Sun-Aware Autonomous Vehicle Routing for Traffic Light Detection
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Solution Overview
Problem
Autonomous vehicles equipped with camera sensor systems face perception degradation due to sunlight interference, leading to incorrect traffic light detection and potentially hazardous maneuvers.
Innovation Solution
A computing system that identifies locations and orientations where sunlight causes perception degradation, allowing the vehicle to adjust its sensor system's position, orientation, or route to mitigate this issue by using sun-aware routing and control techniques, including data from HD maps and weather conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the camera sensor system operates at a predetermined sampling rate to detect traffic light changes, then the vehicle can respond to traffic light state changes, but sunlight backlight causes perception degradation and misperception of traffic light configuration
Solution Approach 1:
The system pre-calculates sun position and trajectory for upcoming time intervals based on geographic location, date, and time. This preliminary action allows the routing system to anticipate sunlight interference before it occurs and proactively adjust the route or sensor orientation to avoid the harmful backlight conditions, thereby maintaining traffic light detection accuracy.
Solution Approach 2:
The system dynamically adjusts the vehicle's route or sensor system orientation in real-time based on current sun position and predicted trajectory. This dynamic adaptation allows the vehicle to continuously optimize its path to minimize sunlight interference with the camera sensor system while maintaining efficient navigation.
2Area of stationary object
If the sun is positioned such that both traffic light and sun are included in the same camera frame, then the camera sensor system captures the scene, but the backlight from the sun causes the computing system to misperceive the illuminated configuration of the traffic light
Solution Approach 1:
The system resolves the conflict between maintaining field of view coverage and avoiding sunlight interference by introducing a temporal dimension. It calculates the sun's position and trajectory over time, allowing the routing system to schedule route adjustments or sensor reorientations that move the camera's field of view out of the sun's backlight zone while still capturing the traffic light when needed.
Solution Approach 2:
The routing system acts as an intermediary between the camera sensor system and the sunlight environment. It processes information about sun position, camera orientation, and traffic light locations to determine optimal routing decisions that mediate between maintaining scene coverage and avoiding harmful backlight conditions.
3Illumination intensity
If the backlight from the sun is directed toward the camera sensor system with high intensity, then the sun appears bright in the frame, but the illuminated configuration of the traffic light becomes unperceivable by the computing system
Solution Approach 1:
The system applies preliminary anti-action by pre-calculating and preventing the harmful effect of sun backlight before it occurs. By computing the sun's trajectory and its potential to cause overexposure, the routing system proactively adjusts the route or sensor orientation to avoid positions where the sun would be directed at high intensity toward the camera, thereby preventing loss of traffic light signal information.
Solution Approach 2:
The system uses feedback from the camera sensor system about current sun position and illumination conditions to continuously adjust routing decisions. When the sensor system detects or the system calculates that sun backlight intensity is approaching levels that would cause traffic light information loss, feedback triggers route adjustments to restore optimal detection conditions.
Data Source
AI summary
Sun-aware routing and controls of an autonomous vehicle is described herein. A location and an orientation of a sensor system is identified within an environment of the autonomous vehicle to determine whether the sun causes a threshold level of perception degradation to the sensor system incident to generating a sensor signal indicative of a traffic light. The determination is based upon perception degradation data that can be precomputed for locations and orientations within the environment for dates and times of day. The perception degradation data is based upon the location of at least one traffic light and positions of the sun relative to the locations and the orientations within the environment. A mechanical system of the autonomous vehicle is controlled to execute a maneuver that reduces perception degradation to the sensor system when the perception degradation is determined to exceed the threshold level of perception degradation.