Autonomous Vehicle Stopping Place Selection via Dynamic Map Updates
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Solution Overview
Problem
Autonomous vehicles face challenges in identifying and selecting suitable stopping places that are both acceptable and feasible near a specified goal position, due to factors like temporary obstacles, insufficient map information, and navigation restrictions, which can lead to difficulties in safely and reliably stopping for activities such as passenger pickup or drop-off.
Innovation Solution
The system analyzes a combination of static and dynamic information using sensors and map data to continuously update the selection of a stopping place, applying strategies such as prioritizing the first feasible or most desirable location, relaxing thresholds for acceptability and feasibility, and expanding the search area if necessary, while also considering passenger input and enabling remote control if a suitable stop cannot be found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses traditional methods to identify stopping places, then the system complexity is reduced, but the reliability of stopping place selection deteriorates in dynamic environments
Solution Approach 1:
The system dynamically adjusts stopping place selection by continuously monitoring environmental changes and updating the list of feasible stopping places. The criteria for acceptability and feasibility are dynamically modified based on real-time conditions such as temporary obstacles, traffic状况, and environmental changes, allowing the vehicle to adapt to dynamic environments while maintaining reliable stopping place selection.
Solution Approach 2:
The system incorporates feedback mechanisms where sensor data continuously informs the stopping place selection process. The autonomous vehicle monitors the environment, compares it against stored map data, and adjusts its stopping place choices based on real-time feedback from sensors detecting obstacles, traffic conditions, and other dynamic factors, thereby improving reliability without requiring overly complex predetermined rules.
2Reliability
If the autonomous vehicle analyzes more current information about potential stopping places, then the reliability of stopping place selection is improved, but the time required to identify a stopping place increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying and storing potential stopping places in map data before the vehicle reaches those locations. The autonomous vehicle maintains a pre-sorted list of candidate stopping places based on stored information, allowing it to quickly select from pre-processed options rather than analyzing every possible location in real-time, thus reducing identification time while maintaining reliability.
Solution Approach 2:
The system applies partial action by analyzing only the most relevant current information about potential stopping places rather than exhaustively evaluating all possible locations. The autonomous vehicle focuses sensor analysis on specific areas and uses selective updating of stopping place feasibility based on immediate environmental changes, achieving sufficient reliability without the time cost of complete environmental analysis.
3Reliability
If the autonomous vehicle strictly adheres to navigation restrictions and map information, then the compliance with road rules is improved, but the adaptability to temporary obstacles and dynamic conditions deteriorates
Solution Approach 1:
The system dynamically reconciles navigation restrictions with adaptability by continuously comparing stored map data against real-time sensor observations. When temporary obstacles or dynamic conditions conflict with pre-stored navigation rules, the system adapts by updating the feasibility status of stopping places while still respecting fundamental navigation restrictions. This dynamic adjustment allows compliance with road rules while adapting to temporary conditions such as construction zones or unexpected obstacles.
Solution Approach 2:
The system uses feedback from real-time sensors to monitor whether current conditions violate stored navigation restrictions or map information. When discrepancies are detected, the autonomous vehicle adjusts its stopping place selection to maintain compliance with fundamental navigation rules while adapting to temporary conditions. The feedback mechanism ensures that adaptability does not compromise navigation restriction compliance by continuously validating choices against stored regulatory information.
Data Source
AI summary
Among other things, stored data is maintained indicative of potential stopping places that are currently feasible stopping places for a vehicle within a region. The potential stopping places are identified as part of static map data for the region. Current signals are received from sensors or one or more other sources current signals representing perceptions of actual conditions at one or more of the potential stopping places. The stored data is updated based on changes in the perceptions of actual conditions. The updated stored data is exposed to a process that selects a stopping place for the vehicle from among the currently feasible stopping places.