5 papers
Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Driving
Ehsan Ahmadi, Ray Mercurius, Soheil Alizadeh +2
Trajectory prediction models in autonomous driving are vulnerable to perturbations from non-causal agents whose actions should not affect the ego-agent's behavior. Such perturbatio…
Getting SMARTER for Motion Planning in Autonomous Driving Systems
Montgomery Alban, Ehsan Ahmadi, Randy Goebel +1
Motion planning is a fundamental problem in autonomous driving and perhaps the most challenging to comprehensively evaluate because of the associated risks and expenses of real-wor…
CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving
Changhe Chen, Mozhgan Pourkeshavarz, Amir Rasouli
Benchmarking is a common method for evaluating trajectory prediction models for autonomous driving. Existing benchmarks rely on datasets, which are biased towards more common scena…
TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
Junrui Zhang, Mozhgan Pourkeshavarz, Amir Rasouli
As a safety critical task, autonomous driving requires accurate predictions of road users' future trajectories for safe motion planning, particularly under challenging conditions.…
AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction
Ray Coden Mercurius, Ehsan Ahmadi, Soheil Mohamad Alizadeh Shabestary +1
Accurate prediction of pedestrians' future motions is critical for intelligent driving systems. Developing models for this task requires rich datasets containing diverse sets of sa…