arXiv subjects

Zhiran Yan

Publications and source records attributed to Zhiran Yan.

6 recordsLinked to original sources

arXiv2026

Hardware-triggered Time Synchronization of Roadside Multi-lidar, Multi-camera Measurement System for Accurate Data Alignment

Accurate temporal alignment of heterogeneous sensors is necessary for reliable environment perception in roadside multi-lidar, multi-camera systems, particularly in dense urban traffic. For this purpose, an open-source, simple, modular, and configurable hardware-triggered time-synchronization circuit is presented in this work to perform temporal alignment or accurate time synchronization between a lidar and multiple cameras. In the designed circuit, a lidar synchronization pulse is used as a reference input, and independently programmable, time-delayed trigger pulses are generated for each camera, allowing flexible adaptation to varying sensor setups and mounting geometries. A series of experiments is conducted on a roadside-mounted perception system comprised of lidar and three cameras, in which the trigger delay is systematically varied, and its impact on spatial-temporal alignment is evaluated. For different classes of road users, the overlap between lidar point cloud measurements and camera measurements is quantified to identify delay configurations that maximize cross-sensor consistency. The proposed circuit is shown to achieve robust and repeatable synchronization while remaining straightforward to deploy, reconfigure, and extend due to its simple and open-source design. Following validation on a three-camera roadside system, the circuit is extended to a vehicle platform with seven cameras and a lidar, providing a low-cost, extensible solution for multi-sensor synchronization across infrastructure and vehicle setups. All hardware circuit design files and source codes are available at https://github.com/shiva-THI/hardware-trigger-time-sync-lidar-cameras.

cs.CV↗

arXiv2026

Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Driving

Traditional semantic segmentation models operate under a closed-set assumption and struggle to recognize unknown or unexpected objects-an essential capability for autonomous driving. As a result, such models often misclassify or overlook out-of-distribution (OOD) road anomalies, posing safety risks in open-world environments. We present a lightweight, postprocessing, road-aware anomaly segmentation framework that requires no retraining, no OOD data, and no auxiliary supervision. Our approach builds on a mask transformer-based segmentation network by exploiting query-level mask confidence and deriving a polygonal road prior to detect gap regions that may correspond to anomalies. To further suppress false positives, we introduce a CLIP-based zero-shot semantic filtering module using in-distribution prompts, with optional generalized OOD prompts. By jointly leveraging spatial priors and semantic verification, our framework produces robust and interpretable anomaly predictions. Evaluation on three public benchmarks-Fishyscapes, SMIYC, and RoadAnomaly-shows consistently strong performance. In particular, our method outperforms the training-free baseline Maskomaly on most metrics and achieves the highest AP on Fishyscapes LostAndFound. These results demonstrate the practicality and deployability of our approach for real-world autonomous driving systems.

cs.CV↗

arXiv2026

Framework and Multi-modal Dataset for Roadwork Zone Detection and Geo-localization

Autonomous vehicles often rely on high-definition (HD) maps for navigation; however, these maps are not frequently updated and often lack semi-static information, such as temporary roadwork zones, which can significantly alter the road network. This limitation underscores the urgent need for an accurate global position of roadwork zones. However, the absence of publicly available datasets for evaluating roadwork zone detection and geo-localization models has hindered the development of reliable autonomous driving systems. To address this challenge, we propose the Roadwork Zone Detection and Geo-localization (RZDG) dataset, which includes both simulated and real-world data, providing multimodal sensor inputs along with comprehensive annotations. The dataset supports multiple perception tasks, including image semantic segmentation, 3D object detection, and object geo-localization. In addition, we introduce a tracker-based roadwork zone detection and geo-localization (RZDG) pipeline, an extension of AB3DMOT, for accurate object geo-localization in roadwork zones. We benchmark our approach on the RZDG dataset, demonstrating its effectiveness in detecting roadwork zones and transforming object positions from the local coordinate system to the global coordinate system. A prediction is considered a true positive (TP) if its estimated position falls within one meter of the ground truth. Our experimental results show that our approach achieves high accuracy on both real and simulated data. Specifically, we report: Precision: 0.565 (real) / 0.615 (simulated) Recall: 0.898 (real) / 0.809 (simulated) F1-score: 0.597 (real) / 0.665 (simulated).

cs.CV↗

arXiv2024

Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles

Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments.

cs.CV↗

arXiv2022

In-situ synchrotron based high energy X-ray diffraction study of the deformation mechanism of δ-hydride in a commercially pure titanium

We used by in-situ high energy X-ray diffraction to inestigate the deformation behavior of Grade 2 commercially pure titanium that was hydrogen charged to form hydrides. The results showed that the peak broadening in the diffraction patterns are due to the high internal and interphase stresses generated within and around hydrides due to the volume expansion induced by the phase transformation. The hydrides exhibit typical high strength but brittle secondary phase behavior, which undertakes more elastic strain than matrix and is the location where cracks are first generated. Interestingly, the δ-hydrides sustain larger strains than the matrix, especially after the matrix yields. This study on the deformation mechanism of hydrides in pure titanium provides insight into the hydride deformation behavior and hydrogen embrittlement in both titanium and zirconium.

cond-mat.mtrl-sci↗

arXiv2022

The role of \b{eta} pockets resulting from Fe impurities in hydride formation in titanium

The corrosion potential of commercially pure titanium in NaCl solutions is dramatically affected by trace Fe additions, which cause the appearance of submicron pockets of \b{eta} phase at grain boundary triple points. Furthermore, the low solubility of hydrogen in hexagonal close-packed α-Ti makes titanium alloys prone to subsequent hydride-associated failures due to stress corrosion cracking. We analyzed α-α and α-\b{eta} sections of the abutting grain boundary of a \b{eta} pocket in a Grade 2 CP-Ti, and the α-\b{eta} phase boundary. Fe and H partition to \b{eta} and segregate at the grain boundary, but no segregation is seen at the α-\b{eta} phase boundary. In contrast, a significant Ni (>1 at%) accumulation is observed at the α-\b{eta} phase boundary. We propose that the \b{eta}-pockets act as hydrogen traps and facilitate the nucleation and growth of hydrides along grain boundaries in CP-Ti.

cond-mat.mtrl-sci↗