15 hasil untuk "Tim Brophy"
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Artikel Jurnal
Easy3D-Labels: Supervising Semantic Occupancy Estimation With 3D Pseudo-Labels for Automotive Perception
In perception for automated vehicles, safety is critical not only for the driver but also for other agents in the scene, particularly vulnerable road users such as pedestrians and cyclists. Previous representation methods, such as Bird's Eye View, collapse vertical information, leading to ambiguity…
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Artikel Jurnal
FIN: Fast Inference Network for Map Segmentation
Bird’s-Eye View (BEV) maps provide a top-down semantic representation of the driving environment, which is critical for downstream tasks such as planning and navigation in autonomous driving systems. However, generating accurate BEV maps from on-board sensors remains challenging due to perspective…
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Artikel Jurnal
Evaluating Small Vision-Language Models on Distance-Dependent Traffic Perception
Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on tasks that require both visual and textual understanding. Their strong generalisation abilities make them a promising component for automated driving systems, which must handle unexpected corner…
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Artikel Jurnal
A Study on the Impact of Rain on Object Detection for Automotive Applications
Visible spectrum cameras have emerged as a key technology in Advanced Driving Assistance Systems (ADAS) and automated vehicles. An important question to be answered is how these sensors perform in challenging adverse weather conditions, such as rain. Although progress has been made in determining…
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Artikel Jurnal
The Impact of Rain on Image Quality From Sensors on Connected and Autonomous Vehicles
As automated vehicles progress toward increasing levels of autonomy, the need for thorough testing of such systems in all relevant environments increases. These safety-critical systems often rely on visible-spectrum cameras to perceive the environment. Therefore, these systems must perform reliably…
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Artikel Jurnal
The Impact of Pose Alignment Errors on a Classical Late Infrastructure-Vehicle Collaboration Framework Using Experimental Data
Accurate perception and localization of dynamic objects is crucial for autonomous systems to navigate complex environments and avoid collisions. However, onboard sensing struggles with complex environments due to the challenges associated with occlusion, hindering progress in achieving advanced…
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Artikel Jurnal
Hyperspectral Sensors and Autonomous Driving: Technologies, Limitations, and Opportunities
Hyperspectral imaging (HSI) is a transformative sensing modality for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD). By capturing fine spectral resolution across hundreds of bands, HSI enables material-level scene understanding that overcomes critical limitations of…
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Artikel Jurnal
Information Capacity as a Predictor of Perception Performance
The design of automated driving systems is of growing industry and societal interest. Perception is a critical technology for these systems, which allows a vehicle to discern the surrounding environment. Perception systems in automated vehicles frequently use machine vision algorithms; however, the…
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Artikel Jurnal
Exploring Sensor Impact and Architectural Robustness in Adverse Weather on BEV Perception
Reliable perception in automated vehicles under adverse conditions, such as fog, rain, snow, and lens defocus, is essential for maintaining the safety of road actors and particularly of vulnerable road users. While prior work has primarily focused on camera occlusions, the impact on RADAR and LiDAR…
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Artikel Jurnal
A Review of the Impact of Rain on Camera-Based Perception in Automated Driving Systems
Automated vehicles rely heavily on image data from visible spectrum cameras to perform a wide range of tasks from object detection, classification, and avoidance to path planning. The availability and reliability of these sensors in adverse weather is therefore of critical importance to the safe…
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Artikel Jurnal
Analysis of the Impact of Rain on Perception in Automated Vehicle Applications
The reliable performance of object detection perception algorithms in automated vehicles under adverse conditions such as rain is critical for maintaining vulnerable road user safety. Visible-spectrum cameras provide a rich source of information and are cost-effective compared with other sensors…
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Artikel Jurnal
Optical Simulation and Image Quality for Automotive Semantic Segmentation
Cameras are often deployed in automated driving systems for multiple perception tasks, including object recognition. In previous work, a novel optical simulation framework, Superposition of Optical Lenses in Automotive Simulation (SOLAS), was proposed to simulate wide-angle and fisheye cameras on…
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Artikel Jurnal
G-MIND: Galway Multimodal Infrastructure Node Dataset for Intelligent Transportation Systems
Autonomous and semi-autonomous vehicles require accurate perception of their surrounding environment to ensure safe operation, yet onboard sensors frequently encounter occlusion challenges that result in incomplete dynamic environmental maps. Infrastructure-to-vehicle cooperative perception…
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Artikel Jurnal
Infrastructure Assisted Autonomous Driving: Research, Challenges, and Opportunities
Despite advancements in perception technology, achieving full autonomy in vehicles remains challenging partly due to limited situational awareness. Even with their sophisticated sensor arrays, autonomous vehicles often struggle to comprehend complex real-world environments due to the challenges…
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Artikel Jurnal
SOLAS 1.1: Automotive Optical Simulation in Computer Vision
Automotive datasets are typically captured using a small number of cameras, with each camera fixed at a single focus setting. In practice, however, camera modules exhibit unit-to-unit variability in their effective focus due to manufacturing tolerances. Since perception models are usually trained…