F1 2026规则下车手天赋仍能发挥重要作用
在车手们对软件过度干预赛车控制提出大量批评的背景下，去年的车队总冠军车队表示，车手天赋依然重要。迈阿密大奖赛前对规则进行了微调，但近期动力单元自学习算法导致的圈速差异又引发新的担忧。
在F1 2026规则下，车手天赋如何仍能产生巨大差异
在车手们对软件对其赛车控制进行粗暴干预提出大量批评之际，去年的车队总冠军车队表示，天赋依然重要
乔治·拉塞尔，梅赛德斯；奥斯卡·皮亚斯特里，迈凯伦
自F1赛季开始前，抱怨就一直没有停过。这些抱怨会周期性地加剧，尤其是在所谓的“能量匮乏”赛道上，那里的弯道与直道比例以及弯道类型不利于新一代赛车在单圈中为电池充电。
迈阿密大奖赛前实施的一项规则微调，减少了部分因车手处于不同电量状态而备受批评的“溜溜球式比赛”。但最近，动力单元的自学习算法对赛道条件和驾驶技术的微小变化做出反应，由此导致的速度差异引发了大量焦虑。
在斯帕，奥斯卡·皮亚斯特里将其描述为“一种相当糟糕的赛车方式”。
但情况在短期内不会改变，尽管有人主张完全禁止机器学习工具——通常引用F1运动规则中的一句话作为终结语。B1.9.1条规定：“车手必须独自且无辅助地驾驶赛车”。
而且仍有理由认为，最好的车手将能应对这些新挑战，尽管它们可能既晦涩又烦人。这是迈凯伦工程总监尼尔·霍尔迪在F1夏休期举行的一次圆桌采访中表达的观点。
当被Autosport问及目前是否能准确评估车手表现时，他说：“实际上，那些明白自己需要做什么的车手，那些头脑清醒的车手，那些世界上最好的F1车手，仍然会是最快的。”
“可能变化在于……这不再完全关乎最高速度，而是关乎背后产生那个圈速的思维过程。”
“斯帕就是一个很好的例子，实际上奥斯卡确实采用了略有不同的能量部署计划，这意味着他损失了零点几秒，但仍能与兰多持平。但我认为这些事情最终会平衡。你仍然能在总圈速以及比赛中看到好车手与不那么好的车手之间的差距。”
“我的回答真的是，最终一切都会扯平。可能某场比赛或某节排位赛会对某位车手或另一位不利，但我仍然认为，到赛季结束时，你会看到最好的车手出现在最好的位置上。”
卫冕冠军兰多·诺里斯在匈牙利赢得了今年首个大奖赛胜利。
从根本上说，当前规则集的局限性——电能部署与内燃机动力接近50:50的分配——在于赛车能储存的能量总量。由于电池必须在整个单圈中充电，出于安全和性能原因，软件干预是必要的，以逐渐减少电能部署，而不是让它突然耗尽。
机器学习软件还具有性能功能，它构成了工程师们建立“最优圈”尝试的一部分——“最优圈”是收获与部署的组合，能给出最快圈速。迈阿密规则调整的效果是让FIA有更大余地降低能量回收限制，以牺牲峰值速度换取更持续（尽管略低）的最高速度。
但所有这些都要求车手在控制输入（如油门位置）上保持一致。例如，如果他们入弯过于激进，在同一位置比队友损失更多速度，重新加速会消耗电能，因此他们会在单圈后段因可用能量减少而受到惩罚。
Houldey认为，这与车手习惯的风险与回报顺序不同：如今激进驾驶带来的回报少于更理性、更自律的驾驶方式。
“这绝对更像是一场脑力游戏，”他说，“而且车手需要处理的事情肯定比以前多得多——即便是2022-25年（地面效应时代），车手也有很多事情要做。但现在又多了一层，你不仅要把弯道速度做对，还必须把能量部署方式、油门位置等所有细节都做到绝对正确。”
“对工程团队来说，要从这些赛车中挖掘最大潜力，技术挑战肯定更大；对车手来说，要从这些赛车中挖掘最大潜力，技术挑战也更大。而最终，一如既往，赛车、车手和工程团队的最佳组合会做得最好。”
英文原文
How driver talent still makes a big difference under F1 2026 rules
Amid much criticism from the drivers about the heavy-handed intervention of software in their control of the car, last year’s constructors’ championship-winning team says talent still counts
George Russell, Mercedes, Oscar Piastri, McLaren
The complaints have been rumbling since before the start of the Formula 1 season. Periodically they intensify, especially at so-called ‘energy-starved’ circuits where the ratio of corners to straights, and the type of corners, mitigate against the new-generation cars being able to recharge their batteries through the lap.
