Predicut - A Machine Learning Model For Online Prediction of Cut-In Manoeuvre For Autonomous Vehicles

When a vehicle changes lane abruptly resulting in low headway distance to the vehicle behind it, it may trigger emergency braking or it may result in a collision. Such events are called cutins and they cause a majority of road accidents in the U.S. Autonomous Vehicles (AVs) have to predict such events in advance and react appropriately to such human behavior. We propose Predicut, a stacking classifier model that uses sensor data of an instrumented host vehicle and predicts if any of the neighboring vehicles would cut in front of it in the next 0.5 to 5.5 seconds. We have built and tested the model using the SPMD and NGSIM (I-80, US-101) driving datasets. The model was able to predict cut-ins with a maximum F1 score of 94.3%. From the datasets, we determine that the model through its advance prediction, avoided up to 90% of emergency braking scenarios. The model is highly performant with average inference time of 26ms when hosted on a cloud based serverless inference service.