Road User Prediction Modules for Timely Section Alarms
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Solution Overview
Problem
Current systems for warning vulnerable road users about potential dangers are limited by inaccurate predictions and late warnings, leading to unnecessary distractions and increased risk due to insufficiently reliable detection of future positions of road users.
Innovation Solution
An alarm system utilizing multiple prediction modules with descending quality, each requiring different conditions, selects the highest quality module based on current conditions to accurately predict road user positions, incorporating trained artificial neural networks and high-definition maps for enhanced precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple prediction modules with different conditions are implemented, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The prediction system is divided into multiple independent prediction modules (first prediction module, second prediction module, third prediction module), each handling specific traffic scenarios with different prediction algorithms. This segmentation allows the system to achieve high prediction accuracy for diverse scenarios while keeping each module's complexity manageable and focused on specific prediction tasks.
Solution Approach 2:
The system dynamically selects which prediction module to use based on real-time traffic conditions and scenario recognition. The test module evaluates current conditions and activates the most appropriate prediction module, making the system adaptable and efficient rather than running all modules continuously, thus balancing accuracy with computational resources.
2Reliability
If the system uses multiple prediction modules with different conditions, then reliability of dangerous situation detection is improved, but the system complexity increases
Solution Approach 1:
The system pre-defines multiple prediction modules with specific conditions and algorithms for different traffic scenarios before operation. The test module contains pre-programmed logic to evaluate current conditions against these predefined scenarios and select the appropriate module. This preliminary preparation ensures reliable detection across various scenarios while avoiding the need for complex real-time decision-making algorithms.
3Loss of time
If sensor range is extended to detect road users farther away, then warning time is improved, but measurement precision deteriorates
Solution Approach 1:
The system addresses the distance-precision trade-off by introducing a scenario-based prediction dimension. Instead of relying solely on sensor detection precision at various distances, the system uses the test module to recognize traffic scenarios and activate appropriate prediction modules that can accurately forecast road user positions even when they are beyond current sensor ranges, effectively extending detection capability through predictive modeling.
Data Source
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AI summary
The invention relates to an alarm system (1) for warning vulnerable road users in a given road section (2) comprising a receiving unit (4) with a communication interface (3) for receiving a plurality of movement data from detected road users relating to the road section (2), wherein a storage unit (10) is provided which has at least three or more prediction modules (5, 6, 7) with descending prediction quality, and wherein the first prediction module (5) is configured to predict at least one position of the detected road users when certain first conditions are met, and wherein the second prediction module (6) is configured to predict at least one position of the detected road users when the first conditions are not met and only certain second conditions are met, and wherein the third prediction module (7) is configured toto predict at least one position of the detected road users if the first conditions as well as the second conditions are not met, and wherein a test module (15) is provided which is designed to check whether the first conditions or the second conditions or the third conditions are met with regard to the road segment (2) and the road user, and in descending order of quality and depending on the conditions met, to select the prediction module (5, 6, 7) with the highest prediction quality, and wherein a processor (16) is provided which is trained to predict at least the future position of the detected road users based on the selected prediction module (5, 6, 7).