publications | Sungjun Heo

To be disclosed after the double-blind peer review process

Sungjun Heo, S. Yang, H. Jeong, J. Ha, and J. Jeon
SHIFT-RL: Sensor-driven Hierarchical Information Fusion Transformer for BEV-based Maneuvering in Dense Multi-lane Environments

Sungjun Heo and Jeong hwan Jeon

Accepted; to be presented at: IEEE/RSJ International Conference on Intelligent Robots and Systems (2026 IROS)
Capacity-Controlled Enforcement of Sparse Traffic-Light Cues for Rule-Conformant End-to-End Driving

Sungjun Heo, H. Jeong, E. Kim, and 4 more authors

Accepted; to be presented at: IEEE International Conference on Systems, Man, and Cybernetics (2026 SMC) · Oral Presentation
Decoupling Actor and Critic Replay for Offline Reinforcement Learning with Three-Factor Selection

Sungjun Heo and Jeong hwan Jeon

Accepted; to be presented at: IEEE International Conference on Systems, Man, and Cybernetics (2026 SMC) · Oral Presentation

Deployment-Oriented End-to-End Autonomous Driving: Enhancing Closed-Loop Stability with a Lightweight Framework

H. Jeong, E. Kim, Sungjun Heo, and 4 more authors

In IEEE Intelligent Vehicles Symposium (2026 IV) · Oral Presentation (Top 8.5% of Accepted Papers)

@inproceedings{11623830,
  author = {Jeong, H. and Kim, E. and Heo, Sungjun and Kim, S. and Lee, S. and Shin, J. and others},
  booktitle = {IEEE Intelligent Vehicles Symposium (2026 IV)},
  title = {Deployment-Oriented End-to-End Autonomous Driving: Enhancing Closed-Loop Stability with a Lightweight Framework},
  pages = {694-701},
  doi = {10.1109/IV66570.2026.11623830},
  note = {Oral Presentation (Top 8.5\% of Accepted Papers)}
}

Predicted State-Based Hierarchical Reinforcement Learning for Long-Term Decision Making in Urban Dynamic Scenarios

DOI BIB

Sungjun Heo and Jeong hwan Jeon

In IEEE Intelligent Vehicles Symposium (2025 IV)

@inproceedings{11097746,
  author = {Heo, Sungjun and Jeon, Jeong hwan},
  booktitle = {IEEE Intelligent Vehicles Symposium (2025 IV)},
  title = {Predicted State-Based Hierarchical Reinforcement Learning for Long-Term Decision Making in Urban Dynamic Scenarios},
  pages = {1164-1170},
  doi = {10.1109/IV64158.2025.11097746}
}