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Controlling vehicle velocity, by coaching the driver to eco-drive with an advanced driver assistance system (ADAS), is a promising method to decrease fuel consumption and greenhouse gas emissions for combustion engine-driven road vehicles. By using optimal control techniques, such a system may find velocity profiles in real-time that minimize fuel consumption. This is particularly useful to recommend the optimal time to initiate coasting, which is otherwise difficult to estimate by a driver. However, this ADAS should not choose velocities and accelerations that the driver will dislike, such as those that leave too much or too little space to the preceding vehicle, or those that take corners at high speed. To remedy this, we introduce an optimal control model of acceleration that mimics drivers' behavior and combine this with a model of fuel consumption to trade-off driver preferences and fuel savings. We give examples of the velocity profiles recommended in a typical driving scenario to demonstrate the potential fuel savings. Finally, we give details of a prototype system, which has recently been implemented in the driving simulator at the University of Southampton.

Accurate understanding of driver behaviour is crucial for future Advanced Driver Assistance Systems (ADAS) and autonomous driving. For user acceptance it is important that ADAS respect individual driving styles and adapt accordingly. Using data collected during a naturalistic driving study carried out at the University of Southampton, we assess existing models of driver acceleration and speed choice during car following and when cornering. We observe that existing models of driver behaviour that specify a preferred inter-vehicle spacing in car-following situations appear to be too prescriptive, with a wide range of acceptable spacings visible in the naturalistic data. Bounds on lateral acceleration during cornering from the literature are visible in the data, but appear to be influenced by the minimum cornering radii specified in design codes for UK roadway geometry. This analysis of existing driver models is used to suggest a small set of parameters that are sufficient to characterise driver behaviour in car-following and curve driving, which may be estimated in real-time by an ADAS to adapt to changing driver behaviour. Finally, we discuss applications to adaptive ADAS with the objectives of improving road safety and promoting eco-driving, and suggest directions for future research.

Flight within degraded visual conditions is a great challenge to pilots of rotary-wing craft. Environmental cues typically used to guide interpretation of speed, location and approach can become obscured, forcing the pilots to rely on data available from in-cockpit instrumentation. To ease the task of flight during degraded visual conditions, pilots require easy access to flight critical information. The current study examined the effect of 'Highways in the Sky' symbology and a conformal virtual pad for landing presented using a Head Up Display (HUD) on pilots' workload and situation awareness for both clear and degraded conditions across a series of simulated rotary-wing approach and landings. Results suggest that access to the HUD lead to significant improvements to pilots' situation awareness, especially within degraded visual conditions. Importantly, access to the HUD facilitated pilot awareness in all conditions. Results are discussed in terms of future HUD development. Practitioner Summary: This paper explores the use of a novel Heads Up Display, to facilitate rotary-wing pilots' situation awareness and workload for simulated flights in both clear and degraded visual conditions. Results suggest that access to HUD facilitated pilots' situation awareness, especially when flying in degraded conditions.