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Samyeul Noh

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Extendable Planning via Multiscale Diffusion

Chang Chen
Hany Hamed
Doojin Baek
Taegu Kang
Samyeul Noh
Yoshua Bengio
Sungjin Ahn

Long-horizon planning is crucial in complex environments, but diffusion-based planners like Diffuser are limited by the trajectory lengths observed during training. This creates a dilemma: long trajectories are needed for effective planning, yet they degrade model performance. In this paper, we introduce this extendable long-horizon planning challenge and propose a two-phase solution. First, Progressive Trajectory Extension incrementally constructs longer trajectories through multi-round compositional stitching. Second, the Hierarchical Multiscale Diffuser enables efficient training and inference over long horizons by reasoning across temporal scales. To avoid the need for multiple separate models, we propose Adaptive Plan Pondering and the Recursive HM-Diffuser, which unify hierarchical planning within a single model. Experiments show our approach yields strong performance gains, advancing scalable and efficient decision-making over long-horizons.

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IROS Conference 2018 Conference Paper

Probabilistic Collision Threat Assessment for Autonomous Driving at Road Intersections Inclusive of Vehicles in Violation of Traffic Rules

Samyeul Noh

In this paper, we propose a probabilistic collision threat assessment algorithm for autonomous driving at road intersections that assesses a given traffic situation at an intersection reliably and robustly for an autonomous vehicle to cross the intersection safely, even in the face of violation vehicles (that is, vehicles in violation of traffic rules at the intersection). To this end, the proposed algorithm employs a detailed digital map to predict future paths of observed vehicles and then utilizes the predicted future paths to identify potential threats (vehicles) and potential collision areas, regardless of whether observed vehicles are obeying traffic rules at the intersection. Next, by means of Bayesian networks and time window filtering under an independent and distributed reasoning structure, it assesses the potential threats regarding the possibility of collision reliably and robustly, even under uncertain and incomplete noise data. Then, it has been tested and evaluated through in-vehicle testing on a closed urban test road under traffic conditions inclusive of non-violation and violation vehicles. In-vehicle testing results show that the performance of the proposed algorithm is sufficiently reliable to be used in decision-making for autonomous driving at intersections in terms of reliability and robustness, even in the face of violation vehicles.

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ICRA Conference 2017 Conference Paper

Risk assessment for automatic lane change maneuvers on highways

Samyeul Noh
Kyounghwan An

This paper presents a risk assessment algorithm for automatic lane change maneuvers on highways. It is capable of reliably assessing a given highway situation in terms of the possibility of collisions and robustly giving a recommendation for lane changes. The algorithm infers potential collision risks of observed vehicles based on Bayesian networks considering uncertainties of its input data. It utilizes two complementary risk metrics (time-to-collision and minimal safety margin) in temporal and spatial aspects to cover all risky situations that can occur for lane changes. In addition, it provides a robust recommendation for lane changes by filtering out uncertain noise data pertaining to vehicle tracking. The validity of the algorithm is tested and evaluated on public highways in real traffic as well as a closed high-speed test track in simulated traffic through in-vehicle testing based on overtaking and overtaken scenarios in order to demonstrate the feasibility of the risk assessment for automatic lane change maneuvers on highways.

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