Autonomous Vehicle Digital Twin for Independent Safety Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The current deployment of autonomous vehicles (AVs) lacks independent verification of safety and performance data, relying on data provided by AV stack providers, which can lead to inaccurate risk assessments and safety concerns due to the lack of local context and historical data in deployment environments.
Innovation Solution
A computer-implemented method and system that utilize simulation modules and analytic engines to estimate the impact of deploying an autonomous vehicle with a specific AV stack in a particular deployment location under varying environmental conditions, by performing multiple simulation runs that simulate the AV behavior and traffic behavior, and analyzing the output data to determine key performance indicators and severity indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AV stack providers provide safety and performance data directly, then deployment speed is improved, but data reliability and independent verification capability deteriorate
Solution Approach 1:
The patent introduces a digital twin as an intermediary system that independently verifies AV safety and performance data. The digital twin creates a virtual replica of the AV system and simulates its behavior under various conditions, serving as an independent mediator between the AV stack provider and deployment stakeholders. This allows deployment speed to be maintained while data reliability is improved through independent verification.
2Device complexity
If generic environment algorithms are used for AV development, then development complexity is reduced, but adaptability to specific deployment environments deteriorates
Solution Approach 1:
The patent applies local quality by creating environment-specific digital twins that capture the unique characteristics of each deployment location. Instead of using a single generic algorithm for all environments, the system creates localized virtual replicas that reflect specific environmental conditions, traffic patterns, and operational contexts. This allows the AV system to maintain simple generic development while adapting to specific local environments through customized digital twin simulations.
3Measurement precision
If extensive simulation and testing are performed, then safety assessment accuracy is improved, but time and resource consumption increase
Solution Approach 1:
The patent implements preliminary action by creating and validating the digital twin model before actual AV deployment. The digital twin is pre-configured with the specific deployment environment characteristics, traffic patterns, and safety criteria. This preliminary setup allows for rapid subsequent simulations and assessments without requiring extensive on-site testing, thereby improving safety assessment accuracy while reducing overall time and resource consumption.
Data Source
AI summary
Various embodiments of the teachings herein include a method for estimating an impact resulting from deploying an autonomous vehicle (AV) characterized by an AV stack in an environment. An example includes: performing simulation runs; in each run, generating a simulated time dependent AV behavior and a simulated time dependent traffic behavior utilizing simulation conditions and data from the AV stack; forming output data comprising the simulated AV behavior, the simulated traffic behavior, and at least a part of the input data; and analyzing the output data for some or all of the simulation runs using an analytic engine module to estimate the impact based on the analysis result.