1 citations · 1 across the 6 of their papers we have counts for
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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…
Mind the Privileged-to-Camera Gap: Actor-Centric Sidecar Supervision for Camera-First Open-Loop Waypoint Prediction
Feeza Khan Khanzada, Jaerock Kwon
Camera-first autonomous-driving models predict future ego waypoints from images, ego-state features, and route commands, but waypoint supervision alone does not explicitly supervis…
Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA
Feeza Khan Khanzada, Jaerock Kwon
Driving agents trained in one simulated town often perform poorly in a new town because the road shapes, intersections, and lane layouts can be different. This paper studies how to…
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…