Tollgate Detection via Road Context Probability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current driving assistance systems for warning drivers of approaching tollgates are limited by inaccurate mapping data, frequent database updates, and environmental factors like worn-off signs or loss of GPS coverage, leading to unreliable detection and warning.
Innovation Solution
A method and system that calculates the probability of a tollgate presence using decorrelated road context attributes such as speed limit signs, marking lines, speed bumps, obstacles, and drivable space, processed from camera and sensor data, with confidence indices and decision thresholds for robust and reliable warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS navigation system and map data are used to detect tollgate approach, then the system can provide warning functionality, but the detection reliability is poor due to inaccurate mapping and GPS coverage loss
Solution Approach 1:
The system segments the detection task into multiple independent road context attributes (speed limit signs, marking lines, speed bumps, obstacles, drivable space) rather than relying on a single GPS-based detection method. Each attribute is detected separately by different sensors, and their combined analysis improves overall detection reliability despite individual measurement limitations.
Solution Approach 2:
The system merges data from multiple independent sources (camera images, radar measurements, lidar data, vehicle sensor readings) to detect road context attributes. By combining these diverse detection sources, the system compensates for individual sensor limitations and achieves more reliable tollgate approach detection than any single system could provide.
2Reliability
If specific tollgate information is stored in on-board database for recognition, then detection can be performed, but the system requires frequent database updates and is sensitive to environmental changes
Solution Approach 1:
The system detects road context attributes directly from the current road environment using onboard sensors without requiring pre-stored tollgate information or frequent database updates. The detection is self-service in that it relies on universally present road features rather than requiring external information updates, making the system adaptable to any tollgate location without maintenance overhead.
Solution Approach 2:
The system changes the detection parameters from specific tollgate identifiers (sign patterns, visual structures) to general road context attributes (speed limit signs, marking lines, speed bumps) that are universally present and stable. This parameter transformation makes detection robust to environmental changes like worn signs or dirt without requiring database updates.
3Reliability
If multiple road context attributes are analyzed to calculate tollgate presence probability, then detection robustness improves, but system complexity increases
Solution Approach 1:
The system uses a unified probability calculation framework that processes multiple road context attributes through a common decision-making mechanism. This multi-functional approach allows the same processing module to handle different attribute types (visual, spatial, motion-based) and combine them into a single tollgate presence probability, managing complexity through universal processing logic.
Solution Approach 2:
The system replaces complex rule-based detection logic with a probabilistic calculation approach. Instead of implementing separate detection rules for each road context attribute, the system uses probability theory to combine multiple attributes, substituting mechanical rule complexity with mathematical probability computation that is more manageable and scalable.
Data Source
AI summary
A driving assistance functionality for a motor vehicle when approaching a tollgate is disclosed. The method involves a step (S4) of calculating a probability of a tollgate being present based on at least two road context attributes that are determined from the motor vehicle and defining a road context ahead of said vehicle, said road context attributes being decorrelated from any concept of a tollgate. Examples of road context attributes: speed limit signs; marking lines on the ground; speed bumps or rumble strips on the ground; obstacles such as other vehicles; drivable space.