Autonomous Vehicle Control via Sensor-Map Correlation
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Solution Overview
Problem
Autonomous vehicles face challenges in managing interactions with human-controlled vehicles and navigating situations where sensor data mismatches with map data, particularly in managing right-of-way and detecting hidden obstacles.
Innovation Solution
The system employs computing devices to develop and adjust control strategies based on periodic comparisons of sensor data with map data, using correlation rates to switch between default and alternative strategies, and incorporates external sensor data to verify vehicle position and adapt lane selection strategies, while also updating shared maps with object information and activating visible signals to indicate autonomous control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle relies solely on sensor data for navigation and hazard detection, then the vehicle can operate independently without constant map updates, but the vehicle may fail to detect hidden obstacles or navigate accurately when sensor data is incomplete or ambiguous
Solution Approach 1:
The patent combines sensor data with map data to create a comprehensive navigation system. The computing device receives both sensor data from vehicle-mounted sensors and map data from external sources, then processes them together to determine vehicle position, detect hazards, and generate control signals. This merging of data sources improves navigation reliability by compensating for the limitations of individual data sources.
2Reliability
If the vehicle uses multiple data sources including central map database and real-time sensor data, then the vehicle can achieve better navigation accuracy and hazard detection, but the system complexity and computational requirements increase
Solution Approach 1:
The computing device acts as an intermediary that receives, processes, and integrates data from multiple sources including sensor data, map data, and hazard information. It correlates this data to determine vehicle position relative to map features and generates control signals based on the integrated information. This intermediary processing layer manages the complexity of multiple data sources while achieving reliable hazard detection.
3Reliability
If the autonomous vehicle adopts a conservative control strategy that prioritizes safety over efficiency, then the vehicle can safely navigate uncertain situations, but the vehicle's productivity and travel time increase
Solution Approach 1:
The control strategy dynamically adjusts based on the correlation between sensor data and map data. When correlation is high (clear visibility, confident position), the vehicle can adopt more efficient routing and speed strategies. When correlation drops (hidden obstacles, ambiguous position), the vehicle automatically adopts more conservative safety-oriented strategies. This dynamic adjustment optimizes the balance between safety and productivity based on real-time conditions.
Data Source
AI summary
A method for operating an automated vehicle includes controlling by one or more computing devices an autonomous vehicle; receiving by one or more computing devices sensor data from the vehicle corresponding to moving objects in a vicinity of the vehicle; receiving by one or more computing devices road condition data; and determining by one or more computing devices undesirable locations related to the moving objects. The undesirable locations related to the moving objects for the vehicle are based at least in part on the road condition data. The step of controlling the vehicle includes avoiding the undesirable locations.