论文标题
通过时空学习预测动态环境中未来的入住网格
Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning
论文作者
论文摘要
可靠地预测高度动态的城市环境的未来占用是安全自动导航的重要先驱。预测中的常见挑战包括预测其他车辆的相对位置，建模遭受不同交通状况的车辆的动态以及消失的物体消失。为了应对这些挑战，我们提出了一条时空预测网络管道，该管道从环境和语义标签中分别获取过去的信息，以产生未来的占用预测。与当前的SOTA相比，我们的方法可以预测3秒钟的较长视野，并且在Nuscenes数据集的相对复杂环境中。我们的实验结果表明，时空网络不需要HD-图和显式建模动态对象的能力了解场景动态。我们公开发布基于Nuscenes的占用网格数据集，以支持进一步的研究。
Reliably predicting future occupancy of highly dynamic urban environments is an important precursor for safe autonomous navigation. Common challenges in the prediction include forecasting the relative position of other vehicles, modelling the dynamics of vehicles subjected to different traffic conditions, and vanishing surrounding objects. To tackle these challenges, we propose a spatio-temporal prediction network pipeline that takes the past information from the environment and semantic labels separately for generating future occupancy predictions. Compared to the current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds and in a relatively complex environment from the nuScenes dataset. Our experimental results demonstrate the ability of spatio-temporal networks to understand scene dynamics without the need for HD-Maps and explicit modeling dynamic objects. We publicly release our occupancy grid dataset based on nuScenes to support further research.
