An Optimization and Validation Method to Detect the Collision Scenarios and Identifying the Safety Specification of Highly Automated Driving Vehicle

다국어 초록 (Multilingual Abstract)

Modeling and simulation techniques are a necessity to solve the problems and aid the automated driving verification and validation process. The scenario-based analysis like hazard analysis and risk assessment is counting as an essential method not only to understand the system behavior