Ground Target Tracking with Multi-Lane Constraint

Knowledge of the lane that a target is located in is of particular interest in on-road surveillance and target tracking systems. We formulate the problem and propose two approaches for on-road target estimation with lane tracking. The first approach for lane tracking is lane identification based ona Hidden Markov Model (HMM) framework. Two identifiers are developed according to different optimality goals of identification, i.e., the optimality for the whole lane sequence and the optimality of the current lane where the target is given the whole observation sequence. The second approach is on-road target tracking with lane estimation. We propose a 2D road representation which additionally allows to model the lateral motion of the target. For fusion of the radar and image sensor based measurement data we develop three, IMM-based, estimators that use different fusion schemes: centralized, distributed, and sequential. Simulation results show that the proposed two methods have new capabilities and achieve improved estimation accuracy for on-road target tracking.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Civil and Environmental Engineering

Year

2009

Author dc:creator

• Chen, Yangsheng

Contributors dc:contributor

• Jilkov, Vesselin
• Li, X. Rong
• Chen, Huimin

• Ground Target Tracking
• HMM
• Lane Identifier
• 2D Road Coordinates
• IMM
• Lane Estimator
• Sensor Data Fusion

Repository record dc:identifier

https://scholarworks.uno.edu/td/925

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1906