Vehicle Detection and Recognition for Autonomous Intelligent Cruise Control
Matthews, N.D., An, P.E. and Harris, C.J. (1995) Vehicle Detection and Recognition for Autonomous Intelligent Cruise Control.
Full text not available from this repository.
The introduction of new technologies such as autonomous intelligent cruise control or collision avoidance schemes to road vehicles necessitates a high degree of robustness and reliability. Whilst very accurate range estimates may be recovered using conventional sensors, e.g. millimetric radar, these typically suffer from both low bearing resolution and potential ambiguities through, for example, false alarms. This work details a novel two-stage vehicle detection and recognition algorithm which combines an image processing area of interest (AOI) designator to cue a secondary recognition process implemented using principal component analysis (PCA) as input to a Multi-Layered Perceptron (MLP) classifier. The combination of an initial detection phase, followed by a recognition process has allowed the classifier design to be greatly simplified. In turn the classifier performance has allowed some of the image processing assumptions to be relaxed, whilst maintaining a high signal to noise ratio (SNR). Both the image processing system and MLP classifier have been designed for real-time implementation and data-fusion with other information sources such as a range/range rate radar.
|Item Type:||Monograph (Technical Report)|
|Additional Information:||1995/6 Research Journal Address: Department of Electronics and Computer Science|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||04 May 1999|
|Last Modified:||27 Mar 2014 19:50|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)