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

Driver unique acceleration behaviours and stability over two years

The identification of characteristic individual driving behaviours is an emerging challenge that occurs within longitudinal studies of drivers to distinguish different drivers of a shared vehicle. It also has application in the insurance industry where insurance risk and associated owner premium depends on the diversity or lack thereof of drivers for a vehicle such as a vehicle driven/never driven by secondary drivers that have higher risk driving behaviours. Lastly, emerging self driving vehicles could allow the owner to personalize the vehicle behaviour to drive more like them increasing owner acceptance of the technology. In this paper, a big data set of driving data for 14 drivers is analyzed - a single year of data includes over 250,000 km and almost 5000 hours of driving for the 14 drivers. Analytics methods are presented that identify acceleration events within the data for the drivers and it then proposes a two-phase relationship model for these events that is indicative of unique drivers' behaviour. The results show that the two-phase acceleration relationship for maximum and mean acceleration allows 84.6% and 80.2% of the 91 driver pairs that can be formed from the 14 drivers to be distinguished (p<5%). The paper shows the stability of two-phase acceleration and deceleration relationships for the 14 drivers as the second year of events for each of the 14 drivers have a mean correlation with the first year relationships of 0.971 or higher