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文章來源：《經濟學人》的主要讀者群體是高級知識分子以及準備考研、考博的同學學習英語的閱讀資料。在本科學生中，越來越多的同學也開始關注這份報紙,經濟學人相比較於其他國內外語報紙的態度更客觀，視角更寬。

Intelligence test

智能測試

A computer program that learns how to save fuel

智能節能軟件

Charge

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. But it is not only the driving of them that will benefit. AI will also permit such cars to use energy more sparingly.

從控制車輛躲避亂穿馬路行人到緊急制動再到無人駕駛，毫無疑問，人工智能是未來汽車重要構成要素。除了優化駕駛過程，人工智能在汽車節能方面也表現不俗。

Cars have long had computerised engine-management that responds on the fly to changes in driving conditions. The introduction of electric power has, however, complicated matters. Hybrids, which have both a petrol engine and an electric motor run by a battery that is recharged by capturing kinetic energy as the vehicle slows or brakes, need more management than does a petrol engine alone. Things get even harder with plug-in hybrids, which can be recharged from the mains and have a longer electric-only range.

一直以來，汽車都是採用計算機系統控制引擎，對行車過程中實時狀況做出及時反應。汽車製造引入電力驅動使行車過程更為複雜。同時具有汽油發動機和電動機的混合動力車由電池運行，該電池通過獲取車輛減速或制動時的動能來進行充電，因此這種汽車運行時比只用汽油機的車輛需要更多的控制。使用插電式混合動力車則更加複雜，它可以由電源直接充電，供電後，行駛里程更長。

This is where AI could help, reckon Xuewei Qi, Matthew Barth and their colleagues at the University of California, Riverside. They are developing a system of energy management which uses a piece of AI that can learn from past experience.

加利福尼亞大學（河濱分校）齊學偉教授、貝斯馬修教授及其團隊認為，人工智能在混合動力車節能方面可以派上用場。他們正在研發一套汽車能耗管理系統，其中就使用了具有記憶功能的人工智能芯片。

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. In each segment the system checks to see if the vehicle has encountered the same driving situations before, 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. If the situation is similar, it employs the same energy-management strategy that it used previously for the next segment of the journey. 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. Ultimately, the idea is that the algorithm will also learn from the experiences of its brethren in other cars, by arranging for all such systems to share their data online.

這套系統通過將行車過程切分為若干小部分來運行，每部分可能小於一分鐘。在每一部分，系統都會通過一系列數據，比如當前交通狀況、車速、實時位置、時間、路面狀況、電池電量以及引擎耗油率等來判斷車輛是否遇到過相似的駕駛狀況。如果狀況相近，它就會採取歷史記錄中的節能方案。對於陌生狀況，系統則會估算最佳能耗，並把分析結果自動加入其數據庫，以供未來參考。該設想預期通過車主在線共享行車數據，最終實現節能目標。

Ideally, such a system would be fed its route and destination in advance, to make things easier to calculate. But Dr Qi and Dr Barth are realists, and know that is unlikely to happen. If a satnav were invoked, it would be able to pass relevant information on to the algorithm. But drivers use satnavs only to get them to unfamiliar destinations. Hence the researchers’ decision to design a system that does not rely on knowing where it is going.

理想情況下，該系統可以提前輸入目的地和行車路線以方便數據計算。但齊博士和貝斯博士都明白，這一願望在現實中是不可能實現。如果使用衛星導航，它確實可以給該系統傳送相關信息，但司機只有在去陌生地點時才會使用到導航。因此研究人員決定研發一款不依賴於目的地位置的系統。

It seems to work—at least, in simulations. Using live traffic information to mimic journeys in southern California, Dr Qi and Dr Barth compared their algorithm with a basic energy-management system for plug-in hybrids that simply switches to combustion power once the battery is depleted. As they report in a paper to be published in IEEE Transactions on Intelligent Transportation Systems, their system was 10.7% more efficient than the conventional one. If the system is aware in advance that a recharging station will be used as part of the trip (which might be arranged by booking one via the vehicle’s information screen) it can spread the use of electric power throughout the journey, to maximum advantage, knowing when the battery will be topped up. In such situations the average fuel saving was 31.5%.

至少在模擬測試中，這一設想看起來是可行的。通過採用實時交通信息來模擬南加州道路情況，兩位教授把他們的運算系統與插電式混合動力車的基本能耗管理系統進行了比較，一旦混合動力車電池耗盡，它就會切換到燃料模式繼續提供動力。測試結果正如他們即將發表在"電器和電子工程師學會---智能交通系統業務"的論文所述，他們的系統相比較傳統系統節能達 10.7%。如果該系統提前把充電站算入行程（通過車載顯示屏來預先設置），電量充滿後，它就可以最大化地把用電量分配到整個行程，汽車平均耗油量節省 31.5%。

Dr Qi and his colleagues now hope to work with a carmaker to test the algorithm on real roads. If all goes well, and their system proves able to cope with the nightmares of commuting in southern California, they will not be left stranded on the hard shoulder.

目前，齊博士及其團隊希望與汽車製造商合作，來測試真實路況下系統運行情況。如果一切順利，且他們的系統確實可以解決南加州交通困境的話，他們至少就不會因為汽車沒油而被困到緊急停車帶。