Vehicle Trafficability Analysis via Zone Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current driver assistance systems that aim to prevent accidents through automatic avoiding maneuvers lack reliable information on the trafficability of alternative paths, which is crucial for minimizing damage and ensuring safety.
Innovation Solution
A method and device that analyze image data from various sources (camera, radar, lidar) to identify different zones and assess their trafficability by estimating a ground plane and considering driving activities, allowing for rapid and reliable trafficability analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a driver assistance system performs comprehensive trafficability analysis to ensure safe avoiding maneuvers, then the reliability of safety information is improved, but the computational time and complexity increase
Solution Approach 1:
The patent divides the surrounding area into multiple zones (e.g., own lane, neighboring lanes, oncoming traffic zones) and analyzes each zone separately for trafficability. This segmentation allows the system to process information in manageable units, improving computational efficiency while maintaining comprehensive safety assessment across all relevant areas
Solution Approach 2:
The system performs preliminary identification of different zones and their basic trafficability characteristics before an avoiding maneuver is required. By pre-processing and categorizing the environment into zones with known trafficability properties, the system reduces the computational burden during critical decision-making moments, enabling faster response when avoidance is needed
2Measurement precision
If the system analyzes all zones in detail to ensure accurate trafficability assessment, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent applies different analysis methods and levels of detail to different zones based on their specific characteristics and relevance to the vehicle. For example, the own lane may receive more detailed analysis than distant zones, and zones with detected driving activities may be analyzed with different precision levels. This localized approach maintains high accuracy where needed while reducing overall system complexity
Solution Approach 2:
The system introduces zone identification as an intermediary step between raw image data and final trafficability assessment. By first categorizing the environment into distinct zones and then analyzing trafficability within each zone separately, the system simplifies the overall processing complexity while maintaining comprehensive and accurate assessment capabilities
Data Source
AI summary
A method and a device for analyzing trafficability use a computer to perform the following steps:receiving image data representing an image of surroundings in front of a vehicle,analyzing the image data to identify different zones in the image of the surroundings, andanalyzing the identified different zones in terms of trafficability thereof for the vehicle.