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.
翻译:预测未来使用高度动态的城市环境是安全自主航行的重要前提。预测的共同挑战包括预测其他车辆的相对位置、模拟受不同交通条件影响的车辆的动态以及周围物体的消失。为了应对这些挑战,我们提议建立一个时空预测网络管道,从环境和语义标签中分别收集过去的信息,以得出未来占用预测。与目前的SOTA相比,我们的方法预测占用的时间范围较长,为3秒钟,在比NuScenes数据集相对复杂的环境中。我们的实验结果表明,spatio-时间网络有能力理解现场动态,而不需要HD-Masps和明确的模拟动态物体。我们公开发布基于nuscenes的占用网数据集,以支持进一步的研究。