3 citations · 4 across the 8 of their papers we have counts for
11 papers
BranchDrive: A Branch-Structured Dataset for Action-Conditioned Driving Prediction
Feeza Khan Khanzada, Sudarshan Sridhar, Jaerock Kwon
Most autonomous-driving datasets record only the action executed by a behavior policy and the single future that followed, providing limited supervision for comparing alternative e…
Nonlinear Performance Degradation of Vision-Based Teleoperation under Network Latency
Aws Khalil, Jaerock Kwon
Teleoperation is increasingly being adopted as a critical fallback for autonomous vehicles. However, the impact of network latency on vision-based, perception-driven control remain…
InDRiVE: Reward-Free World-Model Pretraining for Autonomous Driving via Latent Disagreement
Feeza Khan Khanzada, Jaerock Kwon
Model-based reinforcement learning (MBRL) can reduce interaction cost for autonomous driving by learning a predictive world model, but it typically still depends on task-specific r…
Driving Beyond Privilege: Distilling Dense-Reward Knowledge into Sparse-Reward Policies
Feeza Khan Khanzada, Jaerock Kwon
We study how to exploit dense simulator-defined rewards in vision-based autonomous driving without inheriting their misalignment with deployment metrics. In realistic simulators su…
A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
Elahe Delavari, Feeza Khan Khanzada, Jaerock Kwon
Autonomous-driving research has recently embraced deep Reinforcement Learning (RL) as a promising framework for data-driven decision making, yet a clear picture of how these algori…
Action Space Reduction Strategies for Reinforcement Learning in Autonomous Driving
Elahe Delavari, Feeza Khan Khanzada, Jaerock Kwon
Reinforcement Learning (RL) offers a promising framework for autonomous driving by enabling agents to learn control policies through interaction with environments. However, large a…
Analytical Study on the Exposedness of Potential Positions for External Human-Machine Interfaces
Jose Gonzalez-Belmonte, Jaerock Kwon
As we move towards a future of autonomous vehicles, questions regarding their method of communication have arisen. One of the common questions concerns the placement of the signali…
InDRiVE: Intrinsic Disagreement based Reinforcement for Vehicle Exploration through Curiosity Driven Generalized World Model
Feeza Khan Khanzada, Jaerock Kwon
Model-based Reinforcement Learning (MBRL) has emerged as a promising paradigm for autonomous driving, where data efficiency and robustness are critical. Yet, existing solutions oft…
Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control
Elahe Delavari, John Moore, Junho Hong +1
This paper presents a novel Perceptual Motor Learning (PML) framework integrated with Active Inference (AIF) to enhance lateral control in Highly Automated Vehicles (HAVs). PML, in…
A Physics-Informed Context-Aware Approach for Anomaly Detection in Tele-driving Operations Under False Data Injection Attacks
Subhadip Ghosh, Aydin Zaboli, Junho Hong +1
Tele-operated driving (ToD) systems are special types of cyber-physical systems (CPSs) where the operator remotely controls the steering, acceleration, and braking actions of the v…
Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control
Elahe Delavari, John Moore, Junho Hong +1
This paper presents a novel approach to Autonomous Vehicle (AV) control through the application of active inference, a theory derived from neuroscience that conceptualizes the brai…