評論
打印
Science and technology: Vehicle engine management: Intelligence test
科技：車輛引擎管理：智能測試
A computer program that learns how to save fuel
一種習得省油的電腦程序
From avoiding jaywalkers to emergency braking to eventually, perhaps, chauffeuring the vehicle itself, it is clear that artificial intelligence (AI) will be an important part of the cars of the future.
從避開亂穿馬路的行人到緊急制動再到自動駕駛，毫無疑問，人工智能（AI）將會成為未來汽車的重要組成部分。
But it is not only the driving of them that will benefit.
這不僅能給駕駛本身帶來方便。
AI will also permit such cars to use energy more sparingly.
AI還能使那些車更節(jié)能。
Cars have long had computerized engine-management that responds on the fly to changes in driving conditions.
引擎的智能化管理早已應(yīng)用于汽車，用以快速對行駛過程中路面狀況的變化做出反應(yīng)。
The introduction of electric power has, however, complicated matters.
然而，電力應(yīng)用卻存在難以處理的問題。
Hybrids, which have both a petrol engine and an electric motor run by a battery that is recharged by capturing kineticenergy as the vehicle slows or brakes, need more management than does a petrol engine alone.
混合動力車，同時具有汽油發(fā)動機與電動發(fā)動機，電池在汽車減速或剎車時通過動能轉(zhuǎn)化而充電，但這需要投入較普通車更多的引擎管理。
Things get even harder with plug-in hybrids, which can be recharged from the mains and have a longer electric-only range.
帶插頭接點的混合動力車雖然能通過電源充電，并擁有更長的電動行駛時間，但引擎的智能化管理更加繁瑣。
This is where AI could help, reckon Xuewei Qi, Matthew Barth and their colleagues at the University of California, Riverside.
這是AI的用武之地，來自位于里弗賽德的加州大學(xué)的Xuewei Qi、Matthew Barth和他們同事如此認為。
They are developing a system of energy management which uses a piece of AI that can learn from past experience.
他們正在研發(fā)一套能源管理系統(tǒng)，該系統(tǒng)能利用人工智能學(xué)習以往的經(jīng)驗。
Their algorithm works by breaking the trip down into small segments, each of which might be less than a minute long, as the journey progresses.
該程序的工作機制是在行駛的過程中，將整個行程拆分成獨立小路段，每個路段的時長可能還不到一分鐘。
In each segment the system checks to see if the vehicle has encountered the same driving situations before,
在每個路段中，系統(tǒng)通過校驗以確認該車是否曾遇到過相同的行駛狀態(tài)，
using data ranging from traffic information to the vehicle’s speed, location, time of day, the gradient of the road, the battery’s present state of charge and the engine’s rate of fuel consumption.
參考數(shù)據(jù)包括交通信息，行駛速度，行駛位置，在一天中的時間，路面坡度，電池的電量狀態(tài)以及發(fā)動機的燃油消耗率。
If the situation is similar, it employs the same energy-management strategy that it used previously for the next segment of the journey.
如果條件相似，人工智能系統(tǒng)就會在剩下的行程中啟動與前一次相同的行駛策略。
For situations that it has not encountered before, the system estimates what the best power control might be and adds the results to its database for future reference.
如果是未遇到過的情況，系統(tǒng)會評估出最佳省能方案并將結(jié)果添加到數(shù)據(jù)庫中以便日后的參考。
英文文本來自經(jīng)濟學(xué)人 譯文來自欄目二第五組
