Multilane-road target tracking using radar and image sensors


Tracking of a maneuvering target moving on a multilane road using radar and image-sensor-based mostly measurements is studied. A completely unique 2-D road illustration of an on-road moving target is introduced. A natural description of the target longitudinal and lateral maneuvering behavior in the 2-D road coordinates is given using multiple models. An improved mean-adaptive acceleration model is used to explain the longitudinal maneuver modes of the motion. 3 estimators based on IMM are developed that use completely different schemes for fusion of radar and image-sensor-primarily based measurements: centralized, distributed, and sequential. Simulation results are presented that illustrate the performance of the proposed estimators and demonstrate their improved capability compared with a known 1-D road coordinate (mileage) estimator. Furthermore, a hidden Markov model (HMM) formulation and two algorithms for lane-solely tracking using lane observations are proposed. A lane observation model springs from a basic image sensor providing raw observation knowledge. Simulation results show that the proposed HMM-based lane estimators will achieve good performance for lane tracking when solely basic image-sensor-primarily based measurements are obtainable.

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