Abstract
Accurate recognition of driving intentions can delay upshifts under the intention of quick acceleration to maximize vehicle power performance; avoid frequent gear changes in automatic transmissions for rapid deceleration intention and make all power to flow to the bucket in the desire for fast motion of cylinders. However, due to the ambiguity of the human intentions and multiple meanings of depressing on the accelerator pedal in wheel loader, it is difficult to recognize driving intention. Nevertheless, the driver’s intentions are directly reflected in the accelerator pedal, brake pedal and hydraulic valve control handle. By detecting these observable signals such as the signals of acceleration pedal’s displacement and velocity, brake pedal’s displacement and velocity and valve status Gaussian Mixture – Hidden Markov Model(MGHMM) can recognize the unobservable driving intentions. The experiment is done in Simulink and the results show that MGHMM can recognize driving intentions as expected.
Highlights
Wheel loader often works in construction site and the bumpy road condition brings shakes to the driver, which always fatigue the driver
The application of automatic transmission in wheel loader, relieve driver’s fatigue, but the automatic transmission makes the decisions of gearshift only when the throttle opening and vehicle speed satisfy the shift requirements
Some recent endeavours in this direction in car using a popular methodology Gaussian Mixture - Hidden Markov Models (MGHMM) [6] such as work from H Hou [7], H Berndt [8] et al is fruitful. It can recognize the implicit parameter of temporal data patterns and build the recognition model, and the driving intention recognition itself is a problem of Hidden Markov because human behaviour is made up of a sequence of internal “mental” states which is not directly observable, but it can be inferred from observable signals, such as acceleration pedal’s displacement and velocity as well as brake pedal’s displacement and velocity
Summary
Wheel loader often works in construction site and the bumpy road condition brings shakes to the driver, which always fatigue the driver. If gearshift intention can be recognized accurately, the problem of frequent gearshift in rapid deceleration would be avoid by forcing downshift. Some recent endeavours in this direction in car using a popular methodology Gaussian Mixture - Hidden Markov Models (MGHMM) [6] such as work from H Hou [7], H Berndt [8] et al is fruitful It can recognize the implicit parameter of temporal data patterns and build the recognition model, and the driving intention recognition itself is a problem of Hidden Markov because human behaviour is made up of a sequence of internal “mental” states which is not directly observable, but it can be inferred from observable signals, such as acceleration pedal’s displacement and velocity as well as brake pedal’s displacement and velocity. Through the trained MGHMM models driving intention can be recognized online in MATLAB Simulink
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