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20192021
most citedA Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

171 citations · 255 across the 4 of their papers we have counts for

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10 papers · 1 filter

cs.CV202435 cited

The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data

Florian Kraus, Nicolas Scheiner, Werner Ritter +1

Radar sensors have a long tradition in advanced driver assistance systems (ADAS) and also play a major role in current concepts for autonomous vehicles. Their importance is reasone…

cs.CV2021

A Benchmark for Spray from Nearby Cutting Vehicles

Stefanie Walz, Mario Bijelic, Florian Kraus +3

Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas. To increase driving safety in adverse weath…

cs.CV20212 cited

ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media

Zheng Shi, Ethan Tseng, Mario Bijelic +2

Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe…

cs.CV2020

Using Machine Learning to Detect Ghost Images in Automotive Radar

Florian Kraus, Nicolas Scheiner, Werner Ritter +1

Radar sensors are an important part of driver assistance systems and intelligent vehicles due to their robustness against all kinds of adverse conditions, e.g., fog, snow, rain, or…

cs.CV2019171 cited

A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

Mario Bijelic, Tobias Gruber, Werner Ritter

Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the…

cs.CV2019

Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar

Nicolas Scheiner, Florian Kraus, Fangyin Wei +8

Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Non-line-of-sight (…