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Increasing electronics content, growing computing power, and proliferation of opportunities for information connectivity (through improved sensors, GPS, road and traffic information systems, wireless internet access, vehicle-to-vehicle communication, etc.) are technology trends which can significantly transform and impact future automotive vehicle's control and diagnostic strategies. One aspect of the increasing vehicle connectivity is access to ambient and road condition information, such as ambient temperature, ambient pressure, humidity, % cloudiness, visibility, cloud ceiling, precipitation, rain droplet size, wind speed, and wind direction based on wireless internet access. The paper discusses the potential opportunities made available through wireless communication between the vehicle and the internet.

New vehicle control algorithms are needed to meet future emissions and fuel economy mandates that are quite likely to require a measurement of ambient specific humidity (SH). Current practice is to obtain the SH by measurement of relative humidity (RH), temperature and barometric pressure with physical sensors, and then to estimate the SH using a fit equation. In this paper a novel approach is described: a system of neural networks trained to estimate the SH using data that already exists on the vehicle bus. The neural network system, which is referred to as a virtual SH sensor, incorporates information from the global navigation satellite system such as longitude, latitude, time and date, and from the vehicle climate control system such as temperature and barometric pressure, and outputs an estimate of SH. The conclusion of this preliminary study is that neural networks have the potential of being used as a virtual sensor for estimating ambient and intake manifold's SH.