Autonomous Lane Validation Using Sensor Feedback for Map Updates
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Solution Overview
Problem
Maintaining high-quality virtual maps for autonomous vehicles is difficult, costly, and inefficient, leading to inaccurate navigation and avoidance of low-quality areas, which results in these areas degrading over time.
Innovation Solution
A system and method for real-time lane validation using autonomous vehicles, where sensor data is collected and compared to virtual map data, allowing for immediate updates and identification of discrepancies, enabling the vehicle to safely traverse and update low-quality areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional map maintenance methods are used, then map quality is maintained in high-traffic areas, but low-quality areas are avoided and continue to degrade
Solution Approach 1:
Autonomous vehicles perform self-validation by comparing their sensor data with virtual map data, enabling the system to automatically identify and update low-quality areas without human intervention. The vehicle's own navigation experience contributes to map improvement, creating a self-service mechanism that continuously enhances map quality across all areas including previously neglected low-quality zones.
Solution Approach 2:
The system implements a feedback loop where autonomous vehicles collect sensor data, compare it with virtual map data, identify discrepancies, and trigger updates. This closed-loop feedback mechanism ensures that map quality is continuously improved based on real-world observations from vehicle operations, addressing both high and low-quality areas systematically.
2Reliability
If autonomous vehicles avoid low-quality areas, then navigation safety is maintained, but map degradation continues in those areas
Solution Approach 1:
The system performs preliminary validation by comparing sensor data with virtual map data before navigation decisions are made. Discrepancies are identified in advance, allowing the system to update low-quality areas proactively rather than reactively, ensuring map accuracy is maintained without compromising navigation safety.
Solution Approach 2:
Sensor data acts as an intermediary between the autonomous vehicle and the virtual map. By comparing this intermediate data with map data, the system can identify discrepancies and trigger updates, serving as a mediator that bridges the gap between navigation safety requirements and map quality improvement needs.
3Measurement precision
If manual map updating is used, then map accuracy can be verified, but the process is difficult and costly
Solution Approach 1:
The patent replaces manual mechanical map updating processes with an automated electronic system. Autonomous vehicles automatically collect sensor data, compare it with virtual map data using computational algorithms, and trigger updates through electronic communication. This substitution eliminates the need for manual field verification and significantly reduces maintenance difficulty and cost while maintaining high measurement precision.
Solution Approach 2:
The autonomous vehicle's sensor system serves multiple functions: navigation, obstacle detection, and map validation. By making the sensor system universal, the patent eliminates the need for separate manual verification processes, reducing overall system complexity and cost while maintaining map accuracy through the vehicle's existing operational data.
Data Source
AI summary
The subject disclosure relates to techniques for real-time lane validation. A process of the disclosed technologies can include steps for receiving a route from a remote computing system, the route indicating lanes on the route that an autonomous vehicle must traverse, traversing at least one lane of the lanes on the route, and sending sensor data to the remote computing system after traversing the at least one lane, the sensor data indicating whether the at least one lane is in accordance with a virtual lane of a virtual map, the virtual lane corresponding to the at least one lane.