1 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction
Younwoo Choi, Ray Coden Mercurius, Soheil Mohamad Alizadeh Shabestary +1
Road user trajectory prediction in dynamic environments is a challenging but crucial task for various applications, such as autonomous driving. One of the main challenges in this d…
A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction
Amir Rasouli
Predicting pedestrian behavior is one of the main challenges for intelligent driving systems. In this paper, we present a new paradigm for evaluating egocentric pedestrian trajecto…
DESTINE: Dynamic Goal Queries with Temporal Transductive Alignment for Trajectory Prediction
Rezaul Karim, Soheil Mohamad Alizadeh Shabestary, Amir Rasouli
Predicting temporally consistent road users' trajectories in a multi-agent setting is a challenging task due to unknown characteristics of agents and their varying intentions. Besi…
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…