Evaluation of Performance Enhancement for Crash Constellation Prediction via Car-to-Car Communication

Book chapter


Kuehbeck, Thomas, Hakobyan, Gor, Sikora, Axel, Chibelushi, Claude C. and Moniri, M. 2014. Evaluation of Performance Enhancement for Crash Constellation Prediction via Car-to-Car Communication. in: Communication Technologies for Vehicles Springer.
AuthorsKuehbeck, Thomas, Hakobyan, Gor, Sikora, Axel, Chibelushi, Claude C. and Moniri, M.
Abstract

Active safety systems for advanced driver assistance systems act within a complex, dynamic traffic environment featuring various sensor systems which detect the vehicles’ surroundings and interior. This paper describes the recent progress towards a performance evaluation of car-to-car communication (C2C) for active safety systems - in particular for crash constellation prediction. The methodology introduced in this work is designed to evaluate the impact of different sensors on the accuracy of a crash constellation prediction algorithm. The benefit of C2C communication (viewed as a virtual sensor) within a sensor data fusion architecture for pre-crash collision prediction is explored. Therefore, a simulation environment for accident scenarios analysis reproducing real-world sensor behaviour, is designed and implemented. Performance evaluation results show that C2C increases confidence in the estimated position of the oncoming vehicle. With C2C enhancement the given accuracy in time-to-collision (TTC) estimation is achievable about 110 ms earlier for moderate velocities at TTC range of [0.5s..0.2s]. The uncertainty in the vehicle position prediction at the time of collision can be reduced about half by integrating C2C communication into the sensor data fusion.

Book titleCommunication Technologies for Vehicles
Year2014
PublisherSpringer
Publication dates
Print2014
Publication process dates
Deposited22 Aug 2017
Series Lecture Notes in Computer Science
Event6th International Workshop on Communication Technologies for Vehicles
ISBN978-3-319-06644-8
ISSN0302-9743
Digital Object Identifier (DOI)doi:10.1007/978-3-319-06644-8_6
Web address (URL)https://doi.org/10.1007/978-3-319-06644-8_6
Journal citation8435, pp. 57-68
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