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In highway scenarios, an alert human driver will typically anticipate earlycut-in/cut-out maneuvers of surrounding vehicles using visual cues mainly.Autonomous vehicles must anticipate these situations at an early stage too, toincrease their safety and efficiency. In this work, lane-change recognition andprediction tasks are posed as video action recognition problems. Up to fourdifferent two-stream-based approaches, that have been successfully applied toaddress human action recognition, are adapted here by stacking visual cues fromforward-looking video cameras to recognize and anticipate lane-changes oftarget vehicles. We study the influence of context and observation horizons onperformance, and different prediction horizons are analyzed. The differentmodels are trained and evaluated using the PREVENTION dataset. The obtainedresults clearly demonstrate the potential of these methodologies to serve asrobust predictors of future lane-changes of surrounding vehicles proving anaccuracy higher than 90% in time horizons of between 1-2 seconds.

Video action recognition for lane-change classification and prediction of surrounding vehicles