211 citations · 528 across the 17 of their papers we have counts for
8 papers · 1 filter
JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving
Wenjie Luo, Cheolho Park, Andre Cornman +2
We propose JFP, a Joint Future Prediction model that can learn to generate accurate and consistent multi-agent future trajectories. For this task, many different methods have been…
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
Yiren Lu, Justin Fu, George Tucker +9
Imitation learning (IL) is a simple and powerful way to use high-quality human driving data, which can be collected at scale, to produce human-like behavior. However, policies base…
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou +3
Motion forecasting for autonomous driving is a challenging task because complex driving scenarios result in a heterogeneous mix of static and dynamic inputs. It is an open problem…
CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships
Rebecca Roelofs, Liting Sun, Ben Caine +4
As machine learning models become increasingly prevalent in motion forecasting for autonomous vehicles (AVs), it is critical to ensure that model predictions are safe and reliable.…
Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecasting
DiJia Su, Bertrand Douillard, Rami Al-Rfou +2
Behavior prediction models have proliferated in recent years, especially in the popular real-world robotics application of autonomous driving, where representing the distribution o…
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
Jinkyu Kim, Reza Mahjourian, Scott Ettinger +4
We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy.…