| Volume 25 Issue 1 - Publication Date: 1 January 2006 |
| Special Issue on the Fourth International Conference
on Field and Service Robotics, 2003 |
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| Bayesian Occupancy Filtering
for Multitarget Tracking: An Automotive Application |
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| C. Coué, C.
Pradalier, C. Laugier, T. Fraichard, and P. Bessière
INRIA, Rhône-Alpes and Gravir-CNRS |
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| Reliable and efficient perception
and reasoning in dynamic and densely cluttered environments are still major
challenges for driver assistance systems. Most of today’s systems
use target tracking algorithms based on object models. They work quite well
in simple environments such as freeways, where few potential obstacles have
to be considered. However, these approaches usually fail in more complex
environments featuring a large variety of potential obstacles, as is usually
the case in urban driving situations. In this paper, we propose a new approach
for robust perception and risk assessment in highly dynamic environments.
This approach is called Bayesian occupancy filtering; it basically combines
a four-dimensional occupancy grid representation of the obstacle state space
with Bayesian filtering techniques. |
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| Multimedia Key |
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 = Data |
= Code |
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Extension |
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Description |
1 |
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Example
1: Complete experimentation video for the scenario described in
section IV.B. The CyCab detect and track a temporarily occluded obstacle
using the BOF, while adjusting its speed to minimise collision risk.
(3.8 MB) |
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