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The abilities to understand the social interaction behaviors between avehicle and its surroundings while predicting its trajectory in an urbanenvironment are critical for road safety in autonomous driving. Socialinteractions are hard to explain because of their uncertainty. In recent years,neural network-based methods have been widely used for trajectory predictionand have been shown to outperform hand-crafted methods. However, these methodssuffer from their lack of interpretability. In order to overcome thislimitation, we combine the interpretability of a discrete choice model with thehigh accuracy of a neural network-based model for the task of vehicletrajectory prediction in an interactive environment. We implement and evaluateour model using the INTERACTION dataset and demonstrate the effectiveness ofour proposed architecture to explain its predictions without compromising theaccuracy.

Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios