Traffic Simulation for Autonomous Warehouse Safety Parameters
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Solution Overview
Problem
Current systems for equipping vehicles with parameters for autonomous driving in environments like warehouses are inefficient, requiring extensive and costly observation of real traffic situations to determine safety-relevant parameters, often leading to over-engineered hardware and slow processes due to maximum safety measures.
Innovation Solution
A method for rapidly determining characteristic values of an environment by simulating traffic using an electronic map and parameters, counting events of interest, and adjusting vehicle parameters based on simulation results to optimize safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real traffic situations are observed extensively to determine safety parameters, then reliability of safety settings is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of the real environment (warehouse layout, vehicles, obstacles) and traffic situations through electronic maps and simulation models. These digital twins allow safety parameters to be determined through virtual observation and simulation rather than extensive real-world monitoring, dramatically reducing time and cost while maintaining reliability through controlled simulation scenarios that cover edge cases and rare events more efficiently than passive real-world observation.
Solution Approach 2:
The patent performs preliminary safety analysis through simulation before actual deployment. By pre-determining safety parameters through virtual traffic simulations and analyzing potential collision scenarios in advance, the system identifies optimal safety settings without needing to extensively monitor real traffic for extended periods. This preliminary virtual testing accelerates the parameter determination process while ensuring comprehensive safety coverage.
2Reliability
If maximum safety measures are implemented, then reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses simulation to systematically vary and optimize safety parameters (such as detection ranges, response times, velocity limits) to find the minimum necessary hardware specifications that achieve required safety levels. By analyzing simulation results, the system determines precise parameter thresholds that ensure safety without requiring over-engineered hardware, thus reducing device complexity and cost while maintaining adequate reliability.
Solution Approach 2:
The patent replaces complex physical safety testing infrastructure with virtual simulations. Instead of requiring extensive real-world test facilities, multiple test scenarios, and physical prototypes for safety validation, the system uses digital copies of the environment and traffic patterns to evaluate and optimize safety parameters computationally, significantly reducing the complexity of hardware and testing infrastructure required.
3Measurement precision
If extensive real traffic observation is conducted, then measurement precision of safety parameters is improved, but cost increases
Solution Approach 1:
The patent replaces expensive and time-consuming real-world traffic observation with virtual simulations using electronic maps and digital traffic models. These simulations can precisely measure safety parameters by controlling and repeating specific scenarios, analyzing collision risks, and determining characteristic values through computational analysis rather than costly field observations. The virtual environment allows for precise measurement without the proportional increase in real-world observation costs.
Solution Approach 2:
The patent enables continuous safety parameter analysis through automated simulations that can run continuously without interruption, unlike real-world observation which is limited by operational constraints. The simulation system can continuously evaluate safety parameters, refine measurements, and analyze edge cases without additional marginal costs, achieving high measurement precision through sustained computational analysis rather than proportionally increasing observation expenditures.
Data Source
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AI summary
A method for determining one or more characteristic values for an automatically traversable environment has the following steps: providing a spatially resolved, electronically usable representation of the environment, providing parameters that describe properties of vehicles and traffic in the environment, simulating the traffic in the environment with reference to the electronically usable representation of the environment and with reference to the provided parameters, capturing and counting events of interest in the simulation, determining the characteristic values with reference to the counting result, and outputting and/or storing the characteristic value.