Autonomous Driving in the Real-World: The Weather Challenge in the Sohjoa Baltic Project

Bellone, Mauro; Ismailogullari, Azat; Müür, Jaanus; Nissin, Oscar; Sell, Raivo; Soe, Ralf-Martin

doi:10.1007/978-3-030-66042-0_9

Cited by 12 publications

(9 citation statements)

References 43 publications

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“…The physical validation was carried out in January 2022. The initial scenario was simplified due to the harsh weather conditions, which are a well-known challenge for AVs [44]. Roads were narrowed by snow and huge snow piles were blocking the visual view as well as LiDAR field of view.…”

Section: Physical Validation Resultsmentioning

confidence: 99%

PolyVerif: An Open-Source Environment for Autonomous Vehicle Validation and Verification Research Acceleration

et al. 2023

Self Cite

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Validation and Verification (V&V) of Artificial Intelligence (AI) based cyber physical systems such as Autonomous Vehicles (AVs) is currently a vexing and unsolved problem. AVs integrate subsystems in areas such as detection, sensor fusion, localization, perception, and path planning. Each of these subsystems contains significant AI content integrated with traditional hardware and software components. The complexity for validating even a subsystem is daunting and the task of validating the whole system is nearly impossible. Fundamental research in advancing the state-of-the-art for AV V&V is required. However, for V&V researchers, it is exceedingly difficult to make progress because of the massive infrastructure requirements to demonstrate the viability of any solution. This paper presents PolyVerif, the world's first open-source solution focused on V&V researchers with the objective of accelerating the state-of-the-art for AV V&V research. PolyVerif provides an AI design and verification framework consisting of a digital twin creation process, an open-source AV engine, access to several open-source physics based simulators, and open-source symbolic test generation engines.PolyVerif's objective is to arm V&V researchers with a framework which extends the state-of-the-art on any one of the many major axes of interest and use the remainder of the infrastructure to quickly demonstrate the viability of their solution. Given its open-source nature, researchers can also contribute their innovations to the project. Using this critical property of open-source environments, the innovation rate of the whole research community to solve these vexing issues can be greatly accelerated. Finally, the paper also presents results from several projects which have used PolyVerif.

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Section: Physical Validation Resultsmentioning

confidence: 99%

PolyVerif: An Open-Source Environment for Autonomous Vehicle Validation and Verification Research Acceleration

et al. 2023

Self Cite

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“…Focusing on autonomous driving specifically, many research groups have dedicated themselves to producing datasets recorded by mainstream perceptive sensors and covering various scenarios. In [18,19], autonomous-driving-related datasets over the last 20 years were categorized by time of acquisition, sensor configuration, illumination, and weather conditions. As it happens, some datasets only contain sunny (including cloudy) and daytime scenes [20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Object Segmentation for Autonomous Driving Using iseAuto Data

Bellone

Sell

et al. 2022

Electronics

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Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in a fully convolutional neural network. Our method was tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all weather scenarios, featuring night and rainy weather. In this work, it is shown that with LiDAR–camera fusion, with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm was measured in terms of intersection over union, precision, recall, and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.

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“…Focusing on autonomous driving specifically, many research groups have dedicated themselves to producing a dataset recorded by mainstream perceptive sensors and covering various scenarios. In [18], and [19], autonomous-driving-related datasets in the last 20 years were categorized by time of acquisition, sensor configuration, illumination, and weather conditions. As it happens, some datasets only contain sunny (include cloudy) and daytime scenes [20] [21] [22].…”

Section: Related Workmentioning

confidence: 99%

Object Segmentation for Autonomous Driving Using IseAuto Data

J¹,

Bellone²,

Sell³

et al. 2022

Preprint

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Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR-camera fusion and semi-supervised learning implemented in a fully-convolutional neural network. Our method is tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all-weather scenarios, featuring night and rainy weather. In this work, it is shown that LiDAR-camera fusion with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm is measured in terms of intersection over union, precision, recall and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.

show abstract

Autonomous Driving in the Real-World: The Weather Challenge in the Sohjoa Baltic Project

Cited by 12 publications

References 43 publications

PolyVerif: An Open-Source Environment for Autonomous Vehicle Validation and Verification Research Acceleration

PolyVerif: An Open-Source Environment for Autonomous Vehicle Validation and Verification Research Acceleration

Object Segmentation for Autonomous Driving Using iseAuto Data

Object Segmentation for Autonomous Driving Using IseAuto Data

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