Prior Map Filtering for Autonomous Vehicle Lane Boundary Detection
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Solution Overview
Problem
Autonomous vehicles face inaccuracies in lane boundary detection due to environmental changes, such as repainted lane boundaries, obstructions, and noise from sensors like cameras and lasers, leading to unreliable real-time estimates.
Innovation Solution
The method involves using prior maps to filter detected points by determining distances between sensor data and reference points, establishing a confidence buffer to select reliable points, and using these selected points to direct the vehicle, thereby improving the accuracy of lane boundary detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensor data is used for lane boundary detection, then the system can respond to current environmental conditions, but the detection accuracy deteriorates due to noise and environmental changes
Solution Approach 1:
The system performs preliminary actions by creating and storing high-precision prior maps of lane boundaries before real-time operation. These pre-established reference maps serve as a foundation for accurate detection, allowing the system to filter real-time sensor data against known, reliable geographic information to maintain both responsiveness and precision.
Solution Approach 2:
The prior map acts as an intermediary between raw sensor data and lane boundary detection. By comparing real-time sensor measurements against the stored prior map, the system mediates the detection process, filtering out noise and environmental variations while maintaining accuracy in determining lane boundaries.
2Measurement precision
If prior maps are used to filter detected points, then measurement precision improves, but device complexity increases due to map storage and processing requirements
Solution Approach 1:
The system extracts only the essential lane boundary information from the prior map that is relevant for current detection, rather than storing and processing complete environmental models. This selective extraction reduces computational complexity while maintaining the precision benefits of using prior map data for filtering detected points.
Solution Approach 2:
The system changes parameters by representing the prior map in an optimized format suitable for efficient comparison with sensor data. By transforming the map data into appropriate parameters and representations, the system achieves high measurement precision without proportionally increasing device complexity.
Data Source
AI summary
Disclosed herein are methods and systems for using prior maps for estimation of lane boundaries or other features within an environment. An example method may include receiving a location of a plurality of detected points on a roadway in an environment of an autonomous vehicle, determining, from a prior map of the roadway, a location of a plurality of reference points from a boundary marker on the roadway that correspond to the detected points on the roadway, determining distances between the detected points and the corresponding reference points based on the location of the detected points in the environment and the location of the reference points from the prior map of the roadway, determining a confidence buffer representing a threshold amount of variation associated with the prior map based at least in part on the distances between the detected points and the corresponding reference points, selecting one or more of the detected points such that the distance between a selected detected point and a corresponding reference point is less than the confidence buffer, and using the selected points to direct the autonomous vehicle along the roadway.