Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 65, Issue 7, Pages 5999-6008Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2017.2782236
Keywords
Automated vehicles (AVs) and advanced driver-assistance systems; interactive multiple models (IMMs); physics-based and maneuver-based approaches; vehicle trajectory prediction
Categories
Funding
- National Natural Science Foundation of China [51475254, 51625503]
- Junior Fellowships for Advanced Innovation Think-tank Program of China Association for Science and Technology [DXB-ZKQN-2017-035]
- China Postdoctoral Science Foundation [2017M620765]
- China Scholarship Council
- Tsinghua University
- Daimler
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Vehicle trajectory prediction helps automated vehicles and advanced driver-assistance systems have a better understanding of traffic environment and perform tasks such as criticality assessment in advance. In this study, an integrated vehicle trajectory prediction method is proposed by combining physics-and maneuver-based approaches. These two methods were combined for the reason that the physics-based trajectory prediction method could ensure accuracy in the short term with the consideration of vehicle running dynamic parameters, and the maneuver-based prediction approach has a long-term insight into future trajectories with maneuver estimation. In this study, the interactive multiple model trajectory prediction (IMMTP) method is proposed by combining the two predicting models. The probability of each model in the interactive multiple models could recursively adjust according to the predicting variance of each model. In addition, prediction uncertainty is considered by employing unscented Kalman filters in the physics-based prediction model. To the maneuver-based method, random elements for uncertainty are introduced to the trajectory of each maneuver inferred by using the dynamic Bayesian network. The approach is applied and analyzed in the lane-changing scenario by using naturalistic driving data. Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon.
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