Autonomous Driving Strategy for TTCD Cluster Lane Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of ensuring safety and robustness in autonomous driving systems when encountering complex road conditions, such as construction or accidents, due to limited sensing capabilities and the need to accurately identify and respond to temporary traffic control devices like traffic cones.
Innovation Solution
A decision-making method that determines a driving strategy by analyzing the distribution status of temporary traffic control devices (TTCDs) in a lane, using vehicle-mounted sensors to identify TTCD clusters and their relative positions, allowing for strategies like obstacle bypass, lane change, or braking, thereby improving safety and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the automated driving system uses limited sensing capabilities to detect road conditions, then the system can operate with simpler hardware, but the safety and robustness of the driving strategy cannot be ensured due to incomplete detection
Solution Approach 1:
The patent segments the TTCD detection task by dividing the lane into multiple detection zones and using multiple sensors positioned at different locations. Each sensor detects TTCDs in its specific zone, and the system integrates these segmented detection results to form a complete picture of TTCD distribution, thereby overcoming the limitation of individual sensor coverage
Solution Approach 2:
The system performs preliminary detection and classification of TTCDs before making driving decisions. By pre-identifying TTCD clusters and their distribution patterns in advance, the system can prepare appropriate driving strategies ahead of time, improving both safety and response accuracy without requiring complex real-time sensing
2Measurement precision
If the system determines driving strategy based on comprehensive TTCD distribution analysis, then the decision-making accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies local quality by focusing computational resources on analyzing only the relevant TTCD distribution patterns that directly impact driving decisions. Instead of processing all possible road conditions equally, the system identifies and analyzes specific local features of TTCD clusters (such as their position relative to the vehicle, density, and arrangement patterns) that are most critical for determining the appropriate driving strategy
Solution Approach 2:
The system performs partial analysis by concentrating on the most significant TTCD distribution characteristics rather than attempting to analyze every aspect of the road environment. By focusing on key parameters such as TTCD cluster position, density, and type, the system achieves sufficient decision-making accuracy without the excessive computational complexity that would result from comprehensive analysis of all possible variables
3Productivity
If the vehicle maintains current speed when encountering TTCDs, then the productivity is maintained, but the collision risk increases when TTCD distribution indicates potential hazards
Solution Approach 1:
The patent implements dynamic speed adjustment by continuously monitoring TTCD distribution and adapting the vehicle speed in real-time. When TTCD patterns indicate low risk (such as sparse or clearly marked obstacles), the vehicle maintains higher speeds to preserve productivity. When patterns indicate high risk (such as dense clusters or ambiguous arrangements), the system automatically reduces speed to minimize collision risk, creating a dynamic balance between efficiency and safety
Data Source
AI summary
A method includes obtaining first environment information of a first lane, where a vehicle travels in the first lane; determining, based on the first environment information, that a first temporary traffic control device (TTCD) cluster is distributed in the first lane, where the first TTCD cluster includes at least one TTCD; determining a distribution status of the first TTCD cluster in the first lane, where the distribution status indicates a relative position relationship between the at least one TTCD and the first lane; and determining a driving strategy based on the distribution status of the first TTCD cluster in the first lane.