In this work, we address the problem of lane change maneuver prediction in highway scenarios using information from sensors and perception systems widely used in automated driving. Our prediction approach is twofold. First, a driver model learned from demonstrations via Inverse Reinforcement Learning is used to equip a host vehicle with the anticipatory behavior reasoning capability of common drivers. Second, inference on an interaction-aware augmented Switching State-Space Model allows the approach to account for the dynamic evidence observed. The use of a driver model that correctly balances the driving and risk-aversive preferences of a driver allows the computation of a planning-based maneuver prediction. Integrating this anticipatory prediction into the maneuver inference engine brings a degree of scene understanding into the estimate and leads to faster lane change detections compared to those obtained by relying on dynamics alone. The performance of the presented framework is evaluated using highway data collected with an instrumented vehicle. The combination of model-based maneuver prediction and filtering-based state and maneuver tracking is shown to outperform an Interacting Multiple Model filter in the detection of highway lane change maneuvers regarding accuracy, detection latency—by an average of 0.4 seconds—and false-positive rates.