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Collision Risk Prediction and Warning at Road Intersections Using an Object Oriented Bayesian Network

  • This paper describes a novel approach to situation analysis at intersections using object-oriented Bayesian networks. The Bayesian network infers the collision probability for all vehicles approaching the intersection, while taking into account traffic rules, the digital street map, and the sensors' uncertainties. The environment perception is fused from communicated data, vehicles local perception and self-localization. Thus, a cooperatively validated set of data is obtained to characterize all objects involved in a situation (resolving occlusions). The system is tested with data, acquired by vehicles with heterogenic equipment (without/with perception). In a first step the probabilistic mapping of a vehicle onto a fixed set of traffic lanes and forward motion predictions is introduced. Second, criticality measures are evaluated for these motion predictions to infer the collision probability. In our test vehicle this probability is then used to warn the driver of a possible hazardous situation. It serves as a likelihood alarm parameter for deciding the intensity of HMI acoustic signals to direct the driver's attention. First results in various simulated and live real-time scenarios show, that a collision can be predicted up to two seconds before a possible impact by applying the developed Bayesian network. The extension of this network to further situation features is the content of ongoing research.

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Author:Galia WeidlORCiD, Virat Singhal, Dominik Petrich, Dietmar Kaspar, Andreas Wedel, Gabi Breuel
URL:https://www.researchgate.net/publication/267764514_Collision_Risk_Prediction_and_Warning_at_Road_Intersections_Using_an_Object_Oriented_Bayesian_Network#fullTextFileContent
DOI:https://doi.org/10.1145/2516540.2516577
Parent Title (English):5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013
Document Type:Conference Proceeding
Language:English
Year of Completion:2013
Release Date:2023/12/07
Tag:Artificial Intelligence; ITS (Intelligent Transportation Systems)
GND Keyword:Fahrerassistenzsystem; Künstliche Intelligenz
Urheberrecht:1
Institutes:Einrichtungen / Kompetenzzentrum Künstliche Intelligenz
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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