SMART: Scalable Multi-agent Real-time Simulation via Next-token Prediction
W Wu, X Feng, Z Gao, Y Kan - arXiv preprint arXiv:2405.15677, 2024 - arxiv.org
Data-driven autonomous driving motion generation tasks are frequently impacted by the
limitations of dataset size and the domain gap between datasets, which precludes their …
limitations of dataset size and the domain gap between datasets, which precludes their …
Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset
As autonomous driving systems mature, motion forecasting has received increasing
attention as a critical requirement for planning. Of particular importance are interactive …
attention as a critical requirement for planning. Of particular importance are interactive …
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
This letter presents a S imple and eff I cient M otion P rediction base L ine (SIMPL) for
autonomous vehicles. Unlike conventional agent-centric methods with high accuracy but …
autonomous vehicles. Unlike conventional agent-centric methods with high accuracy but …
Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
Simulation is an essential tool to develop and benchmark autonomous vehicle planning
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
software in a safe and cost-effective manner. However, realistic simulation requires accurate …
M2i: From factored marginal trajectory prediction to interactive prediction
Predicting future motions of road participants is an important task for driving autonomously in
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …
Trajeglish: Learning the language of driving scenarios
A longstanding challenge for self-driving development is simulating dynamic driving
scenarios seeded from recorded driving logs. In pursuit of this functionality, we apply tools …
scenarios seeded from recorded driving logs. In pursuit of this functionality, we apply tools …
Int2: Interactive trajectory prediction at intersections
Motion forecasting is an important component in autonomous driving systems. One of the
most challenging problems in motion forecasting is interactive trajectory prediction, whose …
most challenging problems in motion forecasting is interactive trajectory prediction, whose …
Motionlm: Multi-agent motion forecasting as language modeling
Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
Scene transformer: A unified architecture for predicting multiple agent trajectories
Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …
Motiondiffuser: Controllable multi-agent motion prediction using diffusion
We present MotionDiffuser, a diffusion based representation for the joint distribution of future
trajectories over multiple agents. Such representation has several key advantages: first, our …
trajectories over multiple agents. Such representation has several key advantages: first, our …