3D Vehicle Recognition for Low-Bandwidth Ego Positioning
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Solution Overview
Problem
Existing GNSS-based localization systems for autonomous vehicles require high-bandwidth communication systems for exchanging point cloud information, which is not feasible with conventional low-bandwidth systems, and rely on rigid body simulations for positioning accuracy, necessitating costly hardware upgrades.
Innovation Solution
A method utilizing a 3D object recognition module to determine the position of adjacent vehicles and amalgamate it with the ego vehicle's position using uncertainty coefficients, eliminating the need for rigid body simulation and high-bandwidth communication by predicting the ego vehicle's position based on real-time measurement data and amalgamation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud information is exchanged between vehicles to improve positioning accuracy, then positioning precision is improved, but communication bandwidth requirements increase significantly
Solution Approach 1:
The patent extracts only the essential position and orientation information from the complex point cloud data, transmitting these simplified parameters instead of the full point cloud. This extraction approach maintains positioning accuracy while dramatically reducing communication bandwidth requirements.
Solution Approach 2:
Instead of transmitting actual point cloud data, the patent creates simplified copies in the form of position and orientation parameters that represent the essential spatial information. These parameter copies enable accurate positioning without the bandwidth burden of full point cloud transmission.
2Measurement precision
If multiple IMUs are installed to improve positioning accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces an intermediary computational approach that uses geometric relationships and motion dynamics to achieve high positioning accuracy without requiring multiple physical IMUs. The intermediary calculations bridge the gap between single-sensor limitations and multi-sensor accuracy requirements.
Solution Approach 2:
The patent replaces the mechanical approach of adding more physical sensors (multiple IMUs) with a computational method that uses geometric modeling and dynamic calculations. This substitution achieves the same accuracy improvement through software rather than additional hardware.
3Measurement precision
If rigid body simulation is used to improve positioning accuracy, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex rigid body simulation into simpler geometric relationships and motion constraints. By breaking down the problem into manageable geometric components and applying constraint-based reasoning, the system achieves accurate positioning without the full computational burden of complete rigid body simulation.
Solution Approach 2:
The patent changes the parameters used in positioning calculations from full rigid body dynamics to simplified geometric and kinematic parameters. This parameter transformation maintains positioning accuracy while significantly reducing computational complexity by focusing on essential motion parameters rather than complete dynamic simulation.
Data Source
AI summary
A method for improved positioning of an ego vehicle that includes a localization system, a 3D object recognition module, and a position amalgamation module is disclosed. The method includes a) determining the position of the ego vehicle for the current time step with the localization system, b) determining a position of at least one adjacent vehicle in the vicinity of the ego vehicle with the 3D object recognition module, c) amalgamating the position of the ego vehicle determined in step a) with the position of the at least one adjacent vehicle determined in step b) to form a position amalgamation result with the position amalgamation module, and d) determining the position of the ego vehicle for the next time step with the localization system, taking into account the amalgamation result.