Propagating Characteristics of Pulsed Laser in Rain

Guo, Jing; Zhang, He; Zhang, Xiangjin

doi:10.1155/2015/292905

Cited by 15 publications

(16 citation statements)

References 16 publications

(16 reference statements)

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“…If a LiDAR beam intersects with a raindrop at a short distance from the transmitter, the raindrop can reflect enough of the beam back to the receiver such that it is detected as an object [22], [26]. Alternatively, raindrops can absorb some of the emitted light [27]. This degrades the range performance of the LiDAR demonstrating the need to model the effect that rainfall will have on the sensor [26], [27].…”

Section: B Effect Of Adverse Weather On Lidar Performancementioning

confidence: 99%

“…Alternatively, raindrops can absorb some of the emitted light [27]. This degrades the range performance of the LiDAR demonstrating the need to model the effect that rainfall will have on the sensor [26], [27].…”

Section: B Effect Of Adverse Weather On Lidar Performancementioning

confidence: 99%

“…Hence the equation becomes ( ) = 2 exp (−0.02 0.6 ) (2) To implement the measured distance variation due to rain, Goodin et al derived an equation using work from Filgueira et al [29], and their new distance ( ′ ) measurement was ′ = + (0,0.02 (1 − − ) 2 ) (3) However, the measurements taken by Filgueira et al were only using rainfall rates up to 8 mm/h meaning the model is only valid up to 8 mm/h rainfall intensity [29]. Rain showers up to 2 mm/h is considered a light shower, 10 mm/h a moderate shower and above that is considered a heavy shower, with rainstorms potentially reaching 100 mm/h [27], [30]. For continuous rain less than 0.5 mm/h is considered light, up to 4 mm/h moderate and more than 4 mm/h heavy [30].…”

Section: B Effect Of Adverse Weather On Lidar Performancementioning

confidence: 99%

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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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Section: B Effect Of Adverse Weather On Lidar Performancementioning

confidence: 99%

Section: B Effect Of Adverse Weather On Lidar Performancementioning

confidence: 99%

Section: B Effect Of Adverse Weather On Lidar Performancementioning

confidence: 99%

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

et al. 2021

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“…Previous attempts to simulate the impact of weather on the performance of a LIDAR make use of the LIDAR equation [ 3 ], which calculates the absorption and scattering coefficients of rain, snow or fog based on the drop size distribution [ 12 ]. Given the relationship between drop size distribution and rain intensity, it is possible to use Monte Carlo simulations to calculate a false positives rate caused by the drops [ 13 ] for a given rain intensity.…”

Section: State Of the Artmentioning

confidence: 99%

Weather Classification Using an Automotive LIDAR Sensor Based on Detections on Asphalt and Atmosphere

Rivero

Gerbich

Teiluf

et al. 2020

Sensors

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A semi-/autonomous driving car requires local weather information to identify if it is working inside its operational design domain and adapt itself accordingly. This information can be extracted from changes in the detections of a light detection and ranging (LIDAR) sensor. These changes are caused by modifications in the volumetric scattering of the atmosphere or surface reflection of objects in the field of view of the LIDAR. In order to evaluate the use of an automotive LIDAR as a weather sensor, a LIDAR is placed outdoor in a fixed position for a period of 9 months covering all seasons. As target, an asphalt region from a parking lot is chosen. The collected sensor raw data is labeled depending on the occurring weather conditions as: clear, rain, fog and snow, and the presence of sunlight: with or without background radiation. The influence of different weather types and background radiations on the measurement results is analyzed and different parameters are chosen in order to maximize the classification accuracy. The classification is done per frame in order to provide fast update rates while still keeping an F1 score higher than 80%. Additionally, the field of view is divided into two regions: atmosphere and street, where the influences of different weather types are most notable. The resulting classifiers can be used separately or together increasing the versatility of the system. A possible way of extending the method for a moving platform and alternatives to virtually simulate the scene are also discussed.

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“…Lidar has high accuracy and wide measuring range, and can immediately obtain targets’ displacement, and thus can simply calculate velocity, acceleration, and other states. Laser measurements however easily suffer from attenuation over the air, and the perception accuracy happens to sharply decline due to the serious noise, and thus Lidar fails to work normally in bad weather [ 4 , 5 , 6 ]. Radar is another common distance measurement sensor that can easily obtain targets’ displacement, velocity, and acceleration.…”

Section: Introductionmentioning

confidence: 99%

The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation

Gao

Liu

Wang

et al. 2016

Sensors

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This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.

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Propagating Characteristics of Pulsed Laser in Rain

Cited by 15 publications

References 16 publications

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

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

Weather Classification Using an Automotive LIDAR Sensor Based on Detections on Asphalt and Atmosphere

The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation

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