Prediction of the likely evolution of traffic scenes is a challenging task because of high uncertainties from sensing technology and the dynamic environment. It leads to failure of motion planning for intelligent agents like autonomous vehicles. In this paper, we propose a fluid-inspired model to estimate collision risk in road scenes. Multi-object states are detected and tracked, and then a stable fluid model is adopted to construct the risk field. Objects’ state spaces are used as the boundary conditions in the simulation of advection and diffusion processes. We have evaluated our approach on the public KITTI dataset; our model can provide predictions in the cases of misdetection and tracking error caused by occlusion. It proves a promising approach for collision risk assessment in road scenes.

由于传感技术和动态环境的不确定性很高，因此，预测交通场景可能的演变是一项艰巨的任务。这会导致自动驾驶汽车等智能代理的运动计划失败。在本文中，我们提出了一种流体启发模型来估计道路场景中的碰撞风险。对多目标状态进行检测和跟踪，然后采用稳定的流体模型构造风险场。在对流和扩散过程的模拟中，将对象的状态空间用作边界条件。我们已经在公共KITTI数据集上评估了我们的方法；我们的模型可以在由于遮挡引起的误检测和跟踪错误的情况下提供预测。它被证明是一种在道路场景中进行碰撞风险评估的有前途的方法。