Vehicle Map Reliability Evaluation Using Detected Road Users
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Solution Overview
Problem
Existing systems fail to precisely evaluate the reliability of map data, which is crucial for accurate automated driving, as they lack effective methods to determine the position of mobile objects within road information and adapt driving modes accordingly.
Innovation Solution
A processing device and method that acquire map data with road information and detection results from surrounding detectors, cross-checking the position of a second mobile object with the map data to determine if it is on the road, and adjust automated driving modes based on these determinations, including controlling speed and steering to ensure reliable navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If map data reliability is evaluated using traditional methods comparing terrestrial features with laser scanner data, then the evaluation process is simple, but the reliability evaluation precision is insufficient
Solution Approach 1:
The patent introduces a second mobile object (such as another vehicle or pedestrian) as an intermediary reference target to validate map data reliability. By detecting whether the second mobile object's position falls within the road region defined in map data, the system achieves more precise reliability evaluation without requiring complex terrestrial feature comparison systems.
Solution Approach 2:
The system uses the mobile object's own detection results and position information to evaluate map data reliability. The mobile object detects surrounding environments, obtains its own position, and uses this self-acquired data to cross-check against map information, eliminating the need for external evaluation systems.
2Reliability
If the system continuously determines whether the second mobile object position is within the road region, then map data reliability is continuously monitored, but the processing load and computational resources increase
Solution Approach 1:
The system performs reliability determination at specific intervals or under specific conditions rather than continuously. The processors determine whether the second mobile object's position is within the road region at discrete timing points, reducing computational load while maintaining effective monitoring of map data reliability.
3Reliability
If automated driving mode is adjusted based on real-time map data reliability determination, then driving safety is improved, but the system response time and decision-making complexity increase
Solution Approach 1:
The system determines map data reliability in advance before critical driving decisions are needed. By evaluating whether the second mobile object's position matches the map data beforehand, the system prepares reliability information that can be quickly used for automated driving mode adjustments without adding decision-making delay.
4Measurement precision
If the system cross-checks detection results with map data to determine second mobile object position, then position accuracy is improved, but the measurement and detection complexity increases
Solution Approach 1:
The system uses feedback from the cross-checking process to validate position information. By comparing the detected position of the second mobile object with the expected position from map data (road region information), the system achieves higher position accuracy through iterative validation and error correction.
Data Source
AI summary
A processing device acquires map data that has road information, acquires detection results detected by one or more detectors that detects surroundings of a first mobile object, and cross-checks a position of a second mobile object included in the detection results with the road information of the map data to determine whether or not the position of the second mobile object is included in a region indicating a road of the road information.