The development of algorithms that learn behavioral driving models using human demonstrations has led to increasingly realistic simulations. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annotated high-definition (HD) maps. Recent studies show that these models can greatly benefit from increasing the amount of human data available for training. However, the manual annotation of HD maps which is necessary for every new location puts a bottleneck on efficiently scaling up human traffic datasets. We propose a drone birdview image-based map (DBM) representation that requires minimal annotation and provides rich road context information. We evaluate multi-agent trajectory prediction using the DBM by incorporating it into a differentiable driving simulator as an image-texture-based differentiable rendering module. Our results demonstrate competitive multi-agent trajectory prediction performance when using our DBM representation as compared to models trained with rasterized HD maps.

翻译：利用人类演示学习行为驾驶模型的算法开发，推动了日益逼真的模拟仿真。此类模型通常通过利用从人工标注的高精度地图中获取的可行驶车道等道路环境信息，联合预测所有受控智能体的轨迹。近期研究表明，增加用于训练的人类数据量能使这些模型显著受益。然而，每个新位置所需的高精度地图人工标注，成为高效扩展人类交通数据集的关键瓶颈。我们提出一种基于无人机鸟瞰图像的DBM表示方法，该方法所需标注极少，却能提供丰富的道路环境信息。我们通过将DBM作为基于图像纹理的可微分渲染模块，集成到可微分驾驶模拟器中，评估了基于DBM的多智能体轨迹预测性能。结果表明，与使用栅格化高精度地图训练的模型相比，采用DBM表示方法的模型在多智能体轨迹预测性能上具有竞争力。