ADAS Risk Scoring Using Scaled Vehicle Scenario Testing
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Solution Overview
Problem
Existing risk assessment systems for vehicles equipped with Advanced Driver Assistance Systems (ADAS) fail to accurately measure the impact of ADAS features on accident probability due to lack of standardized methods, rapid technological advancements, and limited access to technical build data, leading to inconsistent and incomplete risk scoring.
Innovation Solution
An electronic risk scoring system that measures a risk-indexing score by determining various driving scenarios with multi-dimensional test matrices, incorporating ADAS functionalities, driver characteristics, and environmental conditions, using a scientific and technological-based methodology to provide real-time, dynamic, and reproducible risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-size vehicle testing is used to accurately assess ADAS performance and accident probability, then measurement precision and reliability are improved, but testing cost and complexity increase significantly
Solution Approach 1:
The patent uses scale-model vehicles (1:43 scale) as copies of full-size vehicles to perform testing. The scale models replicate the essential characteristics and ADAS functionalities of full-size vehicles while being significantly smaller and easier to handle. This allows accurate risk assessment through proportional scaling of test parameters such as speed, distance, and impact force.
Solution Approach 2:
The patent introduces a specialized testing facility with controlled environments as an intermediary between theoretical ADAS assessment and real-world full-size testing. The facility includes scale-model test tracks, controlled obstacle placements, and standardized test protocols that mediate the testing process, enabling reliable data collection without the complexities of full-size road testing.
2Reliability
If standardized testing methods are implemented for ADAS risk assessment, then measurement consistency and reliability are improved, but adaptability to rapid technological advancements decreases
Solution Approach 1:
The patent implements a dynamic testing framework where test protocols and parameters can be updated to reflect new ADAS technologies. The standardized methodology includes defined categories of ADAS functions (sensing, processing, actuation) that can accommodate technological evolution. Testing parameters such as scenario complexity, speed ranges, and obstacle types are adjustable while maintaining the core standardized structure.
Solution Approach 2:
The patent segments ADAS functionality into distinct testable components (sensing systems, processing units, actuation mechanisms) and evaluates them separately through standardized protocols. This modular approach allows individual components to be tested and validated independently, enabling the standardization framework to adapt to technological advancements in specific areas without requiring complete protocol redesign.
3Loss of information
If comprehensive multi-dimensional test matrices are used to assess all driving scenarios, then measurement completeness is improved, but testing time and resource requirements increase
Solution Approach 1:
The patent applies partial testing by focusing on the most critical and representative driving scenarios rather than attempting to test every possible scenario. The test matrix prioritizes high-risk situations (emergency braking, collision avoidance, pedestrian detection) while using proportional scaling to extrapolate results to broader conditions. This selective approach captures essential risk factors without requiring exhaustive testing of all conceivable driving conditions.
Solution Approach 2:
The patent performs preliminary testing with scale-model vehicles to establish baseline performance data and validate test protocols before conducting full-scale risk assessments. The multi-dimensional test matrices are developed and refined through iterative preliminary tests, allowing the system to identify and focus on the most informative test parameters while eliminating redundant measurements, thereby reducing overall testing time while maintaining completeness.
Data Source
AI summary
A system for accidentology-based measuring of accident risk-indexing score values for a motor vehicle to be tested providing a measured accident probability value for an occurrence of an accident event having a physical impact to the tested motor vehicle. The system includes a driving scenario module, a test setting module, and a scoring module. The driving scenario module is configured for determining various driving scenarios for the motor vehicle by defining a set of measurable scenario characteristics, which include at least one ADAS variable. The test setting module is configured for determining a test setting in form of a multi-dimensional test matrix, which includes testing protocols for each of the measurable scenario characteristics for providing measured values of measurable scenario characteristics of the various driving scenarios. The scoring module is configured for generating the risk-indexing score by receiving the multi-dimensional test result signal and a historical data information signal.