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In highway scenarios, an alert human driver will typically anticipate earlycut-in and cut-out maneuvers of surrounding vehicles using only visual cues. Anautomated system must anticipate these situations at an early stage too, toincrease the safety and the efficiency of its performance. To deal withlane-change recognition and prediction of surrounding vehicles, we pose theproblem as an action recognition/prediction problem by stacking visual cuesfrom video cameras. Two video action recognition approaches are analyzed:two-stream convolutional networks and spatiotemporal multiplier networks.Different sizes of the regions around the vehicles are analyzed, evaluating theimportance of the interaction between vehicles and the context information inthe performance. In addition, different prediction horizons are evaluated. Theobtained results demonstrate the potential of these methodologies to serve asrobust predictors of future lane-changes of surrounding vehicles in timehorizons between 1 and 2 seconds.

Two-Stream Networks for Lane-Change Prediction of Surrounding Vehicles