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Motion-prediction algorithms for vehicles often employ historical behavior of a vehicle, rely on the Markov property of the underlying system, and predict the future behavior of the vehicle. However, the Markov property alone may lead to conservative predictions and heavy computational burden. To overcome these drawbacks, this paper develops a method that uses the notion of similarity among vehicle...
Many driver models assume that a human driver can be modeled as a linear time-invariant system. Although during specific execution tasks this can be a reasonably good model, in general, this is an unrealistic and quite restrictive assumption for most real-life situations where more complex cognitive functions need to be evoked, such as long-term, deliberative planning, prioritization among several...
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