Interaction-Aware Cut-in Behavior Prediction and Risk Assessment for Autonomous Driving

Cut-in behavior commonly occurs in both urban and highway driving. Rear-end collisions happen when the lag vehicles cannot predict this abnormal lane change behavior of the front vehicles and response in time. However, related studies on cut-in event prediction and risk assessment have rarely been presented in autonomous driving field. A phase-based design framework is proposed in this work to realize online prediction and risk estimation of the cut-in behavior considering interactions between the involved vehicles. After preprocessing and analyzing a naturalistic driving dataset, a cut-in behavior predictor and a risk estimator are devised based on Gaussian mixture model. Comparing with baseline approaches, both the predictor and estimator designed following the proposed framework achieve enhanced results, which can further improve the driving safety of autonomous vehicles when cut-in behavior occurs.