9 papers
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…
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 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…
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…