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With the inception of model-based design and automatic code generation, many organizations are developing controls and diagnostics algorithms in model-based development tools to meet customer and regulatory requirements. Advances in model-based design have made it easier to generate C code from models and help software engineers streamline their workflow. Typically, after the software has been developed, the models are handed over to a calibration team responsible for calibrating the features to meet specified customer and regulatory requirements. However, once the models are handed over to the calibration team, the calibration engineers are unaware of how to calibrate the features because documentation is not available. Typically, model documentation trails behind the software process because it is created manually, most of this time is spent on formatting. As a result, lack of model documentation or up-to date documentation causes a lot of pain for OEM’s and Tier 1 suppliers.

In a CAS system, the distance and relative velocity between front and host vehicles are estimated to calculate time-to-collision (TTC). The distance estimates by different methods will certainly include noise which should be removed to ensure the accuracy of TTC calculations. Kalman filter is a good tool to filter such type of noise. Nevertheless, Kalman filter is a model based filter, which means a correct model is important to get the good filtering results. Usually, a vehicle is either moving with a constant velocity (CV) or constant acceleration (CA) maneuvers. This means the distance data between front and host vehicles can be described by either constant velocity or constant acceleration model. In this paper, first, CV and CA models are used to design two Kalman filters and an interacting multiple model (IMM) is used to dynamically combine the outputs from two filters.

The paper describes a new strategy for real-time sensor diagnostics that is based on the statistical correlation of various sensor signal pairs. During normal fault-free operation there is a certain correlation between the sensor signals which is lost in the event of a fault. The proposed algorithm quantifies the correlation between sensor signal pairs using real-time scalar metrics based on the Mahalanobis-distance concept. During normal operation all metrics follow a similar pattern, however in the event of a fault; metrics involving the faulty sensor would increase in proportion to the magnitude of the fault. Thus, by monitoring this pattern and using a suitable fault-signature table it is possible to isolate the faulty sensor in real-time. Preliminary simulation results suggest that the strategy can mitigate the false-alarms experienced by most model-based diagnostic algorithms due to an intrinsic ability to distinguish nonlinear vehicle behavior from actual sensor faults.

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.

As automotive electronic control systems continue to increase in usage and complexity, the challenges for developing automotive diagnostics also increase. Reduced development cycle times, the increased significance of diagnostics for safety critical systems, and the integration of vehicle systems across multiple control systems all add to the tasks of developing diagnostics for the automobiles of today and tomorrow. Addressing automotive diagnostics now requires the Tier 1 supplier to utilize a formal diagnostic development methodology. There are also opportunities for Tier 1 suppliers to add value by developing vehicle-level supervisory diagnostic strategies, in addition to subsystem and system-level diagnostic strategies. There is also a prospect to provide strategies and tools to enhance service at the vehicle level. This paper proposes an approach for Tier 1 suppliers to address diagnostic and service issues at the component, system, and vehicle level.