Cars can brake in danger by treating the truth as partly true
Nissan uses it to decide when to brake, based on acceleration, speed, and wheel speed.
AI Accelerator Institute · What are the top 7 branches of artificial intelligence?A car facing a possible crash still has to decide about the brakes, even when the facts are not clear cut. Fuzzy logic treats that kind of choice as a matter of degree, rather than simply true or false.
It is one form of artificial intelligence that works more like people do, allowing an answer to be partly right. Between digital yes and no lie many possibilities, so an uncertain reading can lean toward one outcome without fitting it exactly. That makes the method useful for questions where strict categories do not fit well.
In general, a fuzzy logic system has four parts. Fuzzification converts the inputs into fuzzy terms. A rule base stores the if-then conditions used for judgment. An inference engine checks how closely the fuzzy inputs fit the rules. Defuzzification turns that fuzzy result into a crisp value, a single number a machine can use.
To picture the form of the reasoning, consider a hypothetical illustration: if one reading were high and another looked unusual, then stronger braking might apply. That shows only how such a rule might read, not Nissan's actual rule.
At Nissan, control of brakes in dangerous situations depends on individual car acceleration, speed, and wheel speed.
Questions people ask
How is fuzzy logic different from normal computer logic?
Standard computer logic is binary, so a statement is either true or false, 1 or 0. Fuzzy logic allows degrees of truth, such as 0.2 true or 0.8 true, which is useful when sensor readings are noisy or a situation is developing.
Do all cars use fuzzy logic for braking?
No. Modern anti-lock brakes and stability systems often use conventional control methods, detailed physics models, and other kinds of AI. Fuzzy logic is one established approach because it handles vague, rapidly changing inputs well.
Where else is fuzzy logic used?
They were developed in the 1960s, largely associated with electrical engineer Lotfi Zadeh at the University of California, Berkeley, who introduced fuzzy sets as a way to handle partial truth. The idea later spread widely into appliances, elevators, subways, cameras, and cars.