Multi-sensor fusion for autonomous driving — combining LiDAR, camera, radar and ultrasonic sensors for robust all-weather environment perception.
This study investigates the role and methodological relevance of simulation-based approaches in transportation safety research, with particular emphasis on their applicability to pedestrian safety.
Engineering 5.0 · Research 7.0 · Business 5.0
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 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
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
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
To enhance unmanned ground vehicle (UGV) intelligence in smart cities, disaster rescue, and infrastructure inspection, this paper investigates the collaborative optimization of multimodal fusion end-to-end architectures.
Engineering 6.5 · Research 7.0 · Business 5.5
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
Future traffic scenarios will involve autonomous vehicles (AVs) interacting with other road users, including cyclists.
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
An autonomous driving research paper: Cross-Attention Based Multi-Sensor Fusion for Robust Object Detection in ADAS: YOLOv12 with Spatiotemporal Calibration and iHOA-Tuned Temporal Fusion Transformer.
Engineering 5.0 · Research 7.0 · Business 5.5
Autonomous vehicles (AVs) represent a foundational cornerstone of future smart city transportation systems, offering the potential to eliminate human driving errors, reduce traffic fatalities by at least 40%, and optimize energy consumption.
Engineering 6.5 · Research 7.0 · Business 7.0
This paper presents an integrated autonomous vehicle framework combining Bird’s-Eye View (BEV) trimodal sensor fusion, Bi-LSTM temporal tracking, and adaptive Human Machine Interface (HMI) optimization using the nuScenes dataset.
Engineering 5.5 · Research 7.0 · Business 5.0
The protection of Vulnerable Road Users (VRUs) remains a major challenge in modern transportation safety, as onboard line-of-sight and adverse weather conditions limit conventional onboard sensors.
Engineering 5.5 · Research 7.0 · Business 5.0