Sun-Aware Vehicle Routing for Camera Glare Avoidance
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining image data quality due to sunlight interference, particularly when the sun is proximate to objects within the field of view of the image capture device, leading to issues like blooming and degraded image resolution, which can hinder the vehicle's ability to determine traffic signal states and perform autonomous operations.
Innovation Solution
A computing system determines the expected position of the sun relative to a geographic area using ephemeris data and predicts its position in the vehicle's field of view, allowing it to generate a route that avoids situations where the sun would be proximate to objects, thereby preventing sunlight interference and maintaining image data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the vehicle travels through locations where the sun is proximate to objects in the field of view, then the route is more direct and faster, but the image data quality degrades due to sunlight interference
Solution Approach 1:
The computing system determines the expected position of the sun relative to the geographic area and predicts potential sunlight interference before the vehicle reaches problematic locations. By calculating sun positions using ephemeris data and comparing them with the vehicle's planned route, the system proactively identifies locations where the sun would be proximate to objects in the field of view, allowing route adjustments to be made in advance rather than reacting to degraded image quality after it occurs.
2Manufacturing precision
If the vehicle avoids locations where the sun is proximate to objects in the field of view, then the image data quality is maintained, but the route becomes longer and travel time increases
Solution Approach 1:
The computing system dynamically adjusts the vehicle's route parameters based on sun position calculations and predicted sunlight interference. By changing the route parameters (avoiding specific locations where the sun would interfere with the field of view) only when necessary, the system maintains image data quality while minimizing the impact on travel time. The system evaluates multiple route options and selects the optimal path that balances avoidance of sunlight interference with efficient travel.
3Manufacturing precision
If the computing system calculates and adjusts routes based on sun position, then image data quality is maintained, but the computational complexity and processing requirements increase
Solution Approach 1:
The computing system extracts only the necessary information for sun position calculation from complex ephemeris data, focusing on the key parameters needed to determine sun location relative to the vehicle's route. By extracting and processing only the relevant data elements rather than analyzing complete astronomical datasets, the system reduces computational complexity while still accurately predicting sunlight interference and making appropriate route adjustments.
Data Source
AI summary
Example implementations may relate to sun-aware vehicle routing. In particular, a computing system of a vehicle may determine an expected position of the sun relative to a geographic area. Based on the expected position, the computing system may make a determination that travel of the vehicle through certain location(s) within the geographic area is expected to result in the sun being proximate to an object within a field of view of the vehicle's image capture device. Responsively, the computing system may generate a route for the vehicle in the geographic area based at least on the route avoiding travel of the vehicle through these certain location(s), and may then operate the vehicle to travel in accordance with the generated route. Ultimately, this may help reduce or prevent situations where quality of image(s) degrades due to sunlight, which may allow for use of these image(s) as basis for operating the vehicle.