Autonomous Vehicle Perception Simulation With Sensor Noise Modeling
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Solution Overview
Problem
Conventional autonomous vehicle trajectory simulations assume perfect sensor data, which is often inaccurate due to errors, calibration issues, and noise, leading to inconsistent simulation results and suboptimal vehicle behavior.
Innovation Solution
A perception simulation system that models and applies noise and inaccuracies from real-world sensor data to create a more realistic simulation environment, using ground truth data to calibrate and generate simulated perception data that mimics real-world sensor errors, improving the robustness of motion planning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional vehicle trajectory simulations assume perfect sensor data, then the simulation computation is simple and fast, but the simulation results are inconsistent with actual vehicle behavior due to sensor errors and noise
Solution Approach 1:
The patent applies preliminary action by pre-calibrating sensors and pre-computing perception noise models before simulation. The system performs sensor calibration and noise model generation in advance, storing these models for use during simulation. This allows the simulation to incorporate realistic sensor imperfections without computing them in real-time during trajectory simulation, thus improving reliability while managing complexity.
Solution Approach 2:
The patent introduces an intermediary perception noise model that mediates between ground truth data and simulated sensor data. This noise model acts as a bridge, transforming perfect ground truth into realistic sensor readings by adding calibrated noise and errors. This intermediary layer enables the simulation to reflect actual vehicle behavior without requiring complex real-time sensor modeling during trajectory computation.
2Measurement precision
If perception noise is modeled in the simulation using calibration data and ground truth comparison, then the simulation realism is improved, but the calibration and modeling process becomes more complex
Solution Approach 1:
The patent applies copying by creating a perceived data model that replicates real sensor behavior. Instead of using actual sensors during simulation, the system copies the essential characteristics of sensor imperfections into a computational model. This model is calibrated by comparing simulated sensor readings with ground truth data, then used to generate realistic perception data without requiring physical sensors in the simulation environment.
3Reliability
If the simulation uses real-world sensor errors and noise, then the motion planner robustness test becomes more realistic, but the computation time and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing perception noise models during an offline calibration phase. These pre-computed models capture sensor error characteristics without requiring real-time computation during trajectory simulation. During actual simulation, the system applies these pre-generated noise models to ground truth data, significantly reducing computation time while maintaining realistic error representation for robustness testing.
Data Source
AI summary
A system and method for real world autonomous vehicle perception simulation are disclosed. A particular embodiment includes: configuring a sensor noise modeling module to produce simulated sensor errors or noise data with a configured degree, extent, and timing of simulated sensor errors or noise based on a set of modifiable parameters; using the simulated sensor errors or noise data to generate simulated perception data by simulating errors related to constraints of one or more of a plurality of sensors, and by simulating noise in data provided by a sensor processing module corresponding to one or more of the plurality of sensors; and providing the simulated perception data to a motion planning system for the autonomous vehicle.