Analysis of Automotive Lidar Sensor Model Considering Scattering Effects in Regional Rain Environments

Byeon, Mijung; Yoon, Sanghyun

doi:10.1109/access.2020.2996366

Cited by 29 publications

(13 citation statements)

References 34 publications

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“…In [ 119 ], the quantitative performance of lidar with varying rain intensity, with the help of a mathematical model, is presented and the results show that as rainfall intensity increases, the lidar cloud density is affected, increasing false-positive errors. The same effect is presented in [ 120 ], where the authors’ analysis showed that the varying intensities, size, and shape of raindrops drastically influence the attenuation rates of lidar. The effect of snow on lidar performances, such as reflectivity and propagation through the snowy environment, is evaluated in [ 121 ], using four lidars of different manufacturers.…”

Section: Sensorssupporting

confidence: 67%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

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Section: Sensorssupporting

confidence: 67%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Recent publications have also investigated the validation of noise models and the combination of noise effect on sensor models in virtual environments [33], [34]. The paper by Hasirlioglu and Riener in 2019 proposes a methodology to train and validate a virtual rain simulation on a regathered set of weather noise free dataset [33].…”

Section: B Effect Of Adverse Weather On Lidar Performancementioning

confidence: 99%

“…In the paper by Byeon and Yoon, an automotive simulation software, Prescan, was used with a rain noise model implemented. The proposed model can simulate rain precipitations in three different regions, and the impact on the received detected power is evaluated, via a Matlab LiDAR model [34].…”

Section: B Effect Of Adverse Weather On Lidar Performancementioning

confidence: 99%

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

et al. 2021

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The global Connected and Autonomous Mobility industry is growing at a rapid pace. To ensure the successful adoption of connected automated mobility solutions, their safety, reliability and hence the public acceptance are paramount. It is widely known that in order to demonstrate that L3+ automated systems are safer with respect to human drivers, upwards of several millions of miles need to be driven. The only way to efficiently achieve this amount of tests in a timely manner is by using simulations and high fidelity virtual environments. Two key components of being able to test an automated system in a synthetic environment are validated sensor models and noise models for each sensor technology. In fact, the sensors are the element feeding information into the system in order to enable it to safely plan the trajectory and navigate. In this paper, we propose an innovative real-time LiDAR sensor model based on beam propagation and a probabilistic rain model, taking into account raindrop distribution and size. The model can seamlessly run in real-time, synchronised with the visual rendering, in immersive driving simulators, such as the WMG 3xD simulator. The models are developed using Unreal engine, therefore demonstrating that gaming technology can be merged with the Automated Vehicles (AVs) simulation toolchain for the creation and visualization of high fidelity scenarios and for AV accurate testing. This work can be extended to add more sensors and more noise factors or cyberattacks in real-time simulations.

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“…However, this study focuses upon LiDAR data rather than visual imagery. In a similar vein, Byeon & Yoon (2020) simulate the effects of rain on synthetic sensory data, again focussing on LIDAR sensors.…”

Section: Modelling Weather Effect Upon Sensor and Perception Performancementioning

confidence: 99%

Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions

Fursa¹,

Elias²,

Musat³

et al. 2021

Preprint

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Autonomous vehicles rely heavily upon their perception subsystems to 'see' the environment in which they operate. Unfortunately, the effect of varying weather conditions presents a significant challenge to object detection algorithms, and thus it is imperative to test the vehicle extensively in all conditions which it may experience. However, unpredictable weather can make real-world testing in adverse conditions an expensive and time consuming task requiring access to specialist facilities, and weatherproofing of sensitive electronics. Simulation provides an alternative to real world testing, with some studies developing increasingly visually realistic representations of the real world on powerful compute hardware. Given that subsequent subsystems in the autonomous vehicle pipeline are unaware of the visual realism of the simulation, when developing modules downstream of perception the appearance is of little consequence -rather it is how the perception system performs in the prevailing weather condition that is important. This study explores the potential of using a simple, lightweight image augmentation system in an autonomous racing vehicle -focusing not on visual accuracy, but rather the effect upon perception system performance. With minimal adjustment, the prototype system developed in this study can replicate the effects of both water droplets on the camera lens, and fading light conditions. The system introduces a latency of less than 8 ms using compute hardware that is well suited to being carried in the vehicle -rendering it ideally suited to real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.

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Analysis of Automotive Lidar Sensor Model Considering Scattering Effects in Regional Rain Environments

Cited by 29 publications

References 34 publications

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

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

Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions

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