Probabilistic Grid-based Collision Risk Prediction for Driving Application

Lukas Rummelhard 1, * Amaury Nègre 1, 2 Mathias Perrollaz 1 Christian Laugier 1
* Corresponding author
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
2 PRIMA - Perception, recognition and integration for observation of activity
Inria Grenoble - Rhône-Alpes, UJF - Université Joseph Fourier - Grenoble 1, INPG - Institut National Polytechnique de Grenoble , CNRS - Centre National de la Recherche Scientifique : UMR5217
Abstract : 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.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [11 references]  Display  Hide  Download

https://hal.inria.fr/hal-01011808
Contributor : Lukas Rummelhard <>
Submitted on : Wednesday, July 9, 2014 - 7:00:09 AM
Last modification on : Friday, January 4, 2019 - 1:23:34 AM
Document(s) archivé(s) le : Thursday, October 9, 2014 - 10:36:39 AM

File

ISER2014.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01011808, version 1

Collections

Citation

Lukas Rummelhard, Amaury Nègre, Mathias Perrollaz, Christian Laugier. Probabilistic Grid-based Collision Risk Prediction for Driving Application. ISER, Jun 2014, Marrakech/Essaouira, Morocco. ⟨hal-01011808⟩

Share

Metrics

Record views

891

Files downloads

1229