Autonomous Vehicle Stop Selection Using Ambient Lighting Maps
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Solution Overview
Problem
Autonomous vehicles face challenges in coordinating safe and comfortable passenger pickups and drop-offs, particularly in determining optimal locations based on ambient lighting conditions, which can lead to inconvenience for users.
Innovation Solution
A method is employed to generate a map of ambient lighting conditions using data from vehicles, allowing autonomous vehicles to identify suitable stopping locations based on historical and real-time lighting conditions, and adjust parking locations accordingly to ensure well-lit areas for pickups and drop-offs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous vehicles use predetermined stopping locations without considering ambient lighting conditions, then vehicle operation simplicity is improved, but passenger safety and comfort deteriorate
Solution Approach 1:
The system pre-generates maps of ambient lighting conditions for various locations and times before actual vehicle operation. This preliminary action allows the vehicle to later quickly query and select appropriate stopping locations without real-time complexity, maintaining operational simplicity while ensuring safety through advance lighting condition assessment
Solution Approach 2:
The patent introduces an intermediary lighting condition map that mediates between the vehicle's stopping location selection and the actual ambient lighting environment. This intermediary data structure allows the vehicle to make informed decisions about stopping locations by referencing pre-computed lighting information, thus improving safety without adding operational complexity
2Measurement precision
If autonomous vehicles collect and process real-time ambient lighting data from multiple sources, then stopping location accuracy is improved, but system complexity increases
Solution Approach 1:
The system merges multiple lighting data sources (sensor data, map data, time information) into a unified lighting condition map that consolidates all relevant information. This merging approach improves measurement precision by comprehensively considering all factors while reducing system complexity by providing a single integrated data structure for decision-making
Solution Approach 2:
The patent creates a simplified copy or representation of the complex real-world lighting environment in the form of a digital lighting map. This copy captures essential lighting characteristics without requiring the system to process all raw sensor data in real-time, thus improving accuracy while managing complexity through data abstraction
3Ease of operation
If autonomous vehicles dynamically adjust stopping locations based on lighting conditions, then passenger comfort is improved, but coordination efficiency deteriorates
Solution Approach 1:
The system performs preliminary organization of lighting condition data into structured maps before vehicle operation. This advance preparation allows the vehicle to efficiently query and select optimal stopping locations during operation, maintaining coordination efficiency while enabling dynamic adjustment for passenger comfort through pre-processed lighting information
Solution Approach 2:
The patent implements dynamic stopping location selection by allowing the vehicle to adjust its stopping position based on queried lighting condition data. This dynamic adaptation improves passenger comfort by ensuring stops occur in well-lit areas while maintaining coordination efficiency through efficient data querying and selection algorithms
Data Source
AI summary
The disclosure relates to using ambient lighting conditions with passenger and goods pickups and drop offs with autonomous vehicles. For instance, a map of ambient lighting conditions for stopping locations may be generated by receiving ambient lighting condition data for predetermined stopping locations and arranging this data into a plurality of buckets based on time and one of the stopping locations. A vehicle may then be controlled in an autonomous driving mode in order to stop for a passenger by both observing ambient lighting conditions for different stopping locations and, in some instances, also using the map.