A computer program that learns how to save fuel 一种习得省油的电脑程序

　　From avoiding jaywalkers to emergency braking to eventually, perhaps, chauffeuring the vehicle itself,  从避开乱穿马路的行人到紧急制动再到自动驾驶，

　　it is clear that artificial intelligence (AI) will be an important part of the cars of the future. 毫无疑问，人工智能（AI）将会成为未来汽车的重要组成部分。

　　But it is not only the driving of them that will benefit. 这不仅能给驾驶本身带来方便。

　　AI will also permit such cars to use energy more sparingly. AI还能使那些车更节能。

　　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应用于汽车，用以快速对行驶过程中路面状况的变化做出反应。

　　The introduction of electric power has, however, complicated matters. 然而，电力应用却存在难以处理的问题。

　　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,  混合动力车，同时具有汽油发动机与电动发动机，电池在汽车减速或刹车时通过动能转化而充电，

　　need more management than does a petrol engine alone.  但这需要投入较普通车更多的引擎管理。

　　Things get even harder with plug-in hybrids, which can be recharged from the mains and have a longer electric-only range. 带插头接点的混合动力车虽然能通过电源充电，并拥有更长的电动行驶时间，但引擎的智能化管理更加繁琐。

　　This is where AI could help, reckon Xuewei Qi, Matthew Barth and their colleagues at the University of California, Riverside.  这是AI的用武之地，来自位于里弗赛德的加州大学的Xuewei Qi、Matthew Barth和他们同事如此认为。

　　They are developing a system of energy management which uses a piece of AI that can learn from past experience. 他们正在研发一套能源管理系统，该系统能利用人工智能学习以往的经验。

　　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.  该程序的工作机制是在行驶的过程中，将整个行程拆分成独立小路段，每个路段的时长可能还不到一分钟。

　　In each segment the system checks to see if the vehicle has encountered the same driving situations before,  在每个路段中，系统通过校验以确认该车是否曾遇到过相同的行驶状态，

　　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. 参考数据包括交通信息，行驶速度，行驶位置，在一天中的时间，路面坡度，电池的电量状态以及发动机的燃油消耗率。

　　If the situation is similar, it employs the same energy-management strategy that it used previously for the next segment of the journey.  如果条件相似，人工智能系统就会在剩下的行程中启动与前一次相同的行驶策略。

　　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.  如果是未遇到过的情况，系统会评估出最佳省能方案并将结果添加到数据库中以便日后的参考。