Test methodology for rain influence on automotive surround sensors

Hasirlioglu, Sinan; Kamann, Alexander; Doric, Igor; Brandmeier, Thomas

doi:10.1109/itsc.2016.7795918

Cited by 83 publications

(38 citation statements)

References 16 publications

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“…Hasirlioglu et al proposed a theoretical model considering multiple reflections by rain drops or fog to determine the influence of fog and rain for automotive perception sensors [9]. The principle of the model is based on a longitudinal layer representation.…”

Section: A Lidar Sensors In Adverse Weather Conditionsmentioning

confidence: 99%

Weather Influence and Classification with Automotive Lidar Sensors

Heinzler

Schindler

Seekircher

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

163

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Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as well as ground estimation, but intentionally ignore weather effects to reduce false detections. In this work, we present an in-depth analysis of automotive lidar performance under harsh weather conditions, i.e. heavy rain and dense fog. An extensive data set has been recorded for various fog and rain conditions, which is the basis for the conducted indepth analysis of the point cloud under changing environmental conditions. In addition, we introduce a novel approach to detect and classify rain or fog with lidar sensors only and achieve an mean union over intersection of 97.14 % for a data set in controlled environments. The analysis of weather influences on the performance of lidar sensors and the weather detection are important steps towards improving safety levels for autonomous driving in adverse weather conditions by providing reliable information to adapt vehicle behavior.

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Section: A Lidar Sensors In Adverse Weather Conditionsmentioning

confidence: 99%

Weather Influence and Classification with Automotive Lidar Sensors

Heinzler

Schindler

Seekircher

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

163

View full text Add to dashboard Cite

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“…Indoor fog chambers are used both for experimentally investigating the performance of sensors [6][7][8][9][10][11] and for validating the developed models against real conditions [1,3]. Many have built their own chamber to mimic fog or rain and some have used the state-of-the-art fog chamber of CEREMA.…”

Section: Introductionmentioning

confidence: 99%

“…Tests performed in fog chambers are always static, and neither the sensors nor the targets were moving at all. The targets used are usually "natural" targets, i.e., vehicles, mannequin puppets, but calibrated targets were occasionally used [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions

2019

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Light detection and ranging sensors (LiDARS) are the most promising devices for range sensing in automated cars and therefore, have been under intensive development for the last five years. Even though various types of resolutions and scanning principles have been proposed, adverse weather conditions are still challenging for optical sensing principles. This paper investigates proposed methods in the literature and adopts a common validation method to perform both indoor and outdoor tests to examine how fog and snow affect performances of different LiDARs. As suspected, the performance degraded with all tested sensors, but their behavior was not identical.

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“…The sensing approaches have advantages and disadvantages. LiDARs have high resolution and precise perception even in the dark, but are vulnerable to bad weather conditions (e.g., heavy rain; Hasirlioglu, Kamann, Doric, & Brandmeier, ) and involve moving parts. In contrast, cameras are cost efficient, but lack depth perception and cannot work in the dark.…”

Section: Deep Learning For Driving Scene Perception and Localizationmentioning

confidence: 99%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,153

495

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The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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Test methodology for rain influence on automotive surround sensors

Cited by 83 publications

References 16 publications

Weather Influence and Classification with Automotive Lidar Sensors

Weather Influence and Classification with Automotive Lidar Sensors

Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions

A survey of deep learning techniques for autonomous driving

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