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Modern cars feature a variety of different driving assistance systems, which aim to improve driving comfort and safety as well as fuel consumption. Due to the technical advances and the possibility to consider vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, cooperative adaptive cruise control (CACC) strategies have received significant attention from both research and industrial communities. The performance of such systems can be enhanced if the future velocity of the surrounding traffic can be predicted. Generally, human driving behavior is a complex process and influenced by several environmental impacts. In this work a stochastic model of the velocity of a preceding vehicle based on the incorporation of available information sources such as V2I, V2V and radar information is presented. The main influences on the velocity prediction considered in this approach are current and previous velocity measurements and traffic light signals.

Virtual testing of advanced driver assistance systems (ADAS) using a simulation environment provides great potential in reducing real world testing and therefore currently much effort is spent on the development of such tools. This work proposes a simulation and hardware-in-the-loop (HIL) framework, which helps to create a virtual test environment for ADAS based on real world test drive. The idea is to reproduce environmental conditions obtained on a test drive within a simulation environment. For this purpose, a production standard BMW 320d is equipped with a radar sensor to capture surrounding traffic objects and used as vehicle for test drives. Post processing of recorded GPS raw data from the navigation system using an open source map service and the radar data allows an exact reproduction of the driven road including other traffic participants.

It is well known that driver behavior can affect fuel consumption to a large extent hence modifying it can lead to reasonable reduction in the magnitude of 10 to 20%. However, it is also known that effects of training are short lived and therefore many authors and companies suggest the use of monitoring systems, sometimes called eco-driver, which allow recognizing opportunities for reduction. V2V is an emerging technology which has been widely studied especially for safety applications. In terms of fuel consumption, there has also been a significant effort for methods directed to coordinate the movement of vehicles, especially of trucks, to improve fuel consumption by platooning [1].

This paper shows the potential of a multicalibration approach for reducing fuel consumption while keeping pollutant immissions. The paper demonstrates that the current engine control approach with a single fixed calibration involves important fuel penalties in areas with low vehicle densities where local pollution is not an issue, while the NOx emissions in urban areas are usually too high to fulfill air quality standards. The proposed strategy is based on using information about the vehicle location and the NOx concentrations in the ambient to choose a suitable calibration amongst a set of possibilities. To assess the potential of such a strategy experimental tests have been done with a state-of-art turbocharged Diesel engine. First, a design of experiments is used to obtain three different calibrations.

The emissions of modern Diesel engines, which are known to have various health effects, are beside the drivers torque demands and low fuel consumptions one of the most challenging issues for combustion and after treatment control. To comply with legal requirements, emission control for heavy duty engines is not feasible without additional hardware, usually consisting of a Diesel oxidation catalyst (DOC), a Diesel particulate filter (DPF) and a selective catalytic reduction (SCR) system. In contrast to other NOx reduction systems, e.g. lean NOx traps, the SCR system requires an additional ingredient, namely ammonia (NH3), to reduce the NOx emissions to non harmful components. Consequently, the correct amount of NH3 dosing in the SCR catalyst is one of the critical components to reach high conversion rates and avoid ammonia slip.