Vehicle Lateral Offset Estimation Without Square Root Computation
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Solution Overview
Problem
Autonomous vehicles face challenges in calculating lateral offsets from point cloud data due to the computational intensity and resource demands of square root operations, which are often unsupported in MCU environments, leading to increased power consumption and computation time.
Innovation Solution
A method and apparatus using small angle approximation and symmetric transformation to calculate lateral offsets without performing square root operations, employing driving trajectory approximations based on vehicle yaw rate and speed assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If square root operations are used to calculate lateral offset, then measurement precision is improved, but computation time and power consumption increase significantly
Solution Approach 1:
The patent transforms the lateral offset calculation from using square root operations to using polynomial approximation operations. By changing the mathematical parameters and operations involved in the calculation, the system achieves comparable precision while dramatically reducing computation time and power consumption, making it suitable for MCU environments.
Solution Approach 2:
The patent replaces the computational mechanism of square root operations with a polynomial-based computational mechanism. This substitution eliminates the need for complex iterative algorithms or hardware square root units, enabling efficient calculation on resource-constrained microcontrollers while maintaining acceptable accuracy for collision avoidance applications.
2Measurement precision
If square root operations are used to calculate lateral offset, then measurement precision is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent changes the mathematical parameters from requiring square root operations to using polynomial coefficients and basic arithmetic operations. This parameter transformation simplifies the computational requirements, eliminating the need for complex computational units or libraries, and making the system suitable for deployment on simple microcontrollers with limited resources.
Solution Approach 2:
The patent uses computationally inexpensive polynomial approximation methods that can be executed quickly and discarded, rather than relying on expensive or complex computational resources. This approach treats the calculation as a lightweight operation that consumes minimal computational resources, aligning with the principle of using simple, resource-efficient methods.
3Measurement precision
If Babylonian algorithm is used for square root approximation, then measurement precision is improved through iterations, but power consumption and computation time increase in proportion to iterations
Solution Approach 1:
The patent extracts and eliminates the iterative square root calculation process entirely, replacing it with a direct polynomial evaluation. By removing the iterative refinement step (taking out the Babylonian algorithm), the system achieves sufficient precision through a single-pass calculation, dramatically reducing power consumption and computational overhead.
Solution Approach 2:
The patent skips the iterative refinement process of the Babylonian algorithm and directly computes the lateral offset using polynomial approximation. This rushing through the calculation in a single pass rather than through multiple iterations significantly reduces power consumption while maintaining acceptable accuracy for real-time collision avoidance.
Data Source
AI summary
A method for determining a lateral offset between a vehicle and an object includes obtaining driving information of the vehicle and position information of the object. The position information of the object includes the coordinates of the object with respect to a reference coordinate system at the current time. The method also includes determining a y-axis coordinate of the vehicle when the vehicle has moved to the x-axis coordinate of the object using a driving trajectory approximation that represents a lateral travel distance as a function of a longitudinal travel distance of the vehicle. The driving trajectory approximation is determined based on a small angle approximation and an assumption that the yaw rate and the speed of the vehicle are constant. The method also includes determining the lateral offset using the y-axis coordinate of the vehicle and the y-axis coordinate of the object.