Multi-sensor fusion for autonomous driving — combining LiDAR, camera, radar and ultrasonic sensors for robust all-weather environment perception.
An autonomous driving research paper: Unified Spatio-Temporal BEV Attention for Omniscient Autonomous Driving with Multi-Sensor Fusion.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: Design of a Deep Learning-Based Intelligent Driving Decision System for Vehicle Engineering and Adaptability Verification in Complex Road Conditions.
Engineering 5.5 · Research 7.0 · Business 5.0
The rapid development of new energy vehicles (NEVs) has intensified the demand for accurate and reliable monitoring of both emission-related performance and powertrain health.
Engineering 5.5 · Research 7.0 · Business 5.5
In this paper, we propose Clear2Fog (C2F), an end-to-end, physics-based pipeline that simulates fog for clear-weather datasets under a unified camera and LiDAR framework.
Engineering 6.5 · Research 8.0 · Business 6.5
An autonomous driving research paper: Reliability-Aware Sensor Fusion via Bidirectional Diffusion for Robust Robot Odometry.
Engineering 5.0 · Research 7.0 · Business 5.0
To address this limitation, we propose CRUISE, a novel uncertainty-aware cross-modal sensor fusion framework.
Engineering 5.5 · Research 8.0 · Business 5.5
In this work, we present a dedicated approach to addressing these challenges.
Engineering 5.0 · Research 7.0 · Business 5.0
This work presents a detailed study on passive wireless sensor network tracking for adaptive estimation and control.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper presents a Multi-Modal Edge AI System for Real-Time Road Health Diagnostics Using Vision-Acoustic Sensor Fusion.
Engineering 5.0 · Research 7.0 · Business 5.0
The continued development of embodied intelligence and autonomous driving technologies necessitates comprehensive multi-dimensional environmental perception.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose a joint embedding model driven by cross-modal contrastive learning to realize the dynamic coupling of acoustic physical-perceptual features and visual global-local semantic features.
Engineering 5.0 · Research 7.0 · Business 5.0
To bridge this gap, we introduce Talk2Sensors, the first multi-sensor 3D visual grounding dataset built upon camera, LiDAR, and 4D radar.
Engineering 5.5 · Research 8.0 · Business 5.5
Car-STAGE is a Windows desktop application for generating annotated autonomous-driving datasets from the CARLA simulator (v0.9.15).
Engineering 6.5 · Research 7.0 · Business 5.0
We introduce Radar4D-VLM, a radar-only temporal vision-language model that reasons from ten consecutive 4D-radar point-cloud sweeps without camera or LiDAR input.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we deeply dig into the theories and existing strategies utilized in both optimization-based and filtering-based approaches.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper presents the latent model-joint embedding predictive architecture (LM-JEPA), a resource-efficient collaborative perception framework for connected and autonomous vehicles that integrates latent predictive representation learning with lightweight multi-modal reasoning.
Engineering 6.0 · Research 7.5 · Business 6.0
(2) We propose geometric saliency pillar feature encoding, which enhances point cloud structural representation via point-wise saliency weighting and multi-statistic aggregation.
Engineering 5.5 · Research 7.0 · Business 5.0
To address these limitations, we introduce a novel multi-modal fusion network for 3D object detection, consisting of two principal components: Geometric Pseudo-Image Feature Fusion and the Feature Fusion Encoder.
Engineering 5.5 · Research 8.0 · Business 5.0
In this paper, we improve the performance of point-based methods by effectively learning features from 2D representations through point–plane projections, enabling the extraction of complementary information while relying solely on LiDAR data.
Engineering 6.5 · Research 7.0 · Business 5.0
To achieve this, we propose the Reference Dataset Alignment Method (ReDAM) for weather intensity alignment in fog and Unified-weather-edit (inspired by Weather-edit[1]) for particle positioning alignment in rain and snow.
Engineering 5.5 · Research 7.0 · Business 5.5