The Interacting Multiple Model Smooth Variable Structure Filter for Trajectory Prediction

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The Interacting Multiple Model Smooth Variable Structure Filter for Trajectory Prediction

Akhtar, Salman / Habibi, Saeid

An autonomous vehicle would benefit from being able to predict trajectories of other vehicles in its vicinity for improved safety. In order for the self-driving car to plan safe trajectories, paths of nearby vehicles are required to be predicted for risk assessment, decision making, and motion planning. In this study, a trajectory prediction algorithm based on the Interacting Multiple Model (IMM) estimation strategy is proposed to predict paths involving lane-changing, lane-keeping, and turning motion. More specifically, the Interacting Multiple Model estimation technique is used with models defined in curvi-linear coordinates to predict a vehicle’s trajectory based on prior behavioral maneuvers. The road geometry is used to help facilitate behavior identification and prediction. Moreover, the combination of a more recently developed estimation technique known as the Generalized Variable Boundary Layer-Smooth Variable Structure Filter and the Interacting Multiple Model Estimator is applied to track, identify behaviors, and predict trajectories of a vehicle. The performance of this technique is compared with a Kalman Filter based formulation using synthetic and experimental data. This model-based strategy is also compared with machine learning-based strategies for trajectory prediction.