6 citations · 14 across the 8 of their papers we have counts for
9 papers
VL-DPO: Vision-Language-Guided Finetuning for Preference-Aligned Autonomous Driving
Zhefan Xu, Ghassen Jerfel, Marina Haliem +3
The rapid growth of autonomous driving datasets has enabled the scaling of powerful motion forecasting models. While large-scale pretraining provides strong performance, the standa…
MotionLM: Multi-Agent Motion Forecasting as Language Modeling
Ari Seff, Brian Cera, Dian Chen +6
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…
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
Jiachen Li, Xinwei Shi, Feiyu Chen +9
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersec…
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.…
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
Balakrishnan Varadarajan, Ahmed Hefny, Avikalp Srivastava +8
Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving. Applying deep learning to this problem requires fusing het…
Agent Prioritization for Autonomous Navigation
Khaled S. Refaat, Kai Ding, Natalia Ponomareva +1
In autonomous navigation, a planning system reasons about other agents to plan a safe and plausible trajectory. Before planning starts, agents are typically processed with computat…
New Advances and Theoretical Insights into EDML
Khaled S. Refaat, Arthur Choi, Adnan Darwiche
EDML is a recently proposed algorithm for learning MAP parameters in Bayesian networks. In this paper, we present a number of new advances and insights on the EDML algorithm. First…
EDML: A Method for Learning Parameters in Bayesian Networks
Arthur Choi, Khaled S. Refaat, Adnan Darwiche
We propose a method called EDML for learning MAP parameters in binary Bayesian networks under incomplete data. The method assumes Beta priors and can be used to learn maximum likel…