Two-string fuzzy inference consists of two separate inference mechanisms: One conventional fuzzy inference system that processes recommending rules, as well as a mechanism for processing negative rules, which prevent ...
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Two-string fuzzy inference consists of two separate inference mechanisms: One conventional fuzzy inference system that processes recommending rules, as well as a mechanism for processing negative rules, which prevent the system from outputting their associated values when their premise is fulfilled. Two-string inference has valuable applications in pattern recognition and control tasks. We present a method rendering two-string inference applicable and computationally feasible in recurrent fuzzy systems, i.e. Mamdami-type fuzzy inference systems equipped with defuzzified state feedback. We show the efficiency of our approach by means of an illustrative example from biological systems modelling and suggest application areas for recurrent two-string fuzzy systems.
We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. We first infer a distribution of high-level, abstr...
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We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. We first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the immanent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) - a generalization of the common Time-To-Collision (TTC) in arbitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.
Two-string inference systems produce output values from activations of recommending rules, as in standard fuzzy inference systems, but also with respect to inhibitions or warnings produced by negative rules. Besides a...
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ISBN:
(纸本)9781479938414
Two-string inference systems produce output values from activations of recommending rules, as in standard fuzzy inference systems, but also with respect to inhibitions or warnings produced by negative rules. Besides assuring that a forbidden output value must not occur, the crucial point with two-string inference is how the inference results from positive and negative rules can be combined to a single output membership function by which an output value can be calculated. In this paper, a new method for combining the output membership functions of both inference strings to a combined output membership function by means of fuzzy rule-based metainference is proposed. This method generalizes existing methods and additionally provides means to adjust or combine them in a transparent way. Moreover, the possibility of designing new context-dependent or -independent metainference patterns increases flexibility and applicability of two-string fuzzy inference.
This paper addresses the problem of risk assessment in dynamic traffic environments for future behavior evaluation and *** assessment has to be driven from behavioral needs of the acting scene entity to evaluate the b...
This paper addresses the problem of risk assessment in dynamic traffic environments for future behavior evaluation and *** assessment has to be driven from behavioral needs of the acting scene entity to evaluate the best possible future *** present a general approach for a temporally continuous future risk *** this risk estimation,we introduce predictive risk maps based on the variation of the acting entities’ possible *** predictive risk maps indicate how critical a certain behavior will be in the future and we use them for future behavior evaluation and *** introducing risk indicators for the collision case of moving entities we show the generality of our approach by applying it to several different traffic scene types.
This paper deals with the output synchronization problem for linear heterogeneous multi-agent systems. It is shown that in cycle-free communication networks, synchronization can be ensured by static state feedback law...
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This paper deals with the output synchronization problem for linear heterogeneous multi-agent systems. It is shown that in cycle-free communication networks, synchronization can be ensured by static state feedback laws under mild assumptions. Moreover, we present an observer-based strategy for the case that the agents have only access to relative output differences. While in general additional controller states must be communicated to synchronize the agents by observer-based methods, we prove that in cycle-free networks, only output differences are needed to achieve an observer-based synchronization.
We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in *** first infer a distribution of high-level,abstract drivi...
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We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in *** first infer a distribution of high-level,abstract driving maneuvers such as lane changes,turns,road followings,*** all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic *** is followed by maneuver-based,long-term trajectory predictions,which themselves contain random components due to the immanent uncertainty of how drivers execute specific *** all uncertain predictions of all maneuvers of every vehicle into account,the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction *** serves as the basis for calculating a novel criticality measure,the Time-To-Critical-Collision-Probability(TTCCP) –a generalization of the common Time-To-Collision(TTC) in arbitrary,uncertain,multi-object driving environments and valid for longer prediction *** system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.
This paper considers the output synchronization problem for linear heterogeneous SISO agents. The goal is not only to synchronize the agents to a given trajectory, but also to achieve this goal by distributed controll...
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ISBN:
(纸本)9781467360890
This paper considers the output synchronization problem for linear heterogeneous SISO agents. The goal is not only to synchronize the agents to a given trajectory, but also to achieve this goal by distributed controllers of low order dynamics. We prove that under mild assumptions it is possible to synchronize the agents and to limit the order of the controllers to an upper bound. Moreover, a special condition is derived under which it is even possible to synchronize the agents by means of static controllers. The efficiency of the approach is illustrated by a numerical example.
This paper addresses the problem of risk assessment in dynamic traffic environments for future behavior evaluation and planning. Risk assessment has to be driven from behavioral needs of the acting scene entity to eva...
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This paper addresses the problem of risk assessment in dynamic traffic environments for future behavior evaluation and planning. Risk assessment has to be driven from behavioral needs of the acting scene entity to evaluate the best possible future behavior. We present a general approach for a temporally continuous future risk estimation. Using this risk estimation, we introduce predictive risk maps based on the variation of the acting entities' possible dynamics. The predictive risk maps indicate how critical a certain behavior will be in the future and we use them for future behavior evaluation and planning. Explicitly introducing risk indicators for the collision case of moving entities we show the generality of our approach by applying it to several different traffic scene types.
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