Autonomous Vehicle Navigation Path Correction Engine
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Solution Overview
Problem
Autonomous vehicles face navigation safety issues due to inaccurate or outdated navigation maps, leading to potential collisions with undetected obstacles, as existing high-precision map maintenance is resource-intensive and costly.
Innovation Solution
A method and system that corrects pre-generated navigation paths for autonomous vehicles by determining new data points within the vehicle's environmental field of view and calculating offsets to adjust the path, ensuring safe distances from obstacles, using a navigation path correction engine that processes sensor data and updates the path in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-precision navigation maps are maintained and updated continuously, then navigation accuracy is improved, but cost and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by capturing environmental data ahead of time using sensors (LIDAR, cameras, radar) and pre-processing this data to identify new data points and obstacles before navigation is needed. This preliminary data capture and analysis allows the system to update navigation paths without requiring continuous full-map regeneration, reducing resource consumption while maintaining accuracy
Solution Approach 2:
The navigation correction is segmented into specific regions rather than updating the entire map. The system divides the environment into relevant zones around the vehicle's path, processes only those segments where obstacles or changes are detected, and updates only the affected navigation path portions. This segmentation significantly reduces computational resources and processing time compared to full-map updates
2Reliability
If complete navigation map coverage is ensured, then navigation safety is improved, but system complexity increases
Solution Approach 1:
The system applies local quality by focusing detailed environmental analysis and obstacle detection specifically in the vehicle's immediate vicinity and along the planned navigation path, rather than uniformly processing the entire map. Sensors and processing resources are concentrated on local regions where navigation decisions are critical, maintaining safety while reducing overall system complexity
Solution Approach 2:
The system performs partial action by processing and analyzing only the portion of the environment that is relevant to current navigation needs. Instead of continuously processing all sensor data from the entire operational area, the system selectively processes data within a defined field of view and pre-defined regions around the vehicle, achieving sufficient safety coverage with reduced computational burden
3Measurement precision
If real-time environmental data is processed, then obstacle detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing sensor data as it is captured, immediately identifying new data points and potential obstacles before full navigation path recalculation is required. This preliminary detection and filtering of relevant environmental features reduces the volume of data that needs intensive processing later, maintaining detection accuracy while reducing overall processing time
Solution Approach 2:
Environmental data processing is segmented into multiple stages: initial sensor data capture, preliminary feature extraction and obstacle identification, then focused analysis only of regions containing detected anomalies or changes. This segmented approach processes data in manageable chunks rather than attempting to analyze the entire environment simultaneously, reducing processing time while maintaining comprehensive obstacle detection accuracy
Data Source
AI summary
This disclosure relates generally to autonomous vehicle, and more particularly to method and system for correcting a pre-generated navigation path for an autonomous vehicle. In one embodiment, a method may be provided for correcting the pre-generated navigation path for the autonomous vehicle. The method may include receiving the pre-generated navigation path on a navigation map, an environmental field of view (FOV) of the autonomous vehicle, and a location of the autonomous vehicle on the pre-generated navigation path. The method may further include determining a set of new data points in a pre-defined region with respect to the location of autonomous vehicle based on the environmental FOV and the navigation map, determining an offset of the pre-generated navigation path in the pre-defined region based on the set of new data points, and correcting the pre-generated navigation path in the pre-defined region based on the offset.