Institution profile

Ajou University

Academic institutionasia · kr
Official website
Research library80linked papers
Opportunities0open roles
Selected work

Representative Papers

Anti-bullying Adaptive Cruise Control: A proactive right-of-way protection approach

Dec 14, 2024arXiv.org

Existing adaptive cruise control (ACC) systems exhibit weak right-of-way protection under close-range cut-in maneuvers. This paper proposes Bullying-Resistant Adaptive Cruise Control (AACC), the first framework integrating online inverse optimal control (IOC)-driven driving style identification with Stackelberg game-theoretic interactive motion planning to enable real-time, personalized right-of-way preservation in right-hand traffic. Methodologically, AACC employs IOC to estimate the leading vehicle’s driving style online and formulates a Stackelberg game wherein the ego vehicle acts as leader and the cut-in vehicle as follower, yielding robust defensive trajectory plans. Experimental results demonstrate a substantial improvement in cut-in defense success rate, with safety and ride comfort enhanced by 79.8% and 20.4%, respectively, and traffic flow efficiency increased by 19.33%. Each planning step completes in under 50 ms, confirming feasibility for embedded real-time deployment.

3 citationsRead paper

Pontryagin-Guided Deep Learning for Large-Scale Constrained Dynamic Portfolio Choice

Jan 22, 2025

This paper addresses the joint optimization of portfolio allocation and consumption under continuous-time constraints at large scale. Conventional dynamic programming approaches are severely limited by the “curse of dimensionality,” typically handling no more than seven assets. To overcome this, we propose a novel end-to-end framework that integrates Pontryagin’s Maximum Principle (PMP) with neural network–based gradient optimization, ensuring strict adherence to realistic economic constraints—including short-selling limits, borrowing restrictions, and budget constraints. Our method parameterizes control policies via deep neural networks, leverages PMP for theoretical guidance, employs direct policy optimization, and exploits GPU parallelization—bypassing computationally intensive PDE or BSDE modeling and grid-based discretization. Experiments demonstrate exact recovery of analytical solutions in the unconstrained case with 1,000 assets; rapid convergence under complex constraints; and near-optimal performance achieved within just 1–2 minutes of GPU training—substantially advancing the state-of-the-art in both scalability and computational efficiency.

1 citationsRead paper
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