Autonomous Driving Control via Multi-Sensor Wheel Dynamics Fusion
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Solution Overview
Problem
Existing autonomous driving systems lack the capability to effectively control vehicle operations based on a combination of sensor data to achieve precise and real-time adjustments in wheel speed, inclination, and suspension settings for optimal performance in various driving conditions.
Innovation Solution
An information processing device that calculates control variables for wheel speed, inclination, and suspension using a combination of sensor information through deep learning, enabling precise control of autonomous driving operations every billionth of a second.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor information combinations are used to calculate index values for controlling wheel speed, inclination, and suspension, then the precision of autonomous driving control is improved, but the calculation complexity and processing time increase
Solution Approach 1:
The patent segments the autonomous driving control into multiple independent control targets (wheel speed, wheel inclination, suspension), with each target having dedicated sensor combinations and calculation processes. This allows parallel processing of different control aspects, improving overall precision without proportionally increasing sequential complexity
Solution Approach 2:
The patent introduces a combinatorial dimension by calculating index values from multiple different combinations of sensor information for each control target. Instead of using a single sensor set, it processes multiple sensor combinations (e.g., combining data from various sensors in different configurations) to generate multiple index values that are then aggregated, thereby enhancing control precision through multi-dimensional data analysis
2Reliability
If control variables are calculated and aggregated from multiple sensor information combinations, then the reliability of autonomous driving control is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary calculations by pre-defining multiple sensor information combinations and their corresponding index value calculations for different control targets. By preparing these calculation frameworks in advance, the system can rapidly aggregate results from multiple sensor combinations during real-time operation, improving reliability without proportionally increasing processing time
Solution Approach 2:
The patent implements continuous calculation and aggregation of index values from multiple sensor combinations for all control targets simultaneously. Rather than processing control targets sequentially, the system maintains continuous parallel computation across wheel speed, inclination, and suspension controls, ensuring uninterrupted reliable control while minimizing total processing time
3Measurement precision
If deep learning multivariate analysis is used to calculate control variables from index values, then the accuracy of autonomous driving control is improved, but the computational requirements and system complexity increase
Solution Approach 1:
The patent introduces index values as intermediary variables between raw sensor information and final control variables. Multiple sensor combinations first generate multiple index values for each control target, which are then aggregated and processed through deep learning multivariate analysis to produce control variables. This intermediary layer simplifies the input requirements for deep learning models while maintaining high control accuracy
Data Source
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AI summary
An information processing device disclosed herein includes: a calculation unit that calculates, for each of plural combinations of a predetermined number of pieces of sensor information among plural pieces of sensor information included in a vehicle, index values for controlling a wheel speed and an inclination of each of four wheels of the vehicle, and suspensions that support the wheels for each of the wheel speed, the inclination, and the suspension, and calculates a control variable for each of the wheel speed, the inclination, and the suspension by aggregating the index values; and a control unit that controls autonomous driving on the basis of the control variable.