Parametric Trajectory Prediction of Surrounding Vehicles

Chang Mook Kang1, Soo Jung Jeon1, Seung-Hi Lee1, Chung Choo Chung1

  • 1Hanyang University

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Regular Paper

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10:48 - 12:36 | Tue 27 Jun | | TuBPl

Trajectory Planning for Autonomous Vehicles

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Abstract

For advanced driver assistance system (ADAS) which are related to risk assessment or collision avoidance, predicting object vehicle's path is needed beyond the precise and reliable sensor data to improve the performance of path prediction. This paper proposes an object vehicle path prediction method using parametric interpolation. To obtain a precise and reliable sensor data, multirate sensor data fusion was applied. After that, by using the parametric interpolation, we can predict the object vehicle's motion based on a fused relative vehicle motion data. The performance of the object vehicle path prediction method was validated via simulation and, experimental test with DELPHI 77Hz long range radar and Mobileye camera.

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