Time-of-Flight Sensor Simulation for Autonomous Vehicle Training
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Solution Overview
Problem
Current methods for training autonomous vehicle (AV) driving systems require extensive real-world data collection, leading to increased risks and longer development times, as they necessitate thousands to hundreds of thousands of hours of driving time before the AV can operate safely on public roadways.
Innovation Solution
The use of simulation data generated from light emitting and sensing devices, which includes estimated light signal amplitudes and noise levels, allows for the creation of realistic scenarios in a controlled environment, enabling faster training of AV systems without physical road testing and reducing the latency in simulation outputs while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-world data collection is used for training AV systems, then training accuracy is improved, but development time and deployment risk increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios through simulation environments. Instead of collecting extensive real-world sensor data, the system generates synthetic training data by copying and reproducing driving scenarios in a virtual environment, maintaining the statistical properties and characteristics of real data while eliminating the time and risk associated with physical data collection
Solution Approach 2:
The patent performs preliminary data generation and system training in a virtual environment before real-world deployment. By pre-training AV systems using simulation data and pre-configuring sensor parameters in advance, the system reduces the need for extensive on-road testing and accelerates the development timeline while maintaining training effectiveness
2Reliability
If extensive real-world driving hours are required for AV training, then system reliability is improved, but the risk during early deployment increases
Solution Approach 1:
The patent implements a virtual testing environment that acts as a cushioning layer between development and real-world deployment. By thoroughly testing and validating AV systems in simulation before physical road testing, the system absorbs and mitigates potential failures and risks that would otherwise manifest during early real-world deployment, protecting both the system and public safety
3Measurement precision
If traditional sensor development and testing is performed through physical road testing, then sensor performance accuracy is improved, but development cycle time increases
Solution Approach 1:
The patent replaces physical mechanical testing systems with virtual simulation systems. Instead of physically mounting sensors on vehicles and conducting road tests, the system uses virtual sensor models in a simulated environment to evaluate sensor performance, maintaining measurement accuracy while dramatically accelerating the development cycle through parallel testing and rapid iteration
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 enables AV systems to be trained more quickly and efficiently, reducing the risk associated with early deployment and allowing for the rapid development and deployment of new sensors, as it leverages limited real-world data to simulate various driving scenarios, thereby shortening the development cycle.
Implementation Method 1
transmitting light signals towards the object and receiving reflections of the light signals
Implementation Method 2
time-of-flight sensors used on autonomous vehicles
Data Source
AI summary
Systems and techniques of the present disclosure may access data from a time-of-flight (TOF) sensor of an autonomous vehicle (AV). The TOF sensor may have light signals and received reflections of those transmitted signals such that a set of simulation data can be generated. This set of simulation data may identify a distance to associate with an object that is different from a calibration distance. Equations may be used to identify a light signal amplitude, a signal to noise ratio (SNR), and a range inaccuracy due to noise from the accessed data. The identified the light signal amplitude, the SNR, and the range inaccuracy due to noise may have been identified using equations. Once the set of simulation data is generated, it may be saved for later access by a processor executing a simulation program used to train devices used to control the driving of an AV.