Paper Info
Title
Reliable automotive pre-crash system with out-of-sequence measurement processing
Abstract
In an automotive pre-crash application, it is vital to quickly and accurately estimate the position and velocity of objects in the frontal area of the vehicle. To improve such estimations, several radar sensors are fused to detect objects. Due to their different performance characteristics, their measurements can arrive at the pre-crash processing unit out-of-sequence. This work presents several techniques to integrate measurements into a tracking algorithm that arrive with such an out-of-sequence measurement (OOSM) scenario. A comprehensive complexity analysis of the algorithms is also presented. Most importantly, the algorithms are run on a test vehicle during real crash scenarios. The algorithms' performance is evaluated against reference data from a highly accurate laser scanner. It is shown that using advanced OOSM algorithms in pre-crash systems significantly increases performance and reduces computational cost compared to previous approaches.
Year
DOI
Venue
2010
10.1109/IVS.2010.5548149
Intelligent Vehicles Symposium
Keywords
Field
DocType
out-of-sequence measurement processing,radar sensors,road vehicle radar,velocity estimation,position estimation,object detection,automotive engineering,sensors,vehicle dynamics,radar imaging,complexity analysis,automotive pre-crash system,laser scanner,reference data,algorithm design and analysis,covariance matrix,radar tracking,laser radar,prediction algorithms,sensor fusion,kalman filters
Radar,Reference data (financial markets),Object detection,Crash,Radar imaging,Simulation,Kalman filter,Vehicle dynamics,Engineering,Automotive industry
Conference
ISSN
ISBN
Citations 
1931-0587
978-1-4244-7866-8
13
PageRank 
References 
Authors
0.93
1
7
Name
Order
Citations
PageRank
Marc M. Muntzinger1202.86
Michael Aeberhard21239.61
Sebastian Zuther3130.93
Mirko Mählisch4646.66
Matthias Schmid5162.02
Jürgen Dickmann68314.07
Klaus Dietmayer7822102.64