Racetrack Segmentation for Kinematically Feasible Optimal Laps

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Solution Overview

Problem

Existing methods for optimizing racetrack traversal are time-consuming, require extensive human practice, or rely on user-set parameters that are difficult to set correctly, and often produce infeasible or suboptimal paths due to the complexity of non-linear tracks.

Innovation Solution

A system partitions a racetrack into straight and curved segments based on rate of change, selects the fastest historical traversals for each segment, stitches them together, and smooths transitions to create an optimized traversal that adheres to kinematic constraints, using minimal user input and existing racing measurements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If automated optimization techniques are used, then time to complete a lap is reduced, but the techniques require time-consuming machine learning or difficult user parameter setting

Engineering Contradiction:

Improvetime to complete a lapVSAvoidcomplexity of optimization technique

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The racetrack is divided into multiple segments based on curvature characteristics (straight segments, transition segments, curved segments). This segmentation allows the system to apply different optimization strategies to each segment type, avoiding the need for complex global optimization while still achieving lap time reduction.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system pre-calculates and stores optimal traversal paths for each segment type based on historical racing measurements and kinematic constraints. During actual use, these pre-computed segment paths are retrieved and stitched together, eliminating the need for real-time machine learning or parameter tuning.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If human drivers practice extensively, then optimal path identification improves, but extensive practice time is required

Engineering Contradiction:

Improveaccuracy of optimal path identificationVSAvoidpractice time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary optimization by analyzing historical racing measurements and pre-computing optimal paths for each segment type. This offline computation eliminates the need for drivers to spend extensive practice time learning the optimal line, as the system has already determined it through data analysis.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Instead of relying on human drivers to discover optimal paths through practice, the system creates a digital model of the track and uses historical measurement data to generate optimal path copies that can be directly applied, replacing the need for extensive human practice.

Inventive Principle:

Principle #26Copying

3Loss of time

If automated optimization is applied to non-linear tracks, then lap time is reduced, but the generated paths may be infeasible or suboptimal

Engineering Contradiction:

Improvelap timeVSAvoidfeasibility of generated path

Core Design Contradiction:

Loss of timeVSReliability

Solution Approach 1:

The system applies different optimization approaches to different track segment types based on their local characteristics. Straight segments use one optimization strategy, transition segments use another, and curved segments use a third, ensuring each segment's path is feasible for its specific geometric and kinematic constraints.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system varies optimization parameters based on segment type and kinematic constraints. By adjusting parameters such as target velocity, lateral acceleration limits, and path smoothing factors according to the specific segment characteristics, the system generates feasible paths that respect vehicle capabilities while minimizing lap time.

Inventive Principle:

Principle #35Parameter changes

4Ease of operation

If minimal user parameters are used, then ease of operation improves, but sufficient optimization may not be achieved

Engineering Contradiction:

Improvesimplicity of parameter settingVSAvoidoptimization quality

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system automatically determines optimal parameters by analyzing historical racing measurements and track geometry. Users only need to provide minimal input (track definition and vehicle kinematic constraints), and the system self-configures the optimization parameters, achieving both ease of operation and high optimization quality.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses historical racing measurements as feedback to automatically tune optimization parameters. By analyzing past racing data, the system identifies effective parameter settings for different segment types and applies them automatically, eliminating the need for users to manually adjust parameters while maintaining high optimization quality.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12487096B2Optimum lap derivation via track segmentation and prior racing measurements

Publication Date: 2025.12.02 ORACLE INT CORP

AI summary

Techniques for deriving an optimal traversal path on a racetrack are disclosed. The system partitions a track into straight and curved segments. The system identifies optimal traversals through each segment from historical traversal data. The system stitches the optimal traversals together and smooths the optimal traversals at the transition points between track segments. The system verifies that the smoothed traversals meet one or more kinematic criteria before outputting the optimal traversal path.