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

Influence of accelerator pedal force feedback on truck drivers' speed control

The UK government has set clear targets for 80% reductions (compared with 1990 levels) in greenhouse gas emissions by 2050 and pressure is increasing on the road transport industry to reduce the fuel consumption and harmful exhaust emissions of Heavy Goods Vehicles (HGVs). Vehicle manufacturers and operators alike are having to investigate and find new ways of making reductions. It is thought that improving driver behaviour offers significant potential for these reductions in fuel consumption and emissions. This thesis considers the use of Active Accelerator Pedals (AAPs) and the potential for improved driver performance that they may offer by providing pedal force feedback to the driver. In order to develop understanding of the interactions between the human driver and accelerator pedal, two near identical tractor units, operated by Turners of Soham Ltd, were fitted with the a data logger. Data was collected and stored over a period of four months as they operated on the road. This data provided the basis for a vehicle model to be developed using real-world conditions, rather than strictly controlled test track conditions. Analysis of the behaviour of the two drivers also identified differences is styles, and explained 7% fuel consumption differences between the two drivers when negotiating roundabouts. A new mathematical model of the human driver’s longitudinal control was also developed to include the driver’s cognitive control of the accelerator pedal. Model Predictive Control theory, commonly used for modelling the driver’s steering control, was used and different driving styles were replicated by varying the weightings in a cost function, and a series of driving simulator experiments were performed to validate the model. Nine human drivers, two of which were professionals, performed two driving scenarios (drive cycle and car-following). The driver model was fitted to each driver individually to mathematically express the differences in their styles. The simulated RMS pedal forces from the fitted driver models lay within 20% of the measured simulator values. The driver model was also extended to include the interactions between a human driver and an AAP using mathematical game theory. Three frameworks were proposed: decentralised, cooperative and one-sided cooperative, but, as the cooperative framework would have been very difficult to implement experimentally, it was only considered theoretically. The same nine human drivers were presented with drive cycle and car-following scenarios whilst being assisted by pedal feedback to validate the model. Both decentralised and one-sided cooperative frameworks were applied to the fitting and compared. In the drive cycle scenario, the one-sided cooperative framework output an identical controller to the decentralised framework. In the car-following scenario, the one-sided cooperative framework produced the best fit, suggesting that the human drivers adapted their strategy to reflect the guidance from the AAP. It was noted in both scenarios that the peak pedal displacement decreased by approximately 20% with the presence of pedal force feedback. Further work is suggested to improve the mass and road gradient data obtained from the data loggers in vehicles in order to reduce the uncertainty in the traction force and fuel rate maps. With a model for the interactions of a human driver with an AAP now in place, the pedal feedback strategy can now be optimised to improve the performance of the human driver.Centre for Sustainable Road Freight and EPSR

Kooperative Regelungskonzepte auf Basis der Spieltheorie und deren Anwendung auf Fahrerassistenzsysteme

Cooperative control loops in which human and a technical automation system perform a control task in close cooperation are investigated. A control framework is proposed which is based on a formal description of the cooperative control problem. The main idea of the control algorithm is to solve a differential game on a sliding horizon. The concept has been applied to design two cooperative advanced driver-assistance systems. One for the longitudinal driving task, one for the lateral driving task

Kooperative Regelungskonzepte auf Basis der Spieltheorie und deren Anwendung auf Fahrerassistenzsysteme

Diese Arbeit betrachtet Regelkreise, in denen die Regelaufgabe von Menschen und maschinellen Reglern gemeinsam ausgeführt wird. Für diese maschinellen Regler wird systematisch ein formalisiertes Regelkonzept abgeleitet. Ein wesentlicher Teil der Arbeit besteht in der Entwicklung von Algorithmen für die Implementierung. Als Anwendungsbeispiel werden zwei kooperative Fahrerassistenzsysteme vorgestellt. Am Fahrsimulator durchgeführte Studien zeigen eine deutliche Verbesserung des Fahrverhaltens aber auch des Kraftstoffverbrauchs