Monte Carlo Photon Simulation for LiDAR Fog Scattering
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Solution Overview
Problem
LiDAR sensors used in autonomous vehicles face challenges in adverse conditions such as fog, where light scattering reduces the effective range and causes false positive signals due to the scattering and absorption of light by water droplets.
Innovation Solution
A method of modeling the effect of fog on light by statistically simulating the behavior of individual photons emitted by a LiDAR system, including the distances between interactions with water droplets, deflection, and absorption, using a Monte Carlo simulation to determine if the photon intersects a target or is lost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LiDAR sensor operates in foggy conditions, then the sensor can continuously detect objects, but the effective range is reduced and false positive signals occur due to light scattering by water droplets
Solution Approach 1:
The patent introduces a statistical model as an intermediary between the LiDAR sensor and the navigation system. This model simulates photon interactions with water droplets (the harmful scattering medium) to predict detection reliability and range degradation, allowing the system to compensate for fog effects without physical modification of the sensor or environment.
Solution Approach 2:
The patent changes the operational parameters by using statistical simulations to adjust detection thresholds and range estimates based on fog density. By modeling photon scattering probabilities and modifying detection parameters according to simulated conditions, the system maintains reliable operation within the reduced effective range caused by fog.
2Measurement precision
If LiDAR sensor emits light pulses to detect objects, then the sensor can measure distance and detect objects, but light scattering by water droplets causes false positive signals
Solution Approach 1:
The patent converts the harmful scattering effect into a beneficial predictive tool. By statistically modeling the scattering process that causes false positives, the system uses the same physical phenomenon to calculate probability distributions of detection accuracy, thereby compensating for and eliminating false positive signals through informed threshold adjustment.
Solution Approach 2:
The simulation model provides feedback to the navigation system about the expected detection quality under current fog conditions. This feedback loop allows continuous adjustment of detection parameters and range estimates, correcting false positive signals before they affect navigation decisions.
3Measurement precision
If the LiDAR system uses statistical modeling to simulate photon behavior, then the accuracy of range measurement improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary computational work by pre-calculating and storing statistical parameters (mean free path, scattering coefficients) that characterize fog conditions. This preliminary action allows the navigation system to quickly query pre-computed models rather than performing complex real-time simulations, reducing instantaneous computational burden while maintaining high range accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a representative input for autonomous vehicle navigation systems to improve their response in foggy environments by accurately simulating the behavior of LiDAR photons in fog, reducing false positives and improving range accuracy.
Implementation Method 1
rain and/or fog induce scattering of the light pulse emitted by the LiDAR sensor. Each drop scatters incident light, whether the illumination beam emitted by the LiDAR unit or the return beam reflected by an object.
Implementation Method 2
rain and/or fog induce scattering of the light pulse emitted by the LiDAR sensor... The effect of this scattering is a reduction in the effective range of the LiDAR unit and/or false positive signals.
Data Source
AI summary
The systems and methods disclosed herein address simulating the effect of fog on a photon. One method defines a target at a position in a 3D environment and includes the steps of selecting a starting position of the photon in the 3D environment, selecting a propagation vector directed from the starting position toward the target, selecting a propagation distance, determining a new position of the photon based in part on the starting position of the photon and the propagation vector and the propagation distance, determining whether the photon is absorbed before reaching the new position and determining, if the photon has not been absorbed, whether the photon intersects the target before reaching the new position.