Han, YiWang, HeJin, XiaogangUmetani, NobuyukiWojtan, ChrisVouga, Etienne2022-10-042022-10-0420221467-8659https://doi.org/10.1111/cgf.14699https://diglib.eg.org:443/handle/10.1111/cgf14699We present a novel traffic trajectory editing method which uses spatio-temporal keyframes to control vehicles during the simulation to generate desired traffic trajectories. By taking self-motivation, path following and collision avoidance into account, the proposed force-based traffic simulation framework updates vehicle's motions in both the Frenet coordinates and the Cartesian coordinates. With the way-points from users, lane-level navigation can be generated by reference path planning. With a given keyframe, the coarse-to-fine optimization is proposed to efficiently generate the plausible trajectory which can satisfy the spatio-temporal constraints. At first, a directed state-time graph constructed along the reference path is used to search for a coarse-grained trajectory by mapping the keyframe as the goal. Then, using the information extracted from the coarse trajectory as initialization, adjoint-based optimization is applied to generate a finer trajectory with smooth motions based on our force-based simulation. We validate our method with extensive experiments.We present a novel traffic trajectory editing method which uses spatio-temporal keyframes to control vehicles during the simulation to generate desired traffic trajectories. By taking self-motivation, path following and collision avoidance into account, the proposed force-based traffic simulation framework updates vehicle's motions in both the Frenet coordinates and the Cartesian coordinates. With the way-points from users, lane-level navigation can be generated by reference path planning. With a given keyframe, the coarse-to-fine optimization is proposed to efficiently generate the plausible trajectory which can satisfy the spatio-temporal constraints. At first, a directed state-time graph constructed along the reference path is used to search for a coarse-grained trajectory by mapping the keyframe as the goal. Then, using the information extracted from the coarse trajectory as initialization, adjoint-based optimization is applied to generate a finer trajectory with smooth motions based on our force-based simulation. We validate our method with extensive experiments.We present a novel traffic trajectory editing method which uses spatiotemporal keyframes to control vehicles during the simulation to generate desired traffic trajectories. By taking selfmotivationpath following and collision avoidance into accountthe proposed forcebased traffic simulation framework updates vehicle's motions in both the Frenet coordinates and the Cartesian coordinates. With the waypoints from userslanelevel navigation can be generated by reference path planning. With a given keyframethe coarsetofine optimization is proposed to efficiently generate the plausible trajectory which can satisfy the spatiotemporal constraints. At firsta directed statetime graph constructed along the reference path is used to search for a coarsegrained trajectory by mapping the keyframe as the goal. Thenusing the information extracted from the coarse trajectory as initializationadjointbased optimization is applied to generate a finer trajectory with smooth motions based on our forcebased simulation. We validate our method with extensive experiments.10.1111/cgf.14699541-55212 pages