A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems

Meyer-Delius, Daniel; Plagemann, Christian; Wichert, Georg von; Feiten, Wendelin; Lawitzky, Gisbert; Burgard, Wolfram

doi:10.1007/978-3-540-78246-9_32

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…The authors design a model for predicting vehicle maneuvers based on situation-specific motivations and test their model in a simulated highway environment. Another approach along the same lines is that in [2]. Here, a spatiotemporal situation model is proposed for inferring contextual information from semantical descriptions of the state of traffic.…”

Section: Related Workmentioning

confidence: 98%

A bayesian approach for driving behavior inference

Agamennoni

Nieto

Nebot

2011

2011 IEEE Intelligent Vehicles Symposium (IV)

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Human drivers are endowed with an inborn ability to put themselves in the position of other drivers and reason about their behaviors and intended actions. State-of-the-art driving assistance systems, on the other hand, are generally limited to physical models and ad-hoc safety rules. In order to drive safely amongst humans, autonomous vehicles require a high-level description of the state of traffic participants. This paper presents a probabilistic model for estimating and predicting the behavior of drivers immersed in traffic. The model is defined within a stochastic filtering framework and estimation and prediction are carried out with statistical inference techniques. The approach is validated with real data from a fleet of mining vehicles.

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Section: Related Workmentioning

confidence: 98%

A bayesian approach for driving behavior inference

Agamennoni

Nieto

Nebot

2011

2011 IEEE Intelligent Vehicles Symposium (IV)

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“…In addition, it is not clear if they can support partial states and integrate background knowledge while keeping good performance. A relational representation has been used in Meyer-Delius et al (2008) for situation characterization over time. However, this work is based on HMMs and uses only binary relations (true or false).…”

Section: Applicationsmentioning

confidence: 99%

Probabilistic logic programming for hybrid relational domains

2016

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We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.

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“…In addition, it is not clear if they can support partial states and integrate background knowledge while keeping good performance. A relational representation has been used in [25] for situation characterization over time. However, this work is based on HMMs and uses only binary relations (true or false).…”

Section: B Applicationsmentioning

confidence: 99%

A particle filter for hybrid relational domains

Nitti

Laet

Raedt

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We introduce a probabilistic language and a fast inference algorithm for state estimation in hybrid dynamic relational domains with an unknown number of objects. More specifically, we apply Particle Filters to distributional clauses. The particles represent (partial) interpretations of possible worlds (with discrete and/or continuous variables) and the filter recursively updates its beliefs about the current state. We use backward reasoning to determine which facts should be included in the partial interpretations. Experiments show that our framework can outperform the classical particle filter and is promising for robotics applications.

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A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems

Cited by 15 publications

References 11 publications

A bayesian approach for driving behavior inference

A bayesian approach for driving behavior inference

Probabilistic logic programming for hybrid relational domains

A particle filter for hybrid relational domains

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