Driving Assistance Pattern Matching for Similar Hazard Locations
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Solution Overview
Problem
Existing driving assistance systems fail to provide assistance in places similar to those requiring assistance, limiting their effectiveness in recognizing and responding to potential hazards.
Innovation Solution
A driving assistance device that utilizes a memory to store assistance patterns based on periphery sensor maps and sensor information, performing driving assistance when similar conditions are detected by comparing sensor data with stored patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If driving assistance is performed only at the exact place where near miss occurs, then the assistance is precise to the specific location, but it cannot provide assistance at similar places which also pose hazards
Solution Approach 1:
The system creates a copy of the periphery sensor map from the near miss location and stores it as a reference pattern. When the vehicle encounters a similar periphery sensor map pattern, the system recognizes it as a comparable hazard situation and applies the same assistance response, enabling assistance at similar places without losing the essential spatial pattern information
Solution Approach 2:
The system performs preliminary action by pre-storing periphery sensor maps and their corresponding assistance patterns in memory before similar hazard situations occur. When a similar situation is detected through pattern matching, the pre-stored assistance pattern is immediately applied, enabling rapid response at similar locations
2Adaptability or versatility
If the system stores and compares detailed periphery sensor maps to recognize similar places, then it can provide assistance at similar locations, but the computational complexity and data processing requirements increase
Solution Approach 1:
Instead of complex real-time analysis of the entire environment, the system creates simplified copies (periphery sensor maps) that capture only the essential spatial pattern information needed for hazard recognition. This copying approach enables similarity recognition while keeping the data structure manageable and the system relatively simple
3Measurement precision
If the system uses sensor information to create periphery sensor maps for pattern recognition, then it can identify similar hazard locations, but the data processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential spatial pattern information from sensor data to create periphery sensor maps, discarding redundant details. This extraction process maintains hazard recognition accuracy by preserving key spatial relationships while significantly reducing data processing time and computational resource requirements
Solution Approach 2:
The system creates simplified copies of periphery sensor maps that retain the essential spatial patterns needed for hazard recognition. These compact map representations enable rapid comparison and pattern matching, achieving accurate hazard identification at similar locations without excessive processing time
Data Source
AI summary
A memory stores an assistance pattern including a first periphery sensor map indicating a map in a periphery of a vehicle and an assistance content of driving assistance of the vehicle corresponding to the first periphery sensor map. A processor (sensor information acquisition unit 101) acquires sensor information indicating information on an object in the periphery of the vehicle sensed by a sensor. A processor (periphery sensor map acquisition unit 102) acquires a second periphery sensor map based on the sensor information. A processor (map similarity search unit 103, and assistance instruction unit 104) performs driving assistance of the assistance content corresponding to the first periphery sensor map when the second periphery sensor map is similar to the first periphery sensor map.