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From avoiding jaywalkers（乱穿马路的人） to emergency braking to eventually， perhaps， driving 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 cautiously.
Cars have long had computerized engine?management that responds on the fly to changes in driving conditions. The introduction of electric power has， however， complicated matters. Hybrids（混合动力车）， which have both a petrol engine and an electric motor run by a battery that is recharged by capturing kinetic energy（动能） 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（电源） and have a longer electric?only range.
This is where AI could help， estimate 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.
Their algorithm（計算程序） works by breaking the trip down into small segments（部分）， 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 vehicles speed， location， time of day， the gradient（坡度） of the road， the batterys present state of charge and the engines 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.
Cars have long had computerized engine?management that responds on the fly to changes in driving conditions. The introduction of electric power has， however， complicated matters. Hybrids（混合动力车）， which have both a petrol engine and an electric motor run by a battery that is recharged by capturing kinetic energy（动能） 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（电源） and have a longer electric?only range.
This is where AI could help， estimate 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.
Their algorithm（計算程序） works by breaking the trip down into small segments（部分）， 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 vehicles speed， location， time of day， the gradient（坡度） of the road， the batterys present state of charge and the engines 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.