Probability hypothesis density tracking for interacting vehicles in traffic

R. K. Prasanth; H. Hoang

doi:10.1117/12.919544

17 May 2012 Probability hypothesis density tracking for interacting vehicles in traffic

R. K. Prasanth, H. Hoang

Proceedings Volume 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI; 83920J (2012) https://doi.org/10.1117/12.919544
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Recent work has shown that the random finite set (RFS) approach to multi-target tracking is a computationally viable alternative to the traditional data association based approaches. An assumption in these approaches is that the targets move independently of each other. In this paper, we introduce the concept of a random finite graph and study its application to the tracking of interacting vehicles in traffic. A random finite graph is a random variable taking values in the set of finite directed graphs. The graph describes the influence of vehicles on the motion of other vehicles. The connected components of the graph define groups of vehicles that move independently of other groups. We treat the connected components as the independent entities upon which to perform RFS-based tracking. The approach is illustrated with an arterial traffic simulation in which vehicles interact among themselves through car-following and with traffic control devices at intersections.

Citation Download Citation

R. K. Prasanth and H. Hoang "Probability hypothesis density tracking for interacting vehicles in traffic", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 83920J (17 May 2012); https://doi.org/10.1117/12.919544

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available