A novel diver pedal behavior model framework incorporating vehicle and road environment information

Driver pedal behavior models can benefit vehicle powertrain control and intelligent driving assistance system designs. This paper proposes a novel way to describe the pedal position signal by decomposing it into a series of actions and a nonlinear stochastic driver pedal behavior model is presented under the input-output hidden Markov model (IOHMM) framework. This framework can incorporate the vehicle and road environment information as inputs. A driver model under this IOHMM framework is identified using human driver data collected on a driving simulator and the model behavior is analyzed in comparison with the human driver data. Closed-loop simulation using the model integrated with a vehicle model and a road environment model shows that the outputs of proposed pedal behavior model have similar patterns to those of human drivers.