arXiv subjects

Shuocheng Yang

Publications and source records attributed to Shuocheng Yang.

7 recordsLinked to original sources

arXiv2026

Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception

Cooperative 3D perception via Vehicle-to-Everything communication is a promising paradigm for enhancing autonomous driving, offering extended sensing horizons and occlusion resolution. However, the practical deployment of existing methods is hindered at long distances by two critical bottlenecks: the quadratic computational scaling of dense BEV representations and the fragility of feature association mechanisms under significant observation and alignment errors. To overcome these limitations, we introduce Long-SCOPE, a fully sparse framework designed for robust long-distance cooperative 3D perception. Our method features two novel components: a Geometry-guided Query Generation module to accurately detect small, distant objects, and a learnable Context-Aware Association module that robustly matches cooperative queries despite severe positional noise. Experiments on the V2X-Seq and Griffin datasets validate that Long-SCOPE achieves state-of-the-art performance, particularly in challenging 100-150 m long-range settings, while maintaining highly competitive computation and communication costs.

cs.CV↗

arXiv2026

Equi-RO: A 4D mmWave Radar Odometry via Equivariant Networks

Autonomous vehicles and robots rely on accurate odometry estimation in GPS-denied environments. While LiDARs and cameras struggle under extreme weather, 4D mmWave radar emerges as a robust alternative with all-weather operability and velocity measurement. In this paper, we introduce Equi-RO, an equivariant network-based framework for 4D radar odometry. Our algorithm pre-processes Doppler velocity into invariant node and edge features in the graph, and employs separate networks for equivariant and invariant feature processing. A graph-based architecture enhances feature aggregation in sparse radar data, improving inter-frame correspondence. Experiments on an open-source dataset and a self-collected dataset show Equi-RO outperforms state-of-the-art algorithms in accuracy and robustness. Overall, our method achieves 10.7% and 13.4% relative improvements in translation and rotation accuracy, respectively, compared to the best baseline on the open-source dataset.

cs.RO↗

arXiv2025

RaFD: Flow-Guided Radar Detection for Robust Autonomous Driving

Radar has shown strong potential for robust perception in autonomous driving; however, raw radar images are frequently degraded by noise and "ghost" artifacts, making object detection based solely on semantic features highly challenging. To address this limitation, we introduce RaFD, a radar-based object detection framework that estimates inter-frame bird's-eye-view (BEV) flow and leverages the resulting geometric cues to enhance detection accuracy. Specifically, we design a supervised flow estimation auxiliary task that is jointly trained with the detection network. The estimated flow is further utilized to guide feature propagation from the previous frame to the current one. Our flow-guided, radar-only detector achieves achieves state-of-the-art performance on the RADIATE dataset, underscoring the importance of incorporating geometric information to effectively interpret radar signals, which are inherently ambiguous in semantics.

cs.RO↗

arXiv2025

RINO: Accurate, Robust Radar-Inertial Odometry with Non-Iterative Estimation

Odometry in adverse weather conditions, such as fog, rain, and snow, presents significant challenges, as traditional vision and LiDAR-based methods often suffer from degraded performance. Radar-Inertial Odometry (RIO) has emerged as a promising solution due to its resilience in such environments. In this paper, we present RINO, a non-iterative RIO framework implemented in an adaptively loosely coupled manner. Building upon ORORA as the baseline for radar odometry, RINO introduces several key advancements, including improvements in keypoint extraction, motion distortion compensation, and pose estimation via an adaptive voting mechanism. This voting strategy facilitates efficient polynomial-time optimization while simultaneously quantifying the uncertainty in the radar module's pose estimation. The estimated uncertainty is subsequently integrated into the maximum a posteriori (MAP) estimation within a Kalman filter framework. Unlike prior loosely coupled odometry systems, RINO not only retains the global and robust registration capabilities of the radar component but also dynamically accounts for the real-time operational state of each sensor during fusion. Experimental results conducted on publicly available datasets demonstrate that RINO reduces translation and rotation errors by 1.06% and 0.09°/100m, respectively, when compared to the baseline method, thus significantly enhancing its accuracy. Furthermore, RINO achieves performance comparable to state-of-the-art methods.

cs.RO↗

arXiv2025

Controllable Radar Simulation with Waveform Parameter Embedding

Autonomous driving simulators still lack high-fidelity radar, even though radar is critical for robust perception in adverse weather. A key obstacle is that raw radar point clouds are extremely sparse and stochastic, making it difficult to model; we argue that simulating the full range-azimuth-Doppler cube is a more principled target. Existing radar cube simulators either rely purely on neural generators, which are opaque and offer little control over sensor attributes, or on detailed electromagnetic pipelines, which are slow, require proprietary hardware specifications, and still struggle to capture real-world complexity. We introduce Ctrl-RS, a controllable radar cube simulation framework that combines the strengths of both worlds. First, we build an environment reflection tensor from diverse sensor sources (including LiDAR, monocular cameras, and existing radar). Second, we abstract radar physics into a compact set of waveform parameters that characterize the 3D point spread function, yielding an intuitive embedding of radar attributes such as range resolution, Doppler broadening, and azimuth beam shape. Third, we train a WARP-Net on a large mixed dataset that fuses real, analytically synthesized, and simulator-generated radar cubes to cover a wide distribution of radar attributes. Ctrl-RS supports viewpoint changes, actor removal, and attribute editing. Experiments on RADDet, Carrada, and nuScenes show that our simulated data can match or surpass real radar in 2D detection and semantic segmentation, and consistently boosts performance in 3D detection when combined with real data. The Project is available at https://github.com/zhuxing0/Ctrl-RS.

eess.SP↗

arXiv2024

4D Millimeter-Wave Radar in Autonomous Driving: A Survey

The 4D millimeter-wave (mmWave) radar, proficient in measuring the range, azimuth, elevation, and velocity of targets, has attracted considerable interest within the autonomous driving community. This is attributed to its robustness in extreme environments and the velocity and elevation measurement capabilities. However, despite the rapid advancement in research related to its sensing theory and application, there is a conspicuous absence of comprehensive surveys on the subject of 4D mmWave radar. In an effort to bridge this gap and stimulate future research, this paper presents an exhaustive survey on the utilization of 4D mmWave radar in autonomous driving. Initially, the paper provides reviews on the theoretical background and progress of 4D mmWave radars, encompassing aspects such as the signal processing workflow, resolution improvement approaches, and extrinsic calibration process. Learning-based radar data quality improvement methods are present following. Then, this paper introduces relevant datasets and application algorithms in autonomous driving perception, localization and mapping tasks. Finally, this paper concludes by forecasting future trends in the realm of 4D mmWave radar in autonomous driving. To the best of our knowledge, this is the first survey specifically dedicated to the 4D mmWave radar in autonomous driving.

eess.SP↗

arXiv2021

Image-based study of granular column collapse over controlled-roughness surfaces

Basal effects have important implications for the high mobility and long runout of granular flows such as rock avalanches and landslides. However, fundamental understanding of the basal effect in granular flows remains challenging due to the complex forms of base roughness and the multiscale nature of flow-bed interactions. Here we experimentally investigate the basal effect in granular column collapse over controlled-roughness bases. Image processing methods are developed to obtain robust measurements of base roughness, runout distance and deposit morphology. A geometric roughness parameter Ra is applied to consider both the size and spatial distribution of base particles, which enables systematic analysis of the basal effect. The results indicate that the runout distance can be characterized as a function of Ra, regardless of the variations in the base particle size and spacing, and the roughness has a major influence on the frontal region of the granular flow, as well as the overall profile of the granular deposit. When Ra is increased beyond a threshold value (Ra > 0.62), flow characteristics show minor changes, which coinsides with a previous phase diagram for the transition between slip and non-slip boundary conditions from steady state granular flow simulations.

physics.geo-ph↗