A Framework to Analyze Noise Factors of Automotive Perception Sensors

Chan, Pak Hung; Dhadyalla, Gunwant; Donzella, Valentina

doi:10.1109/lsens.2020.2996428

Cited by 30 publications

(30 citation statements)

References 12 publications

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“…This work presents a real-time LiDAR model implemented using the Unreal gaming engine that is used in automotive testing for high fidelity visual rendering of the testing scenarios. The sensor model is combined with a noise model (in our case a probabilistic rain model) without affecting simulation realtime performance and paving the way to combining more noise sources using the framework presented in [20]. We tested LiDAR performance without and with different rainfall intensities, and studied the effect of the detection threshold on false positive and false negative detections.…”

Section: Discussionmentioning

confidence: 99%

“…This capability allows for repeatability of tests, and also the testing of the same scenario with subtle changes, such as adding different noise factors, to observe changes in the ACS reaction. Noise can cause data loss and distortion, having a profound impact on the perception algorithm; a framework to analyse noise factors affecting automotive sensors have been recently proposed [20]. To the best of our knowledge, this paper presents, for the first time, a real-time LiDAR simulation model which includes beam propagation effects and a probabilistic rain model, evaluating real-time the effect impact of the rain droplets on each LiDAR beam.…”

Section: ) Possibility To Inject Noise Factorsmentioning

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: Discussionmentioning

confidence: 99%

Section: ) Possibility To Inject Noise Factorsmentioning

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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“…• Sensor performance dependence on environmental/weather conditions [7][8]. Sensor performance degradation needs to be consistent and predictable under changing environmental conditions.…”

Section: Avs and Sensing Challengesmentioning

confidence: 99%

A Review and Perspective on Optical Phased Array for Automotive LiDAR

Hsu

Solano-Rivas

et al. 2021

IEEE J. Select. Topics Quantum Electron.

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“…A framework to simulate and analyse noise factors was proposed by Chan et al (2020) to predict sensor performance degradation in autonomous vehicles, concluding that analysis of noise factors individually is insufficient, and as a result perception is drastically affected when multiple noises sources are combined. However, this study focuses upon LiDAR data rather than visual imagery.…”

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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A Framework to Analyze Noise Factors of Automotive Perception Sensors

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 30 publications

References 12 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

A Review and Perspective on Optical Phased Array for Automotive LiDAR

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

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