51 citations · 88 across the 7 of their papers we have counts for
9 papers
Scaling Laws of Motion Forecasting and Planning -- Technical Report
Mustafa Baniodeh, Kratarth Goel, Scott Ettinger +14
We study the empirical scaling laws of a family of encoder-decoder autoregressive transformer models on the task of joint motion forecasting and planning in the autonomous driving…
Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving
Zhenghao Peng, Wenjie Luo, Yiren Lu +4
A major challenge in autonomous vehicle research is modeling agent behaviors, which has critical applications including constructing realistic and reliable simulations for off-boar…
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
Cole Gulino, Justin Fu, Wenjie Luo +19
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 m…
The Waymo Open Sim Agents Challenge
Nico Montali, John Lambert, Paul Mougin +9
Simulation with realistic, interactive agents represents a key task for autonomous vehicle software development. In this work, we introduce the Waymo Open Sim Agents Challenge (WOS…
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
Sergio Casas, Cole Gulino, Simon Suo +3
In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants. In this paper, we aim t…
Imitative Planning using Conditional Normalizing Flow
Shubhankar Agarwal, Harshit Sikchi, Cole Gulino +2
A popular way to plan trajectories in dynamic urban scenarios for Autonomous Vehicles is to rely on explicitly specified and hand crafted cost functions, coupled with random sampli…
The Importance of Prior Knowledge in Precise Multimodal Prediction
Sergio Casas, Cole Gulino, Simon Suo +1
Roads have well defined geometries, topologies, and traffic rules. While this has been widely exploited in motion planning methods to produce maneuvers that obey the law, little wo…
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
Sergio Casas, Cole Gulino, Renjie Liao +1
In this paper, we tackle the problem of relational behavior forecasting from sensor data. Towards this goal, we propose a novel spatially-aware graph neural network (SpAGNN) that m…
Generalizing Informed Sampling for Asymptotically Optimal Sampling-based Kinodynamic Planning via Markov Chain Monte Carlo
Daqing Yi, Rohan Thakker, Cole Gulino +2
Asymptotically-optimal motion planners such as RRT* have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found,…