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Latest revision as of 15:29, 21 January 2021
Abstract
International audience; In the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is necessary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the dynamic objects in the scene. Then the risk is estimated as a Time To Collision (TTC) by projecting the object's trajectory in the future. In this paper, we propose a new grid-based approach for collision risk prediction, based on the Hybrid-Sampling Bayesian Occupancy Filter framework. The idea is to compute an estimation of the TTC for each cell of the grid, instead of reasoning on objects. This strategy avoids to solve the difficult problem of multi-objects detection and tracking and provides a probabilistic estimation of the risk associated to each TTC value. After promising initial results, we propose in this paper to evaluate the relevance of the method for real on-road applications, by using a real-time implementation of our method in an experimental vehicle.
Original document
The different versions of the original document can be found in:
- http://dx.doi.org/10.1007/978-3-319-23778-7_54 under the license http://www.springer.com/tdm
- https://link.springer.com/chapter/10.1007/978-3-319-23778-7_54,
- https://hal.inria.fr/hal-01011808,
- https://dblp.uni-trier.de/db/conf/iser/iser2014.html#RummelhardNPL14,
- https://core.ac.uk/display/102571466,
- https://academic.microsoft.com/#/detail/2203271201
DOIS: 10.13140/2.1.3953.4724 10.1007/978-3-319-23778-7_54
Document information
Published on 01/01/2014
Volume 2014, 2014
DOI: 10.13140/2.1.3953.4724
Licence: Other
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