arXiv subjects

Jinhan Lee

Publications and source records attributed to Jinhan Lee.

4 recordsLinked to original sources

arXiv2026

Open-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints

Bird's-eye view (BEV) perception fuses multi-camera images into a unified top-down representation for autonomous driving. Despite recent progress, state-of-the-art methods remain confined to closed-set scenarios, making them vulnerable to unpredictable real-world environments. In this work, we introduce open-vocabulary BEV segmentation (OVBS), which leverages vision-language models (VLMs) to recognize categories beyond the training set while maintaining precise BEV perception and real-time efficiency. A key challenge in OVBS lies in the 3D geometric inconsistency inherent in the ill-posed lifting of 2D VLM semantics into BEV. To address this, we propose OVBEVSeg, a geometry-aware OVBS framework that enhances efficient Gaussian splatting (GS)-based unprojection by leveraging robust 3D geometric constraints across three progressive stages: (1) 2D-to-BEV pseudo-labeling via reliable 3D projection for OV generalization; (2) joint 2D-BEV per-scene optimization with BEV structural constraints for 3D geometric consistency; and (3) 3D geometric distillation for online efficiency. On the nuScenes dataset, OVBEVSeg achieves state-of-the-art performance, outperforming closed-set methods by 15.3 mIoU on unseen categories. Remarkably, even with no novel-class ground-truth labels, it remains competitive with self- and semi-supervised baselines trained with up to 40% of ground-truth annotations. Furthermore, it achieves 2.5x faster inference with only 0.22x the memory consumption of projection-based methods. Project page: https://hchoi256.github.io/projects/ovbevseg/.

cs.CV↗

arXiv2026

Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation

Realistic and diverse traffic simulation is essential to autonomous driving development. Yet prevailing benchmarks predominantly reward realism, and recent methods have optimized accordingly, leaving diversity underexplored. We introduce \textbf{Flow-ERD}, a multi-agent simulator that pursues realism and diversity jointly. Its backbone, \textbf{Agent-Type Aware Flow Matching} (AFM), couples flow matching's multi-modal expressiveness with type-specific kinematic execution. It preserves fine-grained diversity while keeping motions consistent with each agent type. A second stage, \textbf{Entropy-Regularized Distillation} (ERD), fine-tunes the closed-loop rollout distribution with an entropy-regularized reverse-KL objective. This mitigates covariate shift while explicitly preventing collapse onto high-density modes. We evaluate Flow-ERD with a log-free diversity metric alongside standard realism scores. Flow-ERD ranks first on the WOSAC test benchmark and dominates the realism--diversity Pareto front among reproducible baselines. Our project page is available \href{https://seulbinhwang.github.io/flow-erd-project-page/}{here}.

cs.RO↗

arXiv2026

Shallow Trap States Control Electrical Performance of Amorphous Oxide Semiconductor Thin-Film Transistors

The performance of n-type amorphous oxide semiconductor thin-film transistors (TFTs) is largely controlled by the density of states (DoS) near the conduction band mobility edge. Here, the full subgap DoS of amorphous InGaZnO (a-IGZO) TFTs, used in display panels and dynamic random-access memory (DRAM) development, is measured by ultrabroadband photoconduction (UP-DoS) microscopy to within 0.1 eV of the mobility edge. The measured subgap DoS for 25 TFT processing conditions accurately predicts each transfer curve, showing how shallow defect states are electron traps that rigidly tune subthreshold swing, threshold voltage and drift mobility. For a set of TFTs, the subthreshold transfer characteristics can be independently simulated from the experimental shallow defect DoS, with no adjustable parameters. The full transfer curve is simulated by introducing a single parameter: the conduction band tail energy. Additionally, the simulation reveals that the shallow trap density controlling subthreshold behavior can be directly extracted from transfer curves. Finally, a systematic In-enrichment study, combined with DFT+U DoS simulations, enables identification of vacancy cation coordination environments for all experimentally observed subgap peaks. The dominant trap controlling conventional a-IGZO TFT performance is centered at ~0.32 eV below the conduction band mobility edge and is assigned to a Ga-Ga-In oxygen vacancy defect.

physics.app-ph↗

arXiv2025

Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation

Diffusion-based models are recognized for their effectiveness in using real-world driving data to generate realistic and diverse traffic scenarios. These models employ guided sampling to incorporate specific traffic preferences and enhance scenario realism. However, guiding the sampling process to conform to traffic rules and preferences can result in deviations from real-world traffic priors and potentially leading to unrealistic behaviors. To address this challenge, we introduce a multi-guided diffusion model that utilizes a novel training strategy to closely adhere to traffic priors, even when employing various combinations of guides. This model adopts a multi-task learning framework, enabling a single diffusion model to process various guide inputs. For increased guided sampling precision, our model is fine-tuned using the Direct Preference Optimization (DPO) algorithm. This algorithm optimizes preferences based on guide scores, effectively navigating the complexities and challenges associated with the expensive and often non-differentiable gradient calculations during the guided sampling fine-tuning process. Evaluated using the nuScenes dataset our model provides a strong baseline for balancing realism, diversity and controllability in the traffic scenario generation.

cs.LG↗