With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance. Nevertheless, most of these approaches follow a non-interactive prediction and planning paradigm, hypothesizing that a vehicle's behaviors do not affect others. The approaches based on such a non-interactive philosophy typically perform acceptably in sparse traffic scenarios but can easily fail in dense traffic scenarios. Therefore, we propose an end-to-end interactive neural motion planner (INMP) for autonomous driving in this paper. Given a set of past surrounding-view images and a high definition map, our INMP first generates a feature map in bird's-eye-view space, which is then processed to detect other agents and perform interactive prediction and planning jointly. Also, we adopt an optical flow distillation paradigm, which can effectively improve the network performance while still maintaining its real-time inference speed. Extensive experiments on the nuScenes dataset and in the closed-loop Carla simulation environment demonstrate the effectiveness and efficiency of our INMP for the detection, prediction, and planning tasks. Our project page is at sites.google.com/view/inmp-ofd.

翻译：最近,随着深层学习技术的进步,自主汽车预测和规划的数据驱动方法取得了非凡的成绩,然而,大多数这类方法都遵循非互动的预测和规划模式,假设车辆的行为不会影响他人。基于这种非互动哲学的方法通常在交通稀少的情况下可以接受,但在交通密集的情况下很容易失败。因此,我们提议在本文件中为自主驾驶建立一个端对端互动神经运动规划仪(INMP),以备在本文中进行自动驾驶。鉴于过去一套环景图像和高定义地图,我们的INMP首先在鸟眼观空间制作了一部地貌地图,然后进行处理,以探测其他物剂并联合进行互动的预测和规划。此外,我们采用了光学流蒸馏模型,这可以有效地改进网络的性能,同时保持其实时的推断速度。关于核星数据集和闭环卡拉模拟环境的广泛实验表明,我们的INMP在探测、预测和规划任务方面的效力和效率。我们的项目页面位于各站点。Google.com/ng-ind。