scholarly journals Investigating Intense Rainfall Influence on Distance Measurement with a Time-of-Flight Camera Sensor Using Optical Ray-Tracing Simulation Technique

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 1056
Author(s):  
Marcus Baumgart ◽  
Norbert Druml ◽  
Markus Dielacher ◽  
Cristina Consani
Keyword(s):  
Ray Tracing ◽  
Time Of Flight ◽  
Autonomous Driving ◽  
Intense Rainfall ◽  
Signal Suppression ◽  
Tof Camera ◽  
Ray Tracing Simulation ◽  
Camera Sensors ◽  
Optical Ray Tracing ◽  
Rain Effect

Robust, fast and reliable examination of the surroundings is essential for further advancements in autonomous driving and robotics. Time-of-Flight (ToF) camera sensors are a key technology to measure surrounding objects and their distances on a pixel basis in real-time. Environmental effects, like rain in front of the sensor, can influence the distance accuracy of the sensor. Here we use an optical ray-tracing based procedure to examine the rain effect on the ToF image. Simulation results are presented for experimental rain droplet distributions, characteristic of intense rainfall at rates of 25 mm/h and 100 mm/h. The ray-tracing based simulation data and results serve as an input for developing and testing rain signal suppression strategies.

scholarly journals Fog Effects on Time-of-Flight Imaging Investigated by Ray-Tracing Simulations

Proceedings ◽  
2018 ◽  
Vol 2 (13) ◽  
pp. 859 ◽  
Author(s):  
Cristina Consani ◽  
Norbert Druml ◽  
Markus Dielacher ◽  
Marcus Baumgart
Keyword(s):  
Ray Tracing ◽  
Autonomous Vehicles ◽  
Mobile Robotics ◽  
Time Of Flight ◽  
Distance Information ◽  
Ranging Errors ◽  
Atmospheric Phenomena ◽  
Ray Tracing Simulation ◽  
Autonomous Mobile Robotics ◽  
Optical Ray Tracing

Time-of-Flight (ToF) sensors are a key technology for autonomous vehicles and autonomous mobile robotics. Quantifying the extent of perturbation induced by atmospheric phenomena on ToF imaging is critical to identify effective correction strategies. Here we present an approach that uses optical ray-tracing to simulate the ToF image, while the distance information is recovered by analyzing the optical path of each ray. Such an approach allows, for example, understanding the effects of different ray paths on the ToF image, or testing various retrieval/correction algorithms upon running a single ray-tracing simulation. By modelling several scattering scenarios, we show that ranging errors arise mostly from light backscattered to the sensor prior reaching the scene. Scattering events close to the sensors (<1 m) have the largest influence, therefore strategies capable of filtering out signals from distances shorter than the range of interest can significantly improve the accuracy of ToF sensors.

scholarly journals Simple and Exact Closed-Form Expression for Determination of a Penetrated Ray Path in a Ray Tracing

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hyun Wook Moon ◽  
Woojoong Kim ◽  
Sewoong Kwon ◽  
Jaeheung Kim ◽  
Young Joong Yoon
Keyword(s):  
Closed Form ◽  
Approximate Method ◽  
Ray Tracing ◽  
Conventional Method ◽  
Closed Form Expression ◽  
Indoor Environments ◽  
Straight Line ◽  
Ray Path ◽  
Ray Tracing Simulation

A simple and exact closed-form equation to determine a penetrated ray path in a ray tracing is proposed for an accurate channel prediction in indoor environments. Whereas the penetrated ray path in a conventional ray tracing is treated as a straight line without refraction, the proposed method is able to consider refraction through the wall in the penetrated ray path. Hence, it improves the accuracy in ray tracing simulation. To verify the validation of the proposed method, the simulated results of conventional method, approximate method, and proposed method are compared with the measured results. The comparison shows that the proposed method is in better agreement with the measured results than the conventional method and approximate method, especially in high frequency bands.

Analysis of Multipath Propagation in Urban Environment Through Multidimensional Measurements and Advanced Ray Tracing Simulation

2008 ◽  
Vol 56 (3) ◽  
pp. 848-857 ◽  
Author(s):  
Franco Fuschini ◽  
Hassan El-Sallabi ◽  
Vittorio Degli-Esposti ◽  
Lasse Vuokko ◽  
Doriana Guiducci ◽  
...  
Keyword(s):  
Urban Environment ◽  
Ray Tracing ◽  
Multipath Propagation ◽  
Ray Tracing Simulation

scholarly journals On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6733
Author(s):  
Min-Joong Kim ◽  
Sung-Hun Yu ◽  
Tong-Hyun Kim ◽  
Joo-Uk Kim ◽  
Young-Min Kim
Keyword(s):  
Autonomous Vehicles ◽  
Focal Length ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Cruise Control ◽  
Radar Sensors ◽  
Safety And Reliability ◽  
Time Required ◽  
Variable Focus ◽  
Camera Sensors

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

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