Multi-Layer Neural Planning for Autonomous Vehicle Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing planning architectures for autonomous vehicles and mobile robots face challenges in handling dynamic and complex environments due to the difficulty in formulating rules or finite state machines for every possible situation, leading to computationally intensive and incomplete systems that may hinder operation when encountering unforeseen scenarios.
Innovation Solution
A multi-layer planning system utilizing neural networks for each functional layer, including mission planning, behavior planning, and motion planning, which uses machine learning to determine optimal routes and control vehicle operations, allowing for feed-forward and feedback associations between layers to enhance decision-making and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based methods or finite state machines are used to plan routes for autonomous vehicles, then the system can handle structured and predictable situations, but the system becomes computationally intensive and cannot effectively handle dynamic and complex environments with unforeseen scenarios
Solution Approach 1:
The patent replaces rule-based mechanical decision-making systems with neural network-based learning systems. The neural networks learn to handle driving scenarios through training data, substituting the rigid, pre-programmed rule-based approach with an adaptive, data-driven approach that can generalize to unseen situations without requiring explicit rules for every possible scenario.
Solution Approach 2:
The patent changes the fundamental parameter of decision-making from discrete rule-based logic to continuous neural network predictions. By transforming the decision-making process into a parameter-based learning system that processes sensor inputs and generates control outputs through trained neural networks, the system achieves better adaptability to dynamic environments.
2Adaptability or versatility
If a large number of rules or finite state machines are developed to account for every possible scenario, then the system can handle more situations, but the computational intensity increases and the system becomes difficult to develop, test, and verify
Solution Approach 1:
The patent replaces the manual development, testing, and verification of numerous rules and FSMs with automated neural network training. The system learns scenario coverage from training data, eliminating the need for manual rule creation and simplifying the development process while maintaining high adaptability to diverse driving scenarios.
Solution Approach 2:
The patent performs preliminary learning through offline neural network training before deployment. By pre-training the neural networks on extensive driving data, the system acquires scenario coverage in advance, allowing it to handle diverse situations without requiring complex rule development and verification during operation.
3Ease of operation
If rule-based systems are used for autonomous vehicle control, then the system can operate in predictable environments, but operation is hindered when no rule exists for a given situation
Solution Approach 1:
The patent replaces rule-based decision-making with neural network-based prediction and control. The neural networks continuously process sensor inputs and generate appropriate control actions, enabling smooth operation in predictable environments while simultaneously providing adaptability to unforeseen scenarios through learned generalization from training data.
Solution Approach 2:
The patent introduces dynamic adaptability by replacing static rule-based systems with dynamic neural networks that can adjust their behavior based on learned patterns. The system transitions from fixed rule execution to dynamic, data-driven decision-making that adapts to new situations while maintaining operational smoothness in familiar scenarios.
Data Source
AI summary
A multi layer learning based control system and method for an autonomous vehicle or mobile robot. A mission planning layer, behavior planning layer and motion planning layer each having one or more neural neworks are used to develop an optimal route for the autonomous vehicle or mobile robot, provide a series of functional tasks associated with at least one or more of the neural networks to follow the planned optimal route and develop commands to implement the functional tasks.