Predicting distant future trajectories of agents in a dynamic scene is challenging because the future trajectory of an agent is affected not only by their past trajectory but also the scene contexts. To tackle this problem, the authors propose a model based on recurrent neural networks, and a novel method for training this model. The proposed model is based on an encoder–decoder architecture where the encoder encodes inputs (past trajectory and scene context information), while the decoder produces a future trajectory from the context vector given by the encoder. To make the proposed model better utilise the scene context information, the authors let the encoder predict the positions in the past trajectory and a reward function evaluate the positions along with the scene context information generated by the positions. The reward function, which is simultaneously trained with the proposed model, plays the role of a regulariser for the model during the simultaneous training. The authors evaluate the proposed model on several public benchmark datasets. The experimental results show that the prediction performance of the proposed model is greatly improved by the proposed regularisation method, which outperforms the-state-of-the-art models in terms of accuracy.

中文翻译：

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通过逆强化学习框架对神经网络进行正则化以用于未来的轨迹预测

预测动态场景中智能体的遥远未来轨迹具有挑战性，因为智能体的未来轨迹不仅受其过去轨迹的影响，还受场景上下文的影响。为了解决这个问题，作者提出了一个基于递归神经网络的模型，以及一种训练该模型的新方法。所提出的模型基于编码器-解码器体系结构，其中编码器对输入（过去的轨迹和场景上下文信息）进行编码，而解码器从编码器给出的上下文向量中生成将来的轨迹。为了使所提出的模型更好地利用场景上下文信息，作者让编码器预测了过去轨迹中的位置，并且奖励函数对这些位置以及由这些位置生成的场景上下文信息进行了评估。奖励功能，与提出的模型同时训练的模型在同时训练过程中扮演模型的正则化角色。作者在几个公共基准数据集上评估了提出的模型。实验结果表明，所提出的正则化方法大大提高了所提模型的预测性能，在准确性方面优于最先进的模型。