The Interacting Multiple Model Smooth Variable Structure Filter for Trajectory Prediction

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.

I. Introduction

Self-driving and Advanced Driver Assistance Systems (ADAS) are tools that can be instrumental to reducing driving accidents. Existing ADAS functions implemented in cars include adaptive cruise control, lane departure warning, anti-lock braking, and parking assistance. However, in order for cars to navigate more autonomously and safely, trajectory prediction is needed for understanding the traffic environment and for performing risk assessment. Risk assessment must be conducted for the vehicle to plan safe trajectories as it navigates. To perform trajectory prediction, the object tracking system must estimate the state of motion of surrounding vehicles and then path prediction should occur.