Cut-in Detection from Videos

Abstract

For autonomous driving, it is crucial to anticipate the behaviour of other road users and act accordingly. One important scenario is when a vehicle cuts into the lane of the ego-vehicle with or without sufficient indicator cue. This thesis studies such a scenario and investigates the application of deep learning techniques for understanding and predicting when the cut-in maneuver is performed by the vehicles ahead. As a first step, indicator cues from videos of vehicles performing cut-ins are detected successfully with an F1 score of of 83% and recall value of 85%. We achieve this result by employing ResNet-18 and CNN-LSTM with a tuned level of context around the target vehicle. Further we predict the estimates of interest such as start and end of cut-in intention using the same architectures and discuss the challenges.