Reciprocal Learning Networks for Human Trajectory Prediction
This work addresses trajectory prediction for applications like autonomous driving and robotics, offering an incremental improvement over existing methods.
The paper tackles human trajectory prediction by introducing reciprocal learning, which leverages the symmetry of forward and backward predictability to improve accuracy, achieving state-of-the-art results on benchmark datasets.
We observe that the human trajectory is not only forward predictable, but also backward predictable. Both forward and backward trajectories follow the same social norms and obey the same physical constraints with the only difference in their time directions. Based on this unique property, we develop a new approach, called reciprocal learning, for human trajectory prediction. Two networks, forward and backward prediction networks, are tightly coupled, satisfying the reciprocal constraint, which allows them to be jointly learned. Based on this constraint, we borrow the concept of adversarial attacks of deep neural networks, which iteratively modifies the input of the network to match the given or forced network output, and develop a new method for network prediction, called reciprocal attack for matched prediction. It further improves the prediction accuracy. Our experimental results on benchmark datasets demonstrate that our new method outperforms the state-of-the-art methods for human trajectory prediction.