Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios
This work addresses the lack of interpretability in neural network-based trajectory prediction methods for autonomous driving, which is critical for road safety, but it is incremental as it hybridizes existing approaches.
The paper tackles the problem of predicting vehicle trajectories in interactive scenarios by combining a discrete choice model for interpretability with a neural network for accuracy, achieving effective explanation of predictions without compromising accuracy on the INTERACTION dataset.
The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to explain because of their uncertainty. In recent years, neural network-based methods have been widely used for trajectory prediction and have been shown to outperform hand-crafted methods. However, these methods suffer from their lack of interpretability. In order to overcome this limitation, we combine the interpretability of a discrete choice model with the high accuracy of a neural network-based model for the task of vehicle trajectory prediction in an interactive environment. We implement and evaluate our model using the INTERACTION dataset and demonstrate the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.