Exploiting multi-vehicle interactions to improve urban vehicle tracking

R. K. Prasanth; Dale Klamer; Pablo O. Arambel

doi:10.1117/12.850704

27 April 2010 Exploiting multi-vehicle interactions to improve urban vehicle tracking

R. K. Prasanth, Dale Klamer, Pablo O. Arambel

Proceedings Volume 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX; 76970A (2010) https://doi.org/10.1117/12.850704
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

The subject of traffic flow modeling began over fifty years ago when Lighthill and Whitham used flow continuity equation from fluid dynamics to describe traffic behavior. Since then, a multitude of models, broadly classified into macroscopic, mesoscopic, and microscopic models, has been developed. Macroscopic models describe the space-time evolution of aggregate quantities such as traffic flow density whereas microscopic models describe behavior of individual drivers/vehicles in the presence of other vehicles. In this paper, we consider tracking of vehicles using a specific microscopic model known as the intelligent driver model (IDM). As in other microscopic models, the IDM equations of motion of a vehicle are nonlinearly coupled to those of neighboring vehicles, with the magnitudes of coupling terms becoming larger as vehicles get closer and smaller as vehicles get farther apart. In our approach, the state of weakly coupled groups of vehicles is represented by separated probability distributions. When the vehicles move closer to each other, the state is represented by a joint probability distribution that takes into account the interaction among vehicles. We use a sum of Gaussians approach to represent the underlying interaction structure for state estimation and reduce computational complexity. In this paper we describe our approach and illustrate the approach with simulated examples.

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R. K. Prasanth, Dale Klamer, and Pablo O. Arambel "Exploiting multi-vehicle interactions to improve urban vehicle tracking", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970A (27 April 2010); https://doi.org/10.1117/12.850704

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KEYWORDS

Motion models

Computer simulations

Detection and tracking algorithms

Roads

Sensors

Target detection

Line width roughness

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