A tweak to the regulations implemented ahead of the Miami Grand Prix reduced some of the much-criticised ‘yo-yo racing’ caused by drivers being in different states of charge. But more recently speed differences caused by the power units’ self-learning algorithms reacting to small variations in track conditions and driving technique have caused a great deal of angst.
In Spa, Oscar Piastri described it as a "pretty crap way of going racing".
But the situation isn’t going to change in the immediate future, even though there are those who argue in favour of the machine-learning tools being banned entirely – usually citing a phrase from F1’s sporting regulations as a mic drop. Section B1.9.1 states: "The driver must drive the car alone and unaided".
And there is still an argument to state that the best drivers will manage these new challenges, abstruse and bothersome though they may be. That was the perspective of McLaren’s technical director for engineering Neil Houldey in a round-table interview conducted during F1’s summer break.
"Actually the drivers who understand what they need to do, the ones who are switched on, the ones who are the best F1 drivers in the world are the ones who are still going to be the fastest," he said when asked by Autosport if it’s currently possible to assess driver performance accurately.
"It may have changed in terms of… It's not all about top speed, it's about the thought processes going on in the background that generate that lap time.
"[Spa] is a good example where actually Oscar did get a slightly different deployment schedule, which meant he lost a couple of tenths, but was up with Lando. But I think those things balance out. You still see the difference between the good drivers and the less good drivers in the overall lap time and then in the race as well.
"My answer really would be that it all evens out in the end. That there might be a race or a qualifying session here or there where that disadvantages one driver or the other, but I still think you see the best drivers come out in the best place at the end of the season."
Defending champion Lando Norris took his first GP win of the year in Hungary
Fundamentally the limitation of the current ruleset, with its near-50:50 split of electrical deployment to internal combustion power, is the amount of energy the car can store. Since the battery has to be recharged throughout the lap, software intervention is required for safety as well as performance reasons, to taper the electrical deployment rather than have it run out suddenly.
The machine-learning software also has a performance function in that it forms part of the engineers’ attempts to establish the "optimal lap" – the combinations of harvesting and deployment which gives the fastest lap time. The effect of the Miami rule tweaks, which gave the FIA more leeway to reduce energy harvesting limits, was to trade off peak speeds against a more sustained (if slightly lower) top speed.
But all this requires the driver to be consistent with the control inputs such as throttle position. If they attack a corner too aggressively and scrub off more speed than their team-mate in the same place, for instance, re-accelerating costs electrical power so they are punished by having less available later in the lap.
Houldey views it as a different order of risk and reward than drivers are used to: aggression now carries fewer rewards than a more cerebral and disciplined approach.
"It's definitely more of a thinking game," he said. "And there's definitely a lot more going on for the drivers than there was – even 2022-25 [the ground-effect era] there was a lot happening for the drivers to be doing. But now it's that extra bit, and you've got to get not only your cornering speeds right, but you've got to get the way you go about deploying your energy, your throttle position, whatever it might be, absolutely right as well.
"It's certainly more technically challenging for the engineering team to get the most out of these cars, and it's more technically challenging for the drivers to get the most out of these cars. And in the end, as always, the best combination of car, driver, engineering team ends up doing the best job."
Autosport ·
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