Real-time characterisation of driver steering behaviour

In recent years the application of driver steering models has extended from the off-line simulation environment to autonomous vehicles research and the support of driver assistance systems. For these new environments there is a need for the model to be adaptive in real-time, so the supporting vehicle systems can react to changes in the driver, their driving style, mood and skill. This paper provides a novel means to meet these needs by combining a simple driver model with a single track vehicle handling model in a parameter estimating filter – in this case an Unscented Kalman Filter. Although the steering model is simple, a motion simulator study shows it is capable of characterising a range of driving styles and may also indicate the level of skill of the driver. The resulting filter is also efficient – comfortably operating faster than real-time – and it requires only steer and speed measurements from the vehicle in addition to reference path. Adaptation of the steer model parameters is demonstrated along with robustness of the filter to errors in initial conditions, using data from five test drivers in vehicle tests carried out on the open road

Estimation of parameters and delay in driver models using L1-regularization

A new method is proposed for driver reaction time(delay) and parameter estimation using a generic lateral model that is expressed in terms of the steering angle, yaw rate and lateral lane offset. The idea behind the presented method is to reformulate the original driver model with an overparametrized one and then use the L1-regularization method to enforce sparsity and thereby estimate the delay together with the parameters of the original model. A sequential algorithm is then presented to obtain better estimates of the parameters with a model in which the delay is fixed

Estimation of parameters and delay in driver models using L1-regularization

A new method is proposed for driver reaction time(delay) and parameter estimation using a generic lateral model that is expressed in terms of the steering angle, yaw rate and lateral lane offset. The idea behind the presented method is to reformulate the original driver model with an overparametrized one and then use the L1-regularization method to enforce sparsity and thereby estimate the delay together with the parameters of the original model. A sequential algorithm is then presented to obtain better estimates of the parameters with a model in which the delay is fixed