Planning and prediction are two important modules of autonomous driving and have experienced tremendous advancement recently. Nevertheless, most existing methods regard planning and prediction as independent and ignore the correlation between them, leading to the lack of consideration for interaction and dynamic changes of traffic scenarios. To address this challenge, we propose InteractionNet, which leverages transformer to share global contextual reasoning among all traffic participants to capture interaction and interconnect planning and prediction to achieve joint. Besides, InteractionNet deploys another transformer to help the model pay extra attention to the perceived region containing critical or unseen vehicles. InteractionNet outperforms other baselines in several benchmarks, especially in terms of safety, which benefits from the joint consideration of planning and forecasting. The code will be available at https://github.com/fujiawei0724/InteractionNet.

翻译：规划与预测是自动驾驶的两大重要模块，近年来取得了显著进展。然而，现有方法大多将规划与预测视为独立任务，忽视了两者间的关联性，导致缺乏对交通场景中交互与动态变化的考量。为解决这一挑战，我们提出InteractionNet，该方法利用Transformer在所有交通参与者间共享全局上下文推理以捕捉交互行为，并通过连接规划与预测实现联合建模。此外，InteractionNet部署另一个Transformer，帮助模型额外关注包含关键或未观测车辆的感知区域。在多个基准测试中，InteractionNet优于其他基线方法，尤其在安全性方面表现突出，这得益于规划与预测的联合考量。代码将开源至https://github.com/fujiawei0724/InteractionNet。