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The paper describes the development of a vehicle stability indicator based on the correlation between various current vehicle chassis sensors such as hand wheel angle, yaw rate and lateral acceleration. In general, there is a correlation between various pairs of sensor signals when the vehicle operation is linear and stable and a lack of correlation when the vehicle is becoming unstable or operating in a nonlinear region. The paper outlines one potential embodiment of the technology that makes use of the Mahalanobis distance metric to assess the degree of correlation among the sensor signals. With this approach a single scalar metric provides an accurate indication of vehicle stability.

Traffic engineers use time-of-day travel time databases to characterize normal travel times on roads. This information is used by traffic management centers together with information from sensors in the highway to identify problems and to make alternate route recommendations. In this paper, the travel time database concept is extended to a vehicle-to-vehicle communications network for traffic and safety information, wherein the travel time database is generated and stored by vehicles in the network, and used by the vehicles to identify abnormal traffic conditions. This infrastructure-free approach is attractive due to the potential to eliminate highway sensor and sensor maintenance costs, which are major factors that limit the growth of traffic information beyond major roadways in urban regions. Initial work indicates that database storage requirements in the vehicle should be manageable.

Advances in wireless communications, model-based diagnostics, human-machine interfaces, electronics and embedded system technologies have created the foundation for a dramatic shift in the way the vehicles are diagnosed and maintained. These advances will enable vehicle diagnosis and maintenance to be performed remotely while the vehicle is being driven. There also has been recent strong consumer interest in Remote Diagnosis and Maintenance (RD&M). As a consequence, RD&M is drawing increased attention in the automotive industry. This paper provides the current status of vehicle remote diagnosis and maintenance, analyses the potential features of RD&M and their significance, and discusses how next generation automotive products could benefit from research and development in this area.