7 papers · 1 filter
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…
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…