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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.

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.