的占10%。如果路上的所有車輛都實現(xiàn)自動駕駛，這些事故是可以避免的——但這需要車輛傳感器工作正常和系統(tǒng)運轉(zhuǎn)良好。剩下的三分之二的事故仍然可能會發(fā)生，除非自動駕駛車輛的程序也能避免其他幾種類型的錯誤。

Consider the crash of an Uber test vehicle that killed a pedestrian in Tempe, Arizona, in March 2018. Its automated driving system initially struggled to correctly identify 49-year-old Elaine Herzberg on the side of the road. But once it did, it still was not able to predict that she would cross in front of the vehicle, and it failed to execute the correct evasive maneuver to avoid striking her when she did so.
以2018年3月發(fā)生在亞利桑那州坦佩市，一輛Uber測試車撞死一名行人的事故為例。車輛的自動駕駛系統(tǒng)很難正確識別路邊的行人。就算它能識別出，它仍然無法預(yù)測行人是否會從車前穿過，而且它也無法執(zhí)行正確的規(guī)避動作來避免撞到行人。

Planning and deciding errors, such as speeding and illegal maneuvers, were contributing factors in about 40 percent of crashes in the study sample. The fact that deliberate decisions made by drivers can lead to crashes indicates that rider preferences might sometimes conflict with the safety priorities of autonomous vehicles. For self-driving vehicles to live up to their promise of eliminating most crashes, they will have to be designed to focus on safety rather than rider preference when those two are at odds.
在研究樣本中，約40%的事故是由決策失誤（如超速和非法操作）造成的。駕駛員經(jīng)過深思熟慮做出的決定可能導致撞車事故，這一事實表明，駕駛員的決策和偏好有時可能與自動駕駛車輛的安全優(yōu)先級相沖突。自動駕駛汽車要想實現(xiàn)消除大多數(shù)事故的承諾，就必須在設(shè)計時重視車輛安全，而不是在兩者發(fā)生沖突時關(guān)注駕駛者的偏好。

Self-driving vehicles will need not only to obey traffic laws but also to adapt to road conditions and implement driving strategies that account for uncertainty about what other road users will do, such as driving more slowly than a human driver would in areas with high pedestrian traffic or in low-visibility conditions.
自動駕駛車輛不僅需要遵守交通法規(guī)，還需要考慮路況，考慮到其他道路使用者的不確定性，并采取相應(yīng)駕駛策略。例如在行人流量大或能見度低的地方，要開得比人更慢。

“Our analysis shows that it will be crucial for designers to prioritize safety over rider preferences if autonomous vehicles are to live up to their promise to be safer than human drivers,” Mueller says.
Mueller說：“我們的分析表明，如果自動駕駛汽車要實現(xiàn)比人類駕駛汽車更安全的承諾，那么設(shè)計師就必須將安全性放在優(yōu)先于駕駛者偏好的位置。”

關(guān)于自動駕駛，你是不是有很多疑問呢？一場年度自動駕駛技術(shù)盛宴即將到來！