ASCC update

Author: DDingR

_includes/memberLink.html

@@ -25,6 +25,8 @@
     {% assign memberUrl = "jjKim" %}
 {% elsif memberName contains "Changeun Park" %}
     {% assign memberUrl = "cePark" %}
+{% elsif memberName contains "Ahmad Mouri Zadeh Khaki" %}
+    {% assign memberUrl = "aMouri" %}
 
 <!-- ADD MORE HERE -->
 

_publications/2026-LLM-PPO.md

@@ -0,0 +1,38 @@
+---
+type: "Conference Paper" # Conference Paper, Journal Paper, Ph.D. Thesis, Master's Thesis
+layout: publication # Do not change this
+group: publications # Do not change this
+title: "LLM-PPO Driver: Improving Autonomous Driving via LLM-Guided Reward Shaping and Imitation Learning" # Title of the paper
+# krtitle: # only for domestic papers
+authors: 
+  - name: "Ahmad Mouri Zadeh Khaki"
+  - name: "Kyunghwan Choi"
+    corresponding: true # true if this author is the corresponding author
+domestic_or_international: "International" # "International" or "Domestic"
+# preprint: # Preprint information - REMOVE THIS FIELD IF NOT APPLICABLE!
+#   - name: Techrxiv 
+#     doi: "10.36227/techrxiv.173014412.26480551/v1"
+#     year: 2024
+    # pdf: "/static/pub/2025-all-wheel.pdf"
+    # state: "published" # published, accepted, submitted
+pub: # Publication information - REMOVE THIS FIELD IF NOT APPLICABLE!
+  - name: "Asian Control Conference (ASCC)"
+    pdf: "/static/pub/2026-LLM-PPO.pdf"
+    doi: # Leave it blank if not applicable
+    vol: # Leave it blank if not applicable
+    num: # Leave it blank if not applicable
+    pp: # "380-385" # Leave it blank if not applicable
+    year: "2026" # Leave it blank if not applicable
+    state: "submitted" # published, accepted, submitted
+    bib: # "/static/pub/2025-imposing.bib" # Leave it blank if not applicable
+pub_date: "2026-02-16" # Date of publication. Change Techrxiv (or other preprint) date to Journal date once published.
+image: "/static/pub/2026-LLM-PPO.png" # Representative image of the paper
+abstract: "
+ Proximal Policy Optimization (PPO) has shown promise for autonomous driving; however, it suffers from sparse rewards, slow convergence, and unsafe behaviors due to exploration without prior knowledge. These limitations are particularly critical in safety-sensitive driving scenarios, where failure events are rare but severe. To address this issue, we propose LLM-PPO Driver, a framework that enhances PPO-based motion planning by incorporating high-level semantic driving knowledge from a Large Language Model (LLM). The LLM does not participate in real-time decision-making; instead, it provides structured prior knowledge that is integrated through reward shaping and imitation learning. This lightweight and modular design eliminates deployment-time inference overhead while guiding policy learning toward safer and more efficient behaviors. Experiments in the Gym highway-v0 environment demonstrate consistent improvements in task success and safety over a baseline PPO agent, with imitation learning yielding the largest performance gain. These results highlight the effectiveness of leveraging LLM-based prior knowledge to mitigate unsafe exploration and improve learning efficiency in autonomous driving.
+"
+# additional: # additional information such as awards, etc.
+#  - "📄 Awarded **Best Paper Award** at the _2025 European Control Conference (ECC)_."
+# links: # additional links;
+#   - name: 
+#     url: 
+---

_publications/2026-traffic-network.md

@@ -0,0 +1,37 @@
+---
+type: "Conference Paper" # Conference Paper, Journal Paper, Ph.D. Thesis, Master's Thesis
+layout: publication # Do not change this
+group: publications # Do not change this
+title: "Traffic Network-Aware Energy Management for FCEVs: Integrating Trip-Specific Control and Long-Run Optimality" # Title of the paper
+# krtitle: # only for domestic papers
+authors: 
+  - name: "Kyunghwan Choi"
+    corresponding: true # true if this author is the corresponding author
+domestic_or_international: "International" # "International" or "Domestic"
+# preprint: # Preprint information - REMOVE THIS FIELD IF NOT APPLICABLE!
+#   - name: Techrxiv 
+#     doi: "10.36227/techrxiv.173014412.26480551/v1"
+#     year: 2024
+    # pdf: "/static/pub/2025-all-wheel.pdf"
+    # state: "published" # published, accepted, submitted
+pub: # Publication information - REMOVE THIS FIELD IF NOT APPLICABLE!
+  - name: "Asian Control Conference (ASCC)"
+    pdf: "/static/pub/2026-traffic-network.pdf"
+    doi: # Leave it blank if not applicable
+    vol: # Leave it blank if not applicable
+    num: # Leave it blank if not applicable
+    pp: # "380-385" # Leave it blank if not applicable
+    year: "2026" # Leave it blank if not applicable
+    state: "submitted" # published, accepted, submitted
+    bib: # "/static/pub/2025-imposing.bib" # Leave it blank if not applicable
+pub_date: "2026-02-16" # Date of publication. Change Techrxiv (or other preprint) date to Journal date once published.
+image: "/static/pub/2026-traffic-network.png" # Representative image of the paper
+abstract: "
+  Energy management for fuel cell electric vehicles (FCEVs) is a challenging trajectory optimization problem. Conventional studies primarily focus on trip-specific optimal control, where the power distribution is optimized based on a predicted finite-horizon driving profile. However, these methods often suffer from a limited look-ahead horizon and fail to guarantee long-run optimality within the stochastic traffic network where the vehicle operates. This study proposes a novel framework that integrates finite-horizon optimal control with traffic network-aware long-run average costs. We formulate the problem by embedding the long-run optimality, derived from network-level transition probabilities, into the terminal cost of the trip-specific optimization. This approach enables an adaptive target State of Charge (SOC) that aligns with global network efficiency while satisfying immediate driving constraints. Simulation results in a virtual traffic network demonstrate that the proposed integrated strategy consistently outperforms traditional trip-specific methods, achieving a maximum performance improvement of 11%. These findings highlight the necessity of network-level statistical awareness for maximizing the long-term energy efficiency of electrified mobility.
+"
+# additional: # additional information such as awards, etc.
+#  - "📄 Awarded **Best Paper Award** at the _2025 European Control Conference (ECC)_."
+# links: # additional links;
+#   - name: 
+#     url: 
+---