← Search

Heejin Ahn

7 accepted papers

2026

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving

RA-L 2026

Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on these inference results poses significant safety risks due to inherent uncertainties. To address this challenge, we prese

Cited by 1SourceScholar
2026

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving

ICRA 2026poster

Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on these inference results poses significant safety risks due to inherent uncertainties. To address this challenge, we prese…

2026

Miniature Testbed for Validating Multi-Agent Cooperative Autonomous Driving

ICRA 2026poster

Cooperative autonomous driving, which extends vehicle autonomy by enabling real-time collaboration between vehicles and smart roadside infrastructure, remains a challenging yet essential problem. However, none of the existing testbeds employ smart infrastructure equipped with sensing, edge computing…

2026

Uncertainty Estimation via Hyperspherical Confidence Mapping

ICLR 2026poster

Quantifying uncertainty in neural network predictions is essential for deploying models in high-stakes domains such as autonomous driving, healthcare, and manufacturing. While conventional approaches often depend on costly sampling or parametric distributional assumptions, we propose Hyperspherical…

Cited by 0SourceScholar
2025

A Computation-Efficient Method of Measuring Dataset Quality based on the Coverage of the Dataset

AISTATS 2025poster

Evaluating dataset quality is an essential task, as the performance of artificial intelligence (AI) systems heavily depends on it. A traditional method for evaluating dataset quality involves training an AI model on the dataset and testing it on a separate test set. However, this approach requires s…

Cited by 0SourceScholar
2017

Duckietown: An open, inexpensive and flexible platform for autonomy education and research

ICRA 2017poster

Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies…

Cited by 281SourceScholar
2015

Experimental testing of a semi-autonomous multi-vehicle collision avoidance algorithm at an intersection testbed

IROS 2015poster

This paper describes the implementation of a multi-vehicle supervisor to prevent collisions at intersections. The experiments are performed on an intersection testbed consisting of three RC cars. Here, we account for uncertainty in car dynamics and state measurement, and the presence of an uncontrol…

Cited by 32SourceScholar