Autonomous Vehicle Sensor Fusion for Lane Estimation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining lane boundaries and object presence due to the computational expense and difficulty of incorporating various information sources, such as road maps, detected lane markers, and obstacle presence, which can lead to uncertain estimates.
Innovation Solution
The method involves receiving data from multiple sensors and sources, each with an accuracy value, and combining these estimates to produce a consolidated estimate of the lane or region of interest, along with an associated confidence value, using techniques like sensor fusion and weighted averaging to enhance accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple information sources (sensors, road maps, lane markers, obstacle detection) are incorporated to improve estimation accuracy, then measurement precision improves, but device complexity and computational expense increase
Solution Approach 1:
The patent combines multiple independent estimates (from sensors, road maps, lane markers, obstacle detection) into a single consolidated estimate using probabilistic methods. This merging process integrates diverse information sources to improve measurement precision while managing system complexity through a unified estimation framework.
Solution Approach 2:
The system transforms multiple estimates with different accuracy characteristics into a consolidated estimate by adjusting parameters such as confidence values and probability distributions. This allows the system to weight different information sources appropriately and produce a unified estimate that reflects the overall accuracy of all inputs.
2Reliability
If multiple sensors and information sources are integrated to reduce uncertainty, then reliability improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent introduces an intermediary consolidation process that acts as a mediator between multiple independent estimates. This intermediary layer processes individual estimates from sensors, road maps, and obstacle detection, combining them into a unified consolidated estimate that reduces uncertainty while managing the complexity of integration.
Solution Approach 2:
The system uses parameter transformations to convert multiple estimates with varying reliability characteristics into a consolidated estimate. By adjusting confidence parameters and probability distributions, the system manages the difficulty of combining estimates while improving overall reliability through probabilistic integration.
Data Source
AI summary
A vehicle is provided that may combine multiple estimates of an environment into a consolidated estimate. The vehicle may receive first data indicative of the region of interest in an environment from a sensor of the vehicle. The first data may include a first accuracy value and a first estimate of the region of interest. The vehicle may also receive second data indicative of the region of interest in the environment, and the second data may include a second accuracy value and a second estimate of the region of interest. Based on the first data and the second data, the vehicle may combine the first estimate of the region of interest and the second estimate of the region of interest.