Spatio-temporal Keyframe Control of Traffic Simulation using Coarse-to-Fine Optimization

TL;DR

This paper introduces a novel spatio-temporal keyframe control method for traffic simulation, combining coarse-to-fine optimization to generate realistic vehicle trajectories that adhere to user-defined constraints.

Contribution

It presents a new force-based traffic simulation framework that integrates spatio-temporal keyframes with a coarse-to-fine optimization approach for realistic vehicle trajectory generation.

Findings

Efficient generation of plausible traffic trajectories satisfying spatio-temporal constraints.

Effective integration of coarse and fine optimization for smooth vehicle motions.

Validation through extensive experiments demonstrating method's effectiveness.

Abstract

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.