Sensor-Triggered Navigational Map Updates for Autonomous Driving
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Solution Overview
Problem
Autonomous driving systems face challenges in accurately perceiving and predicting the driving environment due to outdated navigational maps, which can lead to unsafe decisions, necessitating the ability to update maps in real-time using sensor data.
Innovation Solution
A control system for autonomous vehicles that includes a processor and memory with instructions to receive sensor data, detect inconsistencies between sensor output and navigational maps, trigger real-time mapping, and update the maps, using an anomaly detection module and real-time mapping module, while conserving computational resources by only updating when significant inconsistencies are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time mapping is continuously performed to update navigational maps, then map accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system performs real-time mapping selectively rather than continuously. The anomaly detection module triggers real-time mapping only when inconsistencies are detected between sensor data and the navigational map, performing partial updates rather than complete continuous mapping, thus reducing computational resource consumption while maintaining map accuracy where needed
Solution Approach 2:
The system uses feedback from the anomaly detection module to control real-time mapping operations. The anomaly detection module continuously monitors for inconsistencies and provides feedback signals that trigger real-time mapping only when necessary, creating a closed-loop control system that optimizes computational resource usage based on actual map accuracy needs
2Reliability
If real-time mapping is triggered frequently to correct map inconsistencies, then navigation safety is improved, but system response time increases
Solution Approach 1:
The anomaly detection module serves as an intermediary between sensor data input and real-time mapping execution. It filters and pre-processes sensor data to identify only significant inconsistencies, acting as a gatekeeper that prevents unnecessary real-time mapping operations and reduces system response time while maintaining navigation safety through selective updates
3Measurement precision
If sensor data is continuously processed to detect inconsistencies, then map update accuracy is improved, but computational load increases
Solution Approach 1:
The system extracts only the essential inconsistency detection function from complete sensor data processing. The anomaly detection module focuses specifically on identifying discrepancies between sensor data and navigational maps, separating this critical function from other processing tasks, thereby improving inconsistency detection accuracy while reducing overall computational load through functional decomposition
Data Source
AI summary
Systems and methods for updating navigational maps based using at least one sensor are provided. In one aspect, a control system for an autonomous vehicle, includes a processor and a computer-readable memory configured to cause the processor to: receive output from at least one sensor located on the autonomous vehicle indicative of a driving environment of the autonomous vehicle, retrieve a navigational map used for driving the autonomous vehicle, and detect one or more inconsistencies between the output of the at least one sensor and the navigational map. The computer-readable memory is further configured to cause the processor to: in response to detecting the one or more inconsistencies, trigger mapping of the driving environment based on the output of the at least one sensor, update the navigational map based on the mapped driving environment, and drive the autonomous vehicle using the updated navigational map.