Autonomous Navigation Clearance Detection Under Overhead Obstacles
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Solution Overview
Problem
Conventional autonomous navigation systems face challenges in accurately navigating vehicles under overhead obstacles due to insufficient or inaccurate map information and the inability to detect changes in the environment, leading to potential collisions.
Innovation Solution
The system employs a safe navigation module that generates detection and estimation data by comparing map data with real-time sensor data, allowing for the detection of changes in the environment and the estimation of clearance under obstacles, enabling the vehicle to adjust its route or speed to avoid hazards and update the map with new information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional autonomous navigation systems rely on pre-stored map data for navigation, then the system can operate with simple processing, but the system cannot detect environmental changes leading to potential collisions
Solution Approach 1:
The patent combines pre-stored map data with real-time sensor detection data into a unified navigation decision-making system. The map module provides prior environmental information while the sensor module captures current conditions, and their integration enables both safety and adaptability without requiring completely separate systems
Solution Approach 2:
The system performs preliminary actions by pre-storing map data that includes information about overhead obstacles and their characteristics. This advance preparation allows the system to quickly compare real-time sensor data against known map features, enabling rapid detection of environmental changes without complex real-time processing
2Measurement precision
If the system uses detailed map data and real-time sensor data for environment comparison, then detection accuracy improves, but data processing time increases
Solution Approach 1:
The patent extracts only the relevant features and data points needed for comparison between map data and sensor data, rather than processing entire datasets. By focusing on key environmental features such as overhead obstacle positions and characteristics, the system achieves high detection accuracy while minimizing processing time
Solution Approach 2:
The system applies partial action by performing detailed comparison only for areas where environmental changes are suspected or where overhead obstacles are located, rather than uniformly processing the entire environment. This selective approach maintains high accuracy for critical areas while reducing overall processing time
3Measurement precision
If the vehicle reduces speed to improve measurement accuracy of overhead obstacles, then clearance estimation improves, but navigation efficiency decreases
Solution Approach 1:
The system applies partial action by reducing speed only when approaching overhead obstacles or when clearance estimation is critical, rather than maintaining reduced speed throughout the entire navigation. This selective speed adjustment maintains high measurement precision when needed while preserving navigation efficiency during normal driving conditions
Solution Approach 2:
The system performs preliminary identification of overhead obstacles using map data and distant sensor detection, allowing it to prepare for accurate clearance measurement in advance. By identifying potential hazards early, the system can optimize speed adjustments only when necessary, maintaining overall navigation efficiency while ensuring accurate measurements at critical moments
Data Source
AI summary
Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining map data and detection data for an area in an environment; determining a change in the area based on the map data and the detection data; and generating control data based on the change.