InteractionNet：基于Transformer的自动驾驶联合规划与预测
InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers
AI总结：
针对自动驾驶中规划与预测独立处理忽略交互的问题，提出InteractionNet，利用双transformer架构联合规划与预测并聚焦关键车辆，在多个基准上显著提升安全性。
摘要：
规划与预测是自动驾驶的两个重要模块，近期取得了巨大进展。然而，现有方法多将两者视为独立任务，忽略了它们之间的相关性，导致对交通场景中的交互和动态变化缺乏考虑。为此，我们提出InteractionNet，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，并将规划与预测互联实现联合。此外，InteractionNet部署另一transformer帮助模型对包含关键或未见过车辆的可感知区域投入额外关注。InteractionNet在多个基准上优于其他基线，尤其在安全性方面，这得益于规划与预测的联合考量。代码将发布于https://github.com/fujiawei0724/InteractionNet。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
