Trajectory prediction is critical for applications of planning safe future
movements and remains challenging even for the next few seconds in urban mixed
traffic. How an agent moves is affected by the various behaviors of its
neighboring agents in different environments. To predict movements, we propose
an end-to-end generative model named Attentive Maps Encoder Network (AMENet)
that encodes the agent's motion and interaction information for accurate and
realistic multi-path trajectory prediction. A conditional variational
auto-encoder module is trained to learn the latent space of possible future
paths based on attentive dynamic maps for interaction modeling and then is used
to predict multiple plausible future trajectories conditioned on the observed
past trajectories. The efficacy of AMENet is validated using two public
trajectory prediction benchmarks Trajnet and InD.

提出了一种名为 AMENet 的端到端生成模型，使用注意力机制动态地建模了邻居交互信息，实现了准确和逼真的轨迹多路径预测，并在 Trajnet 和 InD 两个公共轨迹预测数据集上进行了验证。