Ambient Lighting Maps for Autonomous Vehicle Pickup Stops
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Solution Overview
Problem
Autonomous vehicles face challenges in coordinating pickups and drop-offs due to difficulties in identifying suitable locations with adequate ambient lighting, which affects passenger comfort and safety, especially at night or in low-light conditions.
Innovation Solution
A method is developed to generate a map of ambient lighting conditions using data from vehicles, allowing autonomous vehicles to identify and select well-lit stopping locations based on historical and real-time data, ensuring safer and more comfortable passenger experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicles use predetermined stopping locations without considering ambient lighting, then vehicle operations are simplified and more efficient, but passenger comfort and safety deteriorate due to inadequate lighting conditions
Solution Approach 1:
The system performs preliminary actions by collecting and storing ambient lighting data at various stopping locations in advance, organizing it into time-based buckets, and generating a map of lighting conditions before vehicles need to make stopping decisions. This allows vehicles to query pre-processed lighting information rather than collecting and analyzing data in real-time, maintaining operational efficiency while enabling informed stopping location selection that prioritizes passenger safety and comfort
Solution Approach 2:
The patent introduces an intermediary component - a centralized server that collects, processes, and stores ambient lighting data from multiple vehicles, then provides this processed information back to vehicles. This intermediary layer aggregates raw lighting data into useful patterns and makes it accessible to individual vehicles without requiring complex onboard processing, thus maintaining vehicle operational simplicity while enabling lighting-aware stopping decisions
2Object-affected harmful factors
If autonomous vehicles collect and process real-time ambient lighting data at each stopping location, then passenger safety and comfort improve, but system complexity and computational requirements increase
Solution Approach 1:
The system performs data collection, processing, and organization in advance - collecting ambient lighting data from multiple vehicles, organizing it into time-based buckets, and generating a map of lighting conditions before vehicles need to make stopping decisions. This preliminary processing shifts computational complexity from individual vehicles to a centralized system, allowing vehicles to simply query pre-processed information
Solution Approach 2:
The patent merges data collection and processing functions into a centralized server that aggregates ambient lighting data from multiple vehicles. Instead of each vehicle independently collecting and processing lighting data, the system combines resources and intelligence centrally, reducing individual vehicle complexity while improving overall system capability through aggregated data
3Object-affected harmful factors
If autonomous vehicles select stopping locations based on historical ambient lighting data, then passenger comfort and safety are enhanced, but the time required for location selection increases
Solution Approach 1:
The system performs preliminary actions by collecting, organizing, and generating ambient lighting maps in advance, storing data in time-based buckets that can be quickly queried. When a vehicle needs to select a stopping location, it can rapidly retrieve pre-organized lighting information for the relevant time period and location, avoiding time-consuming real-time data collection and analysis
Solution Approach 2:
The system applies local quality by organizing ambient lighting data specifically for stopping locations rather than all geographic areas, and by creating time-based buckets that provide locally optimized information for specific time periods. This localized organization allows vehicles to quickly access relevant lighting data without processing unnecessary information from other locations or time periods
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.