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Vehicle Trajectory Prediction Based on Motion Model and Maneuver Model Fusion with Interactive Multiple Models
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance. In this paper, a vehicle trajectory prediction method based on motion model and maneuver model fusion with Interactive Multiple Model (IMM) is proposed. Firstly, Constant Turn Rate and Acceleration (CTRA) motion model and Unscented Kalman Filter (UKF) are used to predict vehicle trajectory with uncertainty in the future. Then, vehicle trajectory prediction based on simplified maneuver recognition model is conducted, using temporal and spatial relationship between vehicle historical trajectory and lane lines. After that, vehicle trajectory prediction by integrating motion model and maneuver model with IMM is conducted. Finally, the proposed method is compared with CTRA motion model based vehicle trajectory prediction and lane keeping model (LKM) based vehicle trajectory prediction in two simulation test scenarios. The simulation results indicates that the IMM-based method achieves both excellent prediction accuracy and appropriate prediction uncertainty in the whole prediction horizon. This research can be used to support decision making for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems and leads to improvement of traffic safety.
CitationXiao, W., Zhang, L., and Meng, D., "Vehicle Trajectory Prediction Based on Motion Model and Maneuver Model Fusion with Interactive Multiple Models," SAE Technical Paper 2020-01-0112, 2020, https://doi.org/10.4271/2020-01-0112.
Data Sets - Support Documents
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- Lefvère, S., Vasquez, D., and Laugier, C. , “A Survey on Motion Prediction and Risk, Assessment for Intelligent Vehicles,” ROBOMECH Journal 1(1):1-14, 2014, doi:10.1186/s40648-014-0001-z.
- Schubert, R., Richter, E., and Wanielik, G. , “Comparison and Evaluation of Advanced Motion Models for Vehicle Tracking,” in 2008 11th International Conference on Information Fusion, Cologne, Germany, 30 June-3 July, 2008.
- Gindele, T., Brechtel, S., and Dillmann, R. , “A Probabilistic Model for Estimating Driver Behaviors and Vehicle Trajectories in Traffic Environments,” in 2010 13th International IEEE Annual Conference on Intelligent Transportation Systems, Madeira Island, Portugal, Sep. 19-22, 2010.
- Kumar, P., Perrollaz, M., Lefèvrem, S. et al. , “Learning-based Approach for Online Lane Change Intention Prediction,” in 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, Australia, June 23-26, 2013.
- Ortiz, M.G., Fritsch, J., Kummert, F. et al. , “Behavior Prediction at Multiple Time-scales in Inner-City Scenarios,” in 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, June 5-9, 2011.
- Morris, B., Doshi, A., and Trivedi, M. , “Lane Change Intent Prediction for Driver Assistance: On-Road Design and Evaluation,” in 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, June 5-9, 2011.
- Stéphanie Lefèvre, S., Gao, Y.Q., Vasquez, D. et al. , “Lane Keeping Assistance with Learning-Based Driver Model and Model Predictive Control,” in 2014 International Symposium on Advanced Vehicle Control, Tokyo, Japan, Sep. 22-26, 2014.
- Polychronopoulos, A., Tsogas, M., Amditis, A.J. et al. , “Sensor Fusion for Predicting Vehicles’ Path for Collision Avoidance Systems,” IEEE Transactions on Intelligent Transportation Systems 8(3):549-562, 2007, doi:10.1109/TITS.2007.903439.
- Houenou, A., Bonnifait, P., Cherfaoui, V. et al. , “Vehicle Trajectory Prediction based on Motion Model and Maneuver Recognition,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, Nov. 3-7, 2013.
- Kim, B. and Yi, K. , “Probabilistic and Holistic Prediction of Vehicle States Using Sensor Fusion for Application to Integrated Vehicle Safety Systems,” IEEE Transactions on Intelligent Transportation Systems 15(5):2178-2190, 2014, doi:10.1109/TITS.2014.2312720.
- Quan, P., Yongmei, C., Yan, L. et al. , Multi-Source Information Fusion Theory and Its Application First Edition (Beijing, China: Tsinghua University Press, 2013), 41, 57-44, 59. ISBN:978-7-302-30127-1.
- Hermes, C., Wöhler, C., Schenk, K. et al , “Long-Term Vehicle Motion Prediction,” in IEEE Intelligent Vehicles Symposium, Xi'an, China, June 3-5, 2009.
- Schreier, M., Willert, V., and Adamy, J. , “An Integrated Approach to Maneuver-Based Trajectory Prediction and Criticality Assessment in Arbitrary Road Environments,” IEEE Transactions on Intelligent Transportation Systems 17(10):2751-2766, 2016.