Autonomous Vehicle Speed Limit Adjustment via Dynamic Detection Distance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous driving vehicles face challenges in adjusting speed limits effectively due to static high-definition map information, which does not account for dynamic weather conditions and traffic changes, impacting their perception range.
Innovation Solution
The system calculates a detection distance by tracking stable objects within the vehicle's field of view, identifying a subset of objects with the longest distances, and using this data to adjust speed limits through a predetermined algorithm, providing a dynamic and accurate perception range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speed limits are adjusted based on static high-definition map information, then the vehicle can maintain a predetermined speed limit, but the speed limit adjustment cannot account for dynamic weather conditions and traffic changes
Solution Approach 1:
The patent transforms the static speed limit adjustment system into a dynamic one by continuously updating the detection distance based on real-time tracking of stable objects. The system calculates detection distance dynamically using current environmental conditions and traffic状况, allowing the speed limit to adapt to changing weather and traffic conditions while maintaining reliability through systematic calculation methods
Solution Approach 2:
The system implements feedback by continuously tracking objects in the vehicle's field of view, calculating detection distances, and using this information to adjust speed limits. The perception system provides ongoing feedback about environmental conditions, enabling the vehicle to adapt its speed limit dynamically while maintaining reliable operation through systematic feedback loops
2Device complexity
If the vehicle uses a fixed detection distance, then the system is simple to implement, but it cannot accurately reflect real-time perception range under varying weather and traffic conditions
Solution Approach 1:
The system performs preliminary action by pre-establishing the methodology for tracking stable objects and calculating detection distances before actual operation. The framework for identifying stable objects and computing detection distance is prepared in advance, allowing the system to quickly adapt to real-time conditions without complex runtime decision-making
Solution Approach 2:
The perception system serves itself by automatically tracking objects, identifying stable ones, calculating detection distances, and updating speed limits without external intervention. The system uses its own sensor data and processing capabilities to maintain accurate perception range measurement, reducing the need for external calibration or adjustment mechanisms
Data Source
AI summary
In one embodiment, a method of adjusting a speed limit of an ADV includes the operations of tracking objects within a field of view of the ADV; and identifying a set of stable objects from the objects tracked by the ADV based on a set of requirements. The method further includes the operations of identifying a subset of objects from the set of stable objects, the subset of objects having longest distances to the ADV; calculating a detection distance by averaging distances from the subset of stable obstacles to the ADV; and adjusting the speed limit of the ADV based on the detection distance using a predetermined algorithm.