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Kasra Rezaee

12 accepted papers

2026

CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving

Hamidreza Mirkhani, Behzad Khamidehi, Ehsan Ahmadi, Mohammed Elmahgiubi, Weize Zhang, Fazel Arasteh +3
ICRA 2026poster

In this paper, we introduce Context-Aware Priority Sampling (CAPS), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses the challenge of imbalanced datasets in imitation learning by leveraging Vector Quantized Variational Autoencoders (VQ-V…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning

Ehsan Ahmadi, Hunter Schofield, Behzad Khamidehi, Fazel Arasteh, Jinjun Shan, Lili Mou +2
CVPR 2026

Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, multi-agent interactions common in complex driving scenarios. We introduce RLFTSim, a reinforcement-learning-based fine-tuning framework that enhances sc

Cited by 0Source ↗code ↗Scholar ↗
2025

Beyond Simulation: Benchmarking World Models for Planning and Causality in Autonomous Driving

Hunter Schofield, Mohammed Elmahgiubi, Kasra Rezaee, Jinjun Shan
ICRA 2025

World models have become increasingly popular in acting as learned traffic simulators. Recent work has explored replacing traditional traffic simulators with world models for policy training. In this work, we explore the robustness of existing metrics to evaluate world models as traffic simulators t

Cited by 1Source ↗Scholar ↗
2025

Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Driving

Ehsan Ahmadi, Ray Coden Mercurius, Soheil Alizadeh, Kasra Rezaee, Amir Rasouli
ICRA 2025

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 perturbations can lead to incorrect predictions of other agents' trajectories, potentially compromising the safety and efficiency o

Cited by 7Source ↗Scholar ↗
2025

Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning

Fazel Arasteh, Mohammed Elmahgiubi, Behzad Khamidehi, Hamidreza Mirkhani, Weize Zhang, Tongtong Cao +1
ICRA 2025

The planning problem constitutes a fundamental aspect of the autonomous driving framework. Recent strides in representation learning have empowered vehicles to comprehend their surrounding environments, thereby facilitating the integration of learning-based planning strategies. Among these approache

Cited by 7Source ↗Scholar ↗
2024

Confidence Aware Inverse Constrained Reinforcement Learning

Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart
ICML 2024poster

In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement learning (RL) agents need these constraints to learn the correct optimal policy in these settings. The field of Inverse Con…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2023

Benchmarking Constraint Inference in Inverse Reinforcement Learning

Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart
ICLR 2023poster

When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints. In many real-world problems, however, the constraints are often hard to specify mathematically and unknown to the RL agents. To tackle these issues…

Cited by 33Source ↗PDF ↗code ↗Scholar ↗
2023

Learning Soft Constraints From Constrained Expert Demonstrations

Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart
ICLR 2023top-25%

Inverse reinforcement learning (IRL) methods assume that the expert data is generated by an agent optimizing some reward function. However, in many settings, the agent may optimize a reward function subject to some constraints, where the constraints induce behaviors that may be otherwise difficult t…

Cited by 28Source ↗PDF ↗Scholar ↗
2022

Looking for Trouble: Informative Planning for Safe Trajectories with Occlusions

Barry Gilhuly, Armin Sadeghi, Peyman Yedmellat, Kasra Rezaee, Stephen L. Smith
ICRA 2022poster

Planning a safe trajectory for an ego vehicle through an environment with occluded regions is a challenging task. Existing methods use some combination of metrics to evaluate a trajectory, either taking a worst case view or allowing for some probabilistic estimate, to eliminate or minimize the risk…

Cited by 11Source ↗Scholar ↗
2021

Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning

Kasra Rezaee, Peyman Yadmellat, Simon Chamorro
IROS 2021poster

Motion planning under uncertainty is one of the main challenges in developing autonomous driving vehicles. In this work, we focus on the uncertainty in sensing and perception, resulted from a limited field of view, occlusions, and sensing range. This problem is often tackled by considering hypotheti…

Cited by 28Source ↗Scholar ↗
2020

SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving

Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao +26
CoRL 2020

Interaction is fundamental in autonomous driving (AD). Despite more than a decade of intensive R&D in AD, how to dynamically interact with diverse road users in various contexts still remains unsolved. Multi-agent learning has recently seen big breakthroughs and has much to offer towards solving rea

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2019

Perception as prediction using general value functions in autonomous driving applications

Daniel Graves, Kasra Rezaee, Sean Scheideman
IROS 2019poster

We propose and demonstrate a framework called perception as prediction for autonomous driving that uses general value functions (GVFs) to learn predictions. Perception as prediction learns data-driven predictions relating to the impact of actions on the agent's perception of the world. It also provi…

Cited by 16Source ↗Scholar ↗