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Collision Avoidance Warning Algorithm Based on Spatiotemporal Position Prediction of Vehicles at Intersections
- Baojian Han - Shanghai Institute of Technology, China ,
- Yu Zhang - Shanghai Institute of Technology, Department of Computer Science and Information Engineering, China ,
- Yunxiang Liu - Shanghai Institute of Technology, Department of Computer Science and Information Engineering, China ,
- Jianlin Zhu - Shanghai Institute of Technology, Department of Computer Science and Information Engineering, China
Journal Article
12-06-03-0019
ISSN: 2574-0741, e-ISSN: 2574-075X
Sector:
Topic:
Citation:
Han, B., Zhang, Y., Liu, Y., and Zhu, J., "Collision Avoidance Warning Algorithm Based on Spatiotemporal Position Prediction of Vehicles at Intersections," SAE Intl. J CAV 6(3):297-307, 2023, https://doi.org/10.4271/12-06-03-0019.
Language:
English
Abstract:
Aiming at the high false alarm rate of vehicle collision avoidance algorithms at
intersections controlled by traffic lights, a vehicle collision avoidance
warning algorithm based on vehicle spatiotemporal position prediction (SPPWA) is
proposed. The algorithm first obtains real-time data information such as the
heading angle and global positioning system (GPS) coordinates of the two
vehicles from the OnBoard Unit (OBU), and then the data is preprocessed by
different filtering methods, and then excludes the data information that the two
vehicles cannot collide. Finally, the filtered data is used to predict the
spatiotemporal position of the vehicle before the two vehicles reach the
collision point and determine whether the vehicle will collide. The algorithm is
verified in three vehicle crash scenarios through PreScan and Matlab/Simulink
co-simulation. The experimental results show that after the data are
preprocessed by Kalman filtering, the algorithm has the lowest false alarm rate
in the three scenarios. It can effectively improve the driving safety of
vehicles at intersections.