6 citations · 12 across the 4 of their papers we have counts for
4 papers
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…
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…