Frenet Coordinate Based Driving Maneuver Prediction at Roundabouts Using LSTM Networks

Abstract

Driving maneuver prediction is a key requirement for automated vehicles to assess situations and effectively navigate in urban environments. In this paper, we present three models to predict whether a vehicle leaves a roundabout at a specific exit. We develop a Feedforward neural network (FNN), as well as two Long short-Term memory (LSTM) networks for this task. We propose several concepts that generalize the models to roundabouts with different radii, layouts, and numbers of exits. For this purpose, we also introduce Frenet coordinates with circles as reference paths. We evaluate our models based on the binary cross-entropy loss and the distance to the exit at which a reliable prediction is obtained in a leave-one-out cross-validation fashion, where one exit is always entirely used as the test set. Training and evaluation is performed on a data set of nearly 4,000 trajectories that we captured using a drone. Our best model achieves a reliable prediction on average 9.34m before an exit for class "Leaving"and 8.13m before an exit for class "Staying".