Cut-in Detection from Videos
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2023
Författare
Udayakumar, Apoorva
Varma, Aditya Padmanabhan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
cut-in , Zenseact , autonomous driving , indicator , intent prediction