LiDAR-based 3D perception for autonomous driving — point cloud processing, 3D detection, segmentation, compression and sensor fusion with cameras and radar.
To overcome this, we introduce SimBEV2X, an advanced synthetic data generation tool built on the CARLA simulator.
Engineering 7.0 · Research 8.0 · Business 5.5
In this study, we present a sensor-based model predictive control (MPC) scheme designed for safe crowd navigation in such non-convex environments.
Engineering 5.5 · Research 7.0 · Business 5.5
The Shunde Lantern-Wine Festival (“Yin Deng Jiu”) in South China presents a compelling case of community-led food heritagization, having transformed from a clan-based ritual into a large-scale charitable event centered on communal banquets and lantern auctions.
Engineering 5.0 · Research 7.0 · Business 6.0
The commercialization of autonomous driving relies heavily on reliable environmental perception in all-weather and all-scenario conditions.
Engineering 6.0 · Research 7.0 · Business 6.0
To address the limitations of traditional multimodal fusion methods—such as insufficient modeling capabilities for dynamic targets, lack of adaptability in fusion weights, and weak temporal consistency-we propose a motion-saliency-guided multimodal temporal fusion method.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: 3D point cloud processing and analysis: a survey.
Engineering 5.0 · Research 7.0 · Business 5.0
We introduce SpecEOT, a source-agnostic and graph-spectral expectation-over-transformation attack.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper addresses the inherent structural incompatibility between continuous visual manifolds and discrete LiDAR simplicial complexes in cross-modal perception fusion.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper proposes loosely coupled fusion methods integrating an SCR model with SLAM to improve localization accuracy and robustness.
Engineering 5.0 · Research 7.0 · Business 5.0
We present urban graph, which combines overhead EO priors, vehicle observations, and fixed roadside anchors in a hierarchical semantic scene graph.
Engineering 5.0 · Research 7.0 · Business 5.0
To overcome these limitations, we propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion.
Engineering 5.5 · Research 7.0 · Business 6.5
We propose Li-ViP3D++, a query-based multimodal PnP framework that introduces Query-Gated Deformable Fusion (QGDF) to integrate multi-view RGB and LiDAR in query space.
Engineering 6.0 · Research 7.0 · Business 5.0
In this paper, we present Sarus, a privacy-preserving framework for multi-vendor perception fusion via homomorphic encryption (HE), enabling aggregation without revealing individual vendor outputs.
Engineering 5.5 · Research 7.0 · Business 6.0
Autonomous driving has become a transformative technology poised to reshape modern transportation systems.
Engineering 5.5 · Research 7.0 · Business 5.0
This paper proposes a fast grid-based ground segmentation method for 3D Light Detection and Ranging (LiDAR) point clouds using dual-seed expansion.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these limitations, this paper proposes an end-to-end traffic scene recognition network based on the fusion of monocular camera images and corresponding road map top-down view data.
Engineering 5.5 · Research 7.0 · Business 5.0
Inspired by recent gated-vision and LiDAR fusion research, this paper proposes WeatherPrompt-Fusion, a prompt-guided multi-modal perception framework that converts compact weather descriptions into modality-reliability gates for camera/gated image, LiDAR, and radar features.
Engineering 5.5 · Research 7.0 · Business 5.0
This paper proposes UA-CF, an uncertainty-aware camera-LiDAR fusion framework that dynamically allocates feature-level weights according to modality reliability.
Engineering 5.0 · Research 7.0 · Business 5.0
DeeperRadar is a radar-centric, sensor-stack-conditioned framework that co-designs radar sensing and multi-modal 3D detection for autonomous mobility by learning a sparse acquisition pattern end-to-end with the fusion model.
Engineering 6.0 · Research 7.0 · Business 6.0
In this work, we present TempoCross, a 3D detection method based on instance-aware sparse representations for multimodal temporal fusion.
Engineering 5.5 · Research 7.0 · Business 5.0