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.

轨迹预测对于规划未来安全交通的应用至关重要，即使在未来几秒钟的城市混合交通中，轨迹预测仍然具有挑战性。代理如何移动受其在不同环境中相邻代理的各种行为的影响。为了预测运动，我们提出了一个名为Attentive Maps Encoder Network（AMENet）的端到端生成模型。可以对主体的运动和交互信息进行编码，以进行准确而现实的多路径轨迹预测。训练条件变分自动编码器模块以基于用于交互建模的注意力动态图来学习可能的未来路径的潜在空间，然后将其用于预测以观察到的过去轨迹为条件的多个可能的未来轨迹。AMENet的有效性通过两个公开的轨迹预测基准Trajnet和InD进行了验证。