Gear-position-decision (GPD) tactics strongly affect the performances
of automatic transmissions (AT) and, therefore, the performance
of the vehicle.Since the electronic control methods were introduced into
ATs, many advanced techniques have been raised to make AT vehicles more
human friendly and better in fuel economy and dynamic behaviors.As
a type of emerging AT, the automated manual transmissions (AMT) are
being researched and developed in all relevant technologies.In this paper,
we proposed a driving knowledge-based GPD (KGPD) method for AMTs.
The KGPD algorithm is composed of a driving environments and driver’s
intentions estimator, the shift schedules for each typical driving environment
and driver’s intention situations, and an inference logic to determine
the most proper gear position for the present situation.The estimator identifies
the driving environments and features of driver’s intentions, which
are divided into some typical patterns.Based on the identified results, the
gear-position inference algorithm calculates the best gear position at the
moment.In fact, the method just simulates the course of a driver’s making
gear-position decision when driving an automobile with manual transmission.
The test results show that the AMT with the method gives less unnecessary
shifting, conducts more proper gear positions, and behaves better in
subjective assessment than that with the method that is directly based only
on automotive state parameters.