Pages The View Glue. Architecting Dynamic Reconfiguration in Dependable Systems. Gomes, Thais V. Batista, Ackbar Joolia, Geoff Coulson. Business Process Monitoring for Dependability. Patrick J. Table 1 derived from [ 42 ] Chapter 5 presents an overview of the common methods used for the information to arrive at decisions.
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Weight values by inverse of their errors and sum to derive score function. Confirm information based on m-out-of-n sources that agree. Apply Bayes rule to combine independent conditional probabilities. Hard decisions constitute the fusion methods when they result in a single optimal choice. The soft decisions are the result of methods where there may be more than one decision but each decision will have an uncertainty associated with it. Based on the requirements, different information fusion methods can be used in different parts of the automated driving pipeline.
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Boolean information fusion method is the easiest to understand and implement. If one pipeline reports an object and other does not, a Boolean OR decision will pass its existence to the next level. On the other hand, a Boolean AND will not report it. A combination of such operators based on multiple data points generated from one step in a pipeline with another pipeline can result in a simple yet powerful decision. If multiple sources result in information in same units, this method can be used to combine and arrive at one concrete answer.
Each information can also be assigned some weight based on some past knowledge like error, past observations etc. M-of-N is a standard voting method where majority value is considered as the final decision. This methodology has been used in designing fault tolerant systems [ 52 ]. These methods can also be combined together with other to arrive at more powerful methods. For example, weights may be added before voting. Bayesian decision making is one of the most common soft decision making processes.
The commonly seen Kalman Filters [ 73 ] and Particle Filters [ 4 ] use Bayesian decision making to arrive at final decisions with covariances matrices. The matrices represent the uncertainty of the arrived final decision. Whereas the Bayesian theory requires probabilities for each information of interest, belief functions allow us to form degrees of belief for one information based on probabilities for a related information. Where Bayesian decision making relies on degree of agreement , Demspter-Shafer method tries to measure absence of conflict. The output is the decision with a belief associated with it.
Fuzzy logic system is a nonlinear mapping of an input information vector into a scalar output.
Fuzzy set theory and fuzzy logic establish the specifics of the nonlinear mapping [ 50 ]. Results from this non-linear mapping can be used along with Boolean operators to make decisions.
Lecture Notes in Computer Science: Architecting Dependable Systems III 3549 (2005, Paperback)
Such rules which facilitate the fusion are called fuzzy rules. The result then is defuzzied to arrive at the final output. The above methods by no means represent an exhaustive list of information fusion. In fact more than one of the above approaches can be combined to result in Hybrid decisions. For example, Bayesian approaches like Kalman Filter can be used to arrive at some information along with the required covariance matrix in multiple parallel pipelines.
This matrix can be inverted and summed to derive appropriate weights for each of the pipeline process. For the next step a weighted sum score can be used to make the final decision. Here we converted the soft decision to the hard decision.
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Additionally, this implies that the weights of contribution of each pipeline may change over time. Monitors observe and report the abnormal outputs that deviate from the correct behavior by either observing the states or by analyzing finite history traces.
As detailed below, our categorization is based on whether a functional specification or an indirect correctness specification is used in the monitor. For vision-based perception components, it is very hard to create correctness specification from first principles.
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Therefore, the labelled data is commonly considered the specification and neural networks are used for implementation. Monitoring computed values of a neural network. Add co-authors Co-authors. Upload PDF. Follow this author. New articles by this author.
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