Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment

Espineira, Juan P.; Robinson, Jonathan; Groenewald, Jakobus; Chan, Pak Hung; Donzella, Valentina

doi:10.1109/jsen.2021.3059310

Cited by 33 publications

(18 citation statements)

References 26 publications

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“…a Lidar, can indicate the presence of degrading environments e.g. rain or fog [42,43,44] and hence sensor impairment. We refer to this sort of interoception as sensor "introspection".…”

Section: Discussionmentioning

confidence: 99%

Safety Assessment for Autonomous Systems' Perception Capabilities

Molloy¹,

McDermid²

2022

Preprint

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Autonomous Systems (AS) are increasingly proposed, or used, in Safety Critical (SC) applications. Many such systems make use of sophisticated sensor suites and processing to provide scene understanding which informs the AS' decision-making. The sensor processing typically makes use of Machine Learning (ML) and has to work in challenging environments, further the ML-algorithms have known limitations,e.g., the possibility of false-negatives or false-positives in object classification. The well-established safety-analysis methods developed for conventional SC systems are not well-matched to AS, ML, or the sensing systems used by AS. This paper proposes an adaptation of well-established safety-analysis methods to address the specifics of perception-systems for AS, including addressing environmental effects and the potential failure-modes of ML, and provides a rationale for choosing particular sets of guidewords, or prompts, for safety-analysis. It goes on to show how the results of the analysis can be used to inform the design and verification of the AS and illustrates the new method by presenting a partial analysis of a road vehicle. Illustrations in the paper are primarily based on optical sensing, however the paper discusses the applicability of the method to other sensing modalities and its role in a wider safety process addressing the overall capabilities of AS.

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“…a Lidar, can indicate the presence of degrading environments e.g. rain or fog [42,43,44] and hence sensor impairment. We refer to this sort of interoception as sensor "introspection".…”

Section: Discussionmentioning

confidence: 99%

Safety Assessment for Autonomous Systems' Perception Capabilities

Molloy¹,

McDermid²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In the last few years, whilst virtual verification has been gaining traction, datasets for training and testing of AV and ADAS functions have been proliferating [14][15][16]. These datasets usually contain data collected from several automotive environmental perception sensors mounted on test vehicles driving in different regions of the world.…”

Section: Kitti Moseg Datasetmentioning

confidence: 99%

The data conundrum: compression of automotive imaging data and deep neural network based perception

Chan¹,

Souvalioti²,

Huggett³

et al. 2021

lim

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Video compression in automated vehicles and advanced driving assistance systems is of utmost importance to deal with the challenge of transmitting and processing the vast amount of video data generated per second by the sensor suite which is needed to support robust situational awareness. The objective of this paper is to demonstrate that video compression can be optimised based on the perception system that will utilise the data. We have considered the deployment of deep neural networks to implement object (i.e. vehicle) detection based on compressed video camera data extracted from the KITTI MoSeg dataset. Preliminary results indicate that re-training the neural network with M-JPEG compressed videos can improve the detection performance with compressed and uncompressed transmitted data, improving recalls and precision by up to 4% with respect to re-training with uncompressed data.

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“…Recent studies have focused on the analysis of a specific noise factor on a sensor type, e.g. rain on LiDAR [4], [5], interference on RADAR [6], [7], fog on camera [8]. However, further investigation of noise factors is required, as the automotive environment is highly complex, swiftly changing, and multiple noise sources can impact the sensor outputs simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

Chan

Baris

et al. 2022

IEEE Sensors J.

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Assisted and Automated Driving functions are increasingly deployed to support improved safety, efficiency, and enhance driver experience. However, there are still key technical challenges that need to be overcome as the degradation of perception sensor data due to noise factors. The quality of data being generated by sensors can directly impact the planning and control of the vehicle, which in turn can affect the vehicle safety. A framework to analyse noise factor effects on automotive environmental perception sensors has been recently proposed and applied to study the effects of noise factors on LiDAR sensors. This work builds on this framework, and deploys it to camera sensors, focusing on the specific disturbed sensor outputs via a detailed analysis and classification of camera specific noise sources. Moreover, the noise factor analysis has been used to identify two omnipresent and independent noise factors (i.e. obstruction and windshield distortion). These noise factors have been modelled to generate noisy camera data; their impact on the perception step, based on deep neural networks, has been evaluated when the noise factors are applied independently and simultaneously. It is demonstrated that the performance degradation from the combination of noise factors is not simply the accumulated performance degradation from each single factor, which raises the importance of including combination noise factor modelling and testing for performance analysis. Thus, through the findings here, the framework can enhance the use of simulation for development and testing of automated vehicles through careful consideration of the noise factors affecting camera data.

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Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment

Cited by 33 publications

References 26 publications

Safety Assessment for Autonomous Systems' Perception Capabilities

Safety Assessment for Autonomous Systems' Perception Capabilities

The data conundrum: compression of automotive imaging data and deep neural network based perception

Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

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