All-Weather Vision for Automotive Safety: Which Spectral Band?

Pinchon, Nicolas; Cassignol, Olivier; Nicolas, A Menzies; Bernardin, Frédéric; Leduc, P.; Tarel, Jean-Philippe; Brémond, Roland; Bercier, Emmanuel; Brunet, Johann

doi:10.1007/978-3-319-99762-9_1

Cited by 35 publications

(35 citation statements)

References 1 publication

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“…Like most visual pipelines, the investigated approaches will suffer from limited visibility. However, using cameras with extended spectral band sensitivity such as long-wave infrared (LWIR) shows potential to enable also good performance in bad weather conditions [53].…”

Section: Discussionmentioning

confidence: 99%

Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

Tschopp

Schneider

Palmer

et al. 2019

IEEE Robot. Autom. Lett.

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Today, rail vehicle localization is based on infrastructure-side Balises (beacons) together with on-board odometry to determine whether a rail segment is occupied. Such a coarse locking leads to a sub-optimal usage of the rail networks. New railway standards propose the use of moving blocks centered around the rail vehicles to increase the capacity of the network. However, this approach requires accurate and robust position and velocity estimation of all vehicles. In this work, we investigate the applicability, challenges and limitations of current visual and visual-inertial motion estimation frameworks for rail applications. An evaluation against RTK-GPS ground truth is performed on multiple datasets recorded in industrial, sub-urban, and forest environments. Our results show that stereo visual-inertial odometry has a great potential to provide a precise motion estimation because of its complementing sensor modalities and shows superior performance in challenging situations compared to other frameworks.

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Section: Discussionmentioning

confidence: 99%

Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

Tschopp

Schneider

Palmer

et al. 2019

IEEE Robot. Autom. Lett.

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“…Therefore, it is very important to use a vision system able to work under dark conditions. Furthermore, Pinchon et al [ 33 ] evaluated the relevance of four different spectral bands under adverse weather conditions: spectral band 0.4 to 0.65 μm (visible RGB), 0.4 to 1 μm, 0.6 to 1.7 μm and 8 to 12 μm and they obtained that the longest wavelength is the most robust against adverse weather conditions and that the NIR systems are better than visible RGB vision systems. Since FAR systems (long-wave infrared) are too expensive, our system is based on a NIR vision system.…”

Section: Methodsmentioning

confidence: 99%

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

González-de-Soto

Mora

Martín-Jiménez

et al. 2020

Sensors

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A new roadway eventual obstacle detection system based on computer vision is described and evaluated. This system uses low-cost hardware and open-source software to detect and classify moving elements in roads using infra-red and colour video images as input data. This solution represents an important advancement to prevent road accidents due to eventual obstacles which have considerably increased in the past decades, mainly with wildlife. The experimental evaluation of the system demonstrated that the proposed solution detects and classifies correctly different types of moving obstacles on roads, working robustly under different weather and illumination conditions.

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“…Near Infrared (NIR) (750-900 nm) complements visible spectrum with a better contrast in high dynamic range scenes, and better night visibility. In [12] authors compare visible light, NIR and FIR cameras under different light and atmospheric conditions.…”

Section: A Artificial Visionmentioning

confidence: 99%

A Review of Sensor Technologies for Perception in Automated Driving

Martí

Miguel

Fernández

et al. 2019

IEEE Intell. Transport. Syst. Mag.

236

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After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show the intention of the market in sensors technologies for Automated Vehicles.

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All-Weather Vision for Automotive Safety: Which Spectral Band?

Cited by 35 publications

References 1 publication

Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

Experimental Comparison of Visual-Aided Odometry Methods for Rail Vehicles

A New Roadway Eventual Obstacle Detection System Based on Computer Vision

A Review of Sensor Technologies for Perception in Automated Driving

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