Probabilistic Decision Engine for Autonomous Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigation and path planning under uncertain and dynamic conditions due to the complexity of probabilistic algorithms and the time-consuming nature of traditional methods like Monte Carlo simulation, making real-time operation difficult.
Innovation Solution
A probabilistic decision engine that uses a stochastic network standardization module, decomposition module, and probabilistic optimization kernel to generate probability distributions of optimal routes by converting environmental and location data into a navigation model, reducing the complexity through deterministic network reduction and convolution techniques, and employing a pure-analytical probabilistic solver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Monte Carlo simulation methods are used to obtain statistical results and derive probability distributions, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent segments the complex probabilistic optimization problem into multiple deterministic subproblems by decomposing the network into smaller components. This allows each subproblem to be solved independently and efficiently, then combined to form the overall solution, dramatically reducing computation time while maintaining accuracy.
Solution Approach 2:
The patent replaces the mechanical simulation-based Monte Carlo method with an analytical mathematical approach. By using closed-form solutions and probabilistic modeling techniques, the system eliminates the need for repeated simulations, achieving the same measurement precision with significantly reduced computational time.
2Reliability
If probabilistic optimization methods are applied to account for uncertainty in navigation, then reliability is improved, but device complexity increases
Solution Approach 1:
The complex probabilistic optimization is segmented into manageable deterministic subproblems through network decomposition. Each subproblem can be solved using standard algorithms, and the results are combined to provide the overall probabilistic solution, making the system more reliable without requiring a single overly complex algorithm.
Solution Approach 2:
The patent introduces intermediate deterministic solutions as mediators between the uncertain probabilistic problem and the final decision-making process. These intermediate solutions serve as building blocks that simplify the overall complexity while maintaining reliability through systematic combination.
3Productivity
If real-time operation is required for autonomous navigation, then productivity is improved, but loss of time for computation must be minimized
Solution Approach 1:
The patent performs preliminary decomposition of the network and pre-computation of deterministic subproblem solutions before real-time operation is required. This allows the system to be ready for rapid probabilistic optimization when actual navigation decisions are needed, achieving real-time performance by preparing in advance.
Solution Approach 2:
By replacing time-consuming simulation mechanisms with analytical computations, the system achieves real-time operation capability. The analytical approach provides closed-form or efficiently computable solutions that meet real-time productivity requirements without sacrificing the probabilistic handling of uncertainty.
Data Source
AI summary
The present disclosure provides a probabilistic decision engine for autonomous vehicles. Briefly described, one embodiment comprises taking a network connection matrix (based on maps and graph theory) and a cost matrix (with entries of the cost's mean values and probability distributions) as input and generates the probability distribution of optimal routes as output. The disclosed probabilistic decision engine comprises a stochastic network standardization module, a stochastic network decomposition module and a probabilistic optimization kernel. A deterministic network reduction method is first used to derive a standard reduced network, augmented by the stochastic network reduction. The standard network is then decomposed into a series of stochastic subnetworks by using the convolution, probability density function (PDF) shifting, and PDF reshaping techniques. A pure-analytical probabilistic solver is finally used to solve the stochastic optimization problem.