An intersection safety system should adapt to the particular characteristics that identify an intersection, by mining traffic and collision data. Given the large amount of sensor data that are obtained for intersections and from sensor-equipped cars, analysis and learning of such data is essential. This paper presents a new method to improve safety at intersections using a combination of a mathematical based collision detection algorithm and data mining. A number of scenarios at a simulated intersection are explored with encouraging results from our data mining implementation. The results suggest that our approach can help improve situation awareness and automate understanding of intersections, which, in turn, can be used to increase safety at intersections.
@inproceedings{DBLP:conf/aina/SalimLRK07,
author = {Flora Dilys Salim and
Seng Wai Loke and
Andry Rakotonirainy and
Shonali Krishnaswamy},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/aina/SalimLRK07.bib},
booktitle = {21st International Conference on Advanced Information Networking and
Applications (AINA 2007), Workshops Proceedings, Volume 2, May 21-23,
2007, Niagara Falls, Canada},
doi = {10.1109/AINAW.2007.360},
pages = {530--535},
publisher = {IEEE Computer Society},
timestamp = {Mon, 16 Sep 2019 01:00:00 +0200},
title = {U&I Aware: A Framework Using Data Mining and Collision Detection
to Increase Awareness for Intersection Users},
url = {https://doi.org/10.1109/AINAW.2007.360},
year = {2007}
}
© 2021 Flora Salim - CRUISE Research Group.