  Lane change prediction of surrounding vehicles is a key building block of
path planning. The focus has been on increasing the accuracy of prediction by
posing it purely as a function estimation problem at the cost of model
understandability. However, the efficacy of any lane change prediction model
can be improved when both corner and failure cases are humanly understandable.
We propose an attention-based recurrent model to tackle both understandability
and prediction quality. We also propose metrics which reflect the discomfort
felt by the driver. We show encouraging results on a publicly available dataset
and proprietary fleet data.
