Multi-tier Neural Network for Autonomous Vehicle Control
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Solution Overview
Problem
Current rule-based algorithms for autonomous driving are difficult to scale and prone to errors due to the need for extensive anticipation and hardcoding of conditions, making them inefficient and error-prone as the system size increases.
Innovation Solution
The use of a deep neural network (DNN) that receives image data and outputs driving controls, allowing for scalable autonomous driving by interpolating new conditions without requiring changes to the algorithm architecture, with data additions and retraining enabling handling of new situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based algorithms are used for autonomous driving, then every situation can be explicitly handled through hard-coded conditions, but the system becomes difficult to scale and error-prone as it grows larger
Solution Approach 1:
The patent replaces the mechanical rule-based system with a neural network-based system. Instead of using explicit if-then rules and hard-coded conditions, the system uses a neural network that learns patterns from training data. This substitution transforms the approach from manual rule engineering to automated pattern recognition, reducing algorithm complexity while maintaining reliability.
Solution Approach 2:
The patent changes the fundamental parameter of how conditions are represented. Rather than using discrete rule parameters (if condition X then action Y), the system uses continuous neural network parameters that are adjusted through training. This allows the system to handle new situations by learning from data rather than requiring explicit rule definitions, thereby reducing complexity as the system scales.
2Adaptability or versatility
If rule-based algorithms anticipate every new situation, then comprehensive coverage is achieved, but extensive engineering labor and hardcoding are required
Solution Approach 1:
The patent applies preliminary action by collecting and preparing training data in advance that covers diverse situations. Instead of hard-coding rules for every possible scenario, the system pre-processes real-world driving data into training sets. This preliminary data preparation enables the neural network to learn and adapt to new situations automatically, reducing the need for extensive engineering labor when deploying the system.
Solution Approach 2:
The patent uses copying by creating synthetic training data that replicates real-world driving scenarios. Rather than manually engineering rules for each situation, the system copies and transforms actual driving data into comprehensive training sets. This allows the system to achieve broad situation coverage without proportional increases in engineering labor, as the neural network learns from copied examples rather than requiring explicit rule creation for each case.
3Adaptability or versatility
If the rule-based algorithm size increases to handle more conditions, then more situations are covered, but the risk of bugs and conflicting conditions increases
Solution Approach 1:
The patent replaces the mechanical rule-based system with a neural network-based system. Instead of using explicit if-then rules that can conflict with each other, the system uses a neural network that learns patterns from training data. This substitution transforms the approach from manual rule engineering to automated pattern recognition, reducing algorithm complexity while maintaining reliability.
Solution Approach 2:
The patent applies self-service by enabling the system to automatically learn and adapt to new conditions through continuous training on additional data. Rather than requiring manual rule updates that introduce bugs, the system self-updates by learning from new training examples. This automated learning process reduces bug risk while increasing condition coverage, as the system improves itself without human intervention.
Data Source
AI summary
A method for controlling an autonomous vehicle is disclosed. The method may include receiving image data. The image data may be logistically classified by a first neural network as pertaining to one situation of a plurality of situations. Based on this logistic classification, the image data may be assigned to a second neural network that is specifically trained to address the one situation. The second neural network may perform regression on the image data. Thereafter, the vehicle may be control with a command based on the regression.