Accident Analysis & Automated Driving
The system test validation can be divided into two types: validation based on positive logic and validation based on negative logic.
1) Positive logic-based test validation aims to validate the functional safety of the system in all cases by analyzing the system functions and exporting test cases for the test system based on all the use cases of the system under test.
2) Negative logic based test validation is the opposite of positive logic based test validation. This type of method is designed to execute the validation test by using error-prone scenarios of the system under test. These scenarios are normally identified by error analysis methods (e.g.: FTA, FMEA, etc.). If the system under test can operate normally in error-prone scenarios, the functional safety of the system can be demonstrated from the reverse side.
The negative logic-based test validation method is more suitable for validation of complex systems (e.g.: high-level automated driving systems) than positive logic-based test validation methods, because it has some advantages like low number of test cases, target-oriented test and so on. Therefore, it is considered by the industry worldwide to be one of the methods for validation of high-level automated driving systems.
For automated driving systems, error analysis is naturally linked to accident analysis. The original meaning of automated driving systems was to copy the driving behavior of a human driver. Therefore, a scene (accident scene) in which the human driver makes a mistake can also be recognized as an error-prone scene of the automated driving system, and therefore identified as a test case for the automated driving system validation.
Automated Driving Test Scenario Based on Accident Analysis
PilotD has entered into a strategic partnership with the Shanghai United Road Traffic Safety Scientific Research Center. The two companies work together to expand and promote automated driving validation technology based on traffic accident analysis and simulation reproduction.
On the one hand, based on thousands of traffic accident scenario information from 2005 collected by the Shanghai United Road Traffic Safety Scientific Research Center, we filtered useful information, and represented and reproduced the scenarios in the GaiA simulation environment to form a test case set. On the other hand, based on the existing traffic accident scenarios, we changed and traversed the essential parameters in the scenario construction, to expand the test case set, and finally reach a high test coverage and complete the test case set.
In this way, our company will help speed up the rising of the validation level and early marketization of automated driving systems.