Environment Recognition System for Vehicle Contact Risk Prediction
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Solution Overview
Problem
Existing vehicle detection systems face difficulties in predicting the possibility of contact with traffic participants, especially when they appear suddenly from behind large vehicles or in occlusion regions, leading to challenges in vehicle behavior control such as braking and steering.
Innovation Solution
An environment recognition system that uses a database of reference symbol strings to evaluate the similarity between detected scenes and stored reference scenes, identifying potential risks and generating commands for vehicle control to mitigate these risks, including notification, acceleration suppression, and speed changes based on evaluated risk levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor-based detection methods are used to detect traffic participants, then the system can detect visible traffic participants, but it fails to predict contact risk when traffic participants suddenly appear from behind large vehicles or in occlusion regions
Solution Approach 1:
The system performs preliminary action by pre-storing reference symbol strings representing various environmental scenes in a database before actual detection occurs. When detecting the current scene, the system compares generated symbol strings against these pre-stored references to predict potential contact risks, enabling proactive risk assessment before actual contact becomes imminent.
Solution Approach 2:
The system creates a copy of the current environmental scene by generating a symbol string representation from sensor data. This copied symbolic representation is then compared against stored reference symbol strings to identify similar scenarios, allowing the system to infer risk based on historical pattern matching rather than direct observation of potential hazards.
2Reliability
If the system evaluates similarity between detected scenes and stored reference scenes using symbol strings, then the reliability of risk assessment is improved, but the computational complexity increases
Solution Approach 1:
The system extracts essential environmental features by converting complex sensor data into simplified symbol strings that capture key scene characteristics. This extraction process reduces the complexity of data while preserving essential information needed for risk assessment, making the similarity comparison more efficient.
Solution Approach 2:
The system changes the representation parameters of environmental data from raw sensor formats to symbolic representations. This parameter transformation enables more efficient storage and comparison operations, as symbol strings can be directly matched against reference data without requiring complex multi-sensor fusion or deep learning inference during runtime.
Data Source
AI summary
Provided is a system capable of further reducing risk such as a contact between a moving body such as a vehicle and a traffic participant present around the moving body. According to an environment recognition system (1) of the present invention, a database (10) stores each of a plurality of reference symbol strings describing the state of an environmental element constituting each of a plurality of scenes assumed to be around the moving body. A first arithmetic processing element (11) detects a scene around the moving body and generates a symbol string describing the state of the environmental element constituting the detected scene. A second arithmetic processing element (12) evaluates similarity between the symbol string and each of the plurality of reference symbol strings stored in the database (10).