103 citations · 112 across the 19 of their papers we have counts for
19 papers
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
Ehsan Ahmadi, Hunter Schofield, Behzad Khamidehi +5
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 i…
Beyond Simulation: Benchmarking World Models for Planning and Causality in Autonomous Driving
Hunter Schofield, Mohammed Elmahgiubi, Kasra Rezaee +1
World models have become increasingly popular in acting as learned traffic simulators. Recent work has explored replacing traditional traffic simulators with world models for polic…
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
Hamidreza Mirkhani, Behzad Khamidehi, Ehsan Ahmadi +6
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…
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories
Niloufar Saeidi Mobarakeh, Behzad Khamidehi, Chunlin Li +6
The primary goal of motion planning is to generate safe and efficient trajectories for vehicles. Traditionally, motion planning models are trained using imitation learning to mimic…
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…
Confidence Aware Inverse Constrained Reinforcement Learning
Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi +2
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 lea…
Vectorized Representation Dreamer (VRD): Dreaming-Assisted Multi-Agent Motion-Forecasting
Hunter Schofield, Hamidreza Mirkhani, Mohammed Elmahgiubi +2
For an autonomous vehicle to plan a path in its environment, it must be able to accurately forecast the trajectory of all dynamic objects in its proximity. While many traditional m…
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning
Fazel Arasteh, Mohammed Elmahgiubi, Behzad Khamidehi +4
The planning problem constitutes a fundamental aspect of the autonomous driving framework. Recent strides in representation learning have empowered vehicles to comprehend their sur…
Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge
Weize Zhang, Mohammed Elmahgiubi, Kasra Rezaee +8
In this paper we present the architecture of the Kyber-E2E submission to the map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first place.…
Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories
Hamidreza Mirkhani, Behzad Khamidehi, Kasra Rezaee
Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning. However, imitating trajectories that inadequately represent the original expert da…
NeurIPS 2022 Competition: Driving SMARTS
Amir Rasouli, Randy Goebel, Matthew E. Taylor +15
Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous dri…
Benchmarking Constraint Inference in Inverse Reinforcement Learning
Guiliang Liu, Yudong Luo, Ashish Gaurav +2
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…
Learning Soft Constraints From Constrained Expert Demonstrations
Ashish Gaurav, Kasra Rezaee, Guiliang Liu +1
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…
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning
Kasra Rezaee, Peyman Yadmellat, Masoud S. Nosrati +3
Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety. This paper proposes a design for autonomous multi-lan…
How To Not Drive: Learning Driving Constraints from Demonstration
Kasra Rezaee, Peyman Yadmellat
We propose a new scheme to learn motion planning constraints from human driving trajectories. Behavioral and motion planning are the key components in an autonomous driving system.…
Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning
Kasra Rezaee, Peyman Yadmellat, Simon Chamorro
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, resu…
CoachNet: An Adversarial Sampling Approach for Reinforcement Learning
Elmira Amirloo Abolfathi, Jun Luo, Peyman Yadmellat +1
Despite the recent successes of reinforcement learning in games and robotics, it is yet to become broadly practical. Sample efficiency and unreliable performance in rare but challe…
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
Ming Zhou, Jun Luo, Julian Villella +34
Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently i…
Perception as prediction using general value functions in autonomous driving applications
Daniel Graves, Kasra Rezaee, Sean Scheideman
We propose and demonstrate a framework called perception as prediction for autonomous driving that uses general value functions (GVFs) to learn predictions. Perception as predictio…