Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic
- Resource Type
- Periodical
- Authors
- Zhu, Xianglei; Hu, Wen; Deng, Zejian; Zhang, Jinwei; Hu, Fengqing; Zhou, Rui; Li, Keqiu; Wang, Fei-Yue
- Source
- IEEE/CAA Journal of Automatica Sinica IEEE/CAA J. Autom. Sinica Automatica Sinica, IEEE/CAA Journal of. 9(10):1752-1762 Oct, 2022
- Subject
- Computing and Processing
Communication, Networking and Broadcast Technologies
General Topics for Engineers
Robotics and Control Systems
Support vector machines
Training
Simulation
Predictive models
Data models
Trajectory
Behavioral sciences
Cut-in behavior
interaction-aware
mixed traffic
risk assessment
trajectory prediction
- Language
- ISSN
- 2329-9266
2329-9274
Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in. To improve the safety of autonomous vehicles in the mixed traffic, this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants. The integration of the support vector machine and Gaussian mixture model (SVM-GMM) is developed to simultaneously predict cut-in behavior and trajectory. The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance. Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles, two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function. Finally, the comparative analysis is performed to validate the proposed method using the naturalistic driving data. The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.