collaborators

7 papers

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

Towards Generative Predictive Display for Vision-Based Teleoperation: A Zero-Shot Benchmark of Off-the-Shelf Video Models

Aws Khalil, Jaerock Kwon

Teleoperation systems are fundamentally limited by communication latency, which degrades situational awareness and control performance. Predictive display aims to mitigate this lim…

cs.RO2026

Defining an Evaluation Method for External Human-Machine Interfaces

Jose Gonzalez-Belmonte, Jaerock Kwon

As the number of fatalities involving Autonomous Vehicles increase, the need for a universal method of communicating between vehicles and other agents on the road has also increase…

cs.RO2026

Using Unwrapped Full Color Space Recording to Measure the Exposedness of Vehicle Exterior Parts for External Human Machine Interfaces

Jose Gonzalez-Belmonte, Jaerock Kwon

One of the concerns with autonomous vehicles is their ability to communicate their intent to other road users, specially pedestrians, in order to prevent accidents. External Human-…

cs.RO2026

PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles

Aws Khalil, Jaerock Kwon

This study introduces the Perception Latency Mitigation Network (PLM-Net), a modular deep learning framework designed to mitigate perception latency in vision-based imitation-learn…