Vehicle Dynamics Model Libraries for Real-Time Domain Optimization
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Solution Overview
Problem
Current vehicle dynamic models are computationally heavy and inefficient, making them unsuitable for real-time processing in autonomous vehicles, which requires fast and accurate models for safe and efficient motion management.
Innovation Solution
A computer-implemented method to derive a library of optimized vehicle dynamic implementation models, each correlated to specific operational design domains, by defining and simulating abstract models, generating reference trajectories, optimizing implementation models based on these trajectories, and validating them for use in different operational scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional vehicle dynamic models are used, then model accuracy is maintained, but computational load increases and processing speed decreases
Solution Approach 1:
The patent segments the vehicle dynamic model into multiple simplified implementation models, each optimized for specific operational design domains (e.g., low-speed maneuvering, high-speed cruising, transient conditions). This segmentation allows the system to use computationally lighter models appropriate for each domain rather than a single heavy conventional model, thereby improving processing speed while maintaining accuracy within each domain's operational context
Solution Approach 2:
The patent changes key parameters of the vehicle dynamic model based on operational conditions. By adjusting model parameters (such as damping coefficients, stiffness values, and mass distribution) according to the current operational design domain, the system achieves accurate predictions tailored to specific operating conditions while using simplified model structures that reduce computational load compared to conventional fixed-parameter models
2Adaptability or versatility
If a single comprehensive vehicle dynamic model is used, then all operational scenarios are covered, but computational complexity increases
Solution Approach 1:
The patent divides the comprehensive vehicle dynamic model into multiple specialized implementation models, each tailored to specific operational design domains such as low-speed maneuvering, high-speed cruising, and transient conditions. This segmentation reduces the complexity of individual models while collectively covering all operational scenarios through domain-specific optimization
Solution Approach 2:
The patent implements a dynamic model selection mechanism that adapts the model complexity to the current operational context. By dynamically selecting or switching between different implementation models based on real-time operational parameters (speed, acceleration, steering angle), the system achieves versatile scenario coverage without maintaining the computational complexity of a single comprehensive model
3Measurement precision
If high-fidelity vehicle dynamic models are used, then simulation accuracy improves, but real-time processing capability deteriorates
Solution Approach 1:
The patent applies local quality by creating implementation models with varying fidelity levels tailored to specific operational design domains. Each model is optimized to provide sufficient simulation accuracy for its designated domain while using reduced computational complexity appropriate for that local operational context, rather than applying uniform high-fidelity modeling across all scenarios
Solution Approach 2:
The patent changes model parameters and structural complexity based on the operational design domain requirements. For domains requiring high accuracy (e.g., safety-critical transient conditions), more detailed parameters are used, while for less critical domains (e.g., steady-state cruising), simplified parameters suffice. This parameter adaptation maintains real-time processing capability while preserving simulation accuracy where needed
Data Source
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AI summary
A computer-implemented method of deriving a vehicle dynamic library (30) comprising at least two optimized vehicle dynamic implementation models (22a-e) is provided. The method comprises defining (102) at least two operational design domains (24a-e), choosing (104) one vehicle dynamic abstract model (20), choosing (106) one vehicle dynamic implementation model (24) to be optimized, simulating (110) the vehicle dynamic abstract model (20) for each operational design domain (24a-e), wherein the simulation generates reference trajectories (111a, 111b), optimizing (114) the vehicle dynamic implementation model (22) for each operational design domain (24a-e) so as to create at least two optimized vehicle dynamic implementation models (22a-e), wherein each optimization is based on at least one of said generated reference trajectories (111a, 111b), validating (116) each optimized vehicle dynamic implementation model (22a-e) to achieve a validation level for each optimized vehicle dynamic implementation model (22a-e), and saving (118) each optimized vehicle dynamic model (22a-e) together with its validation level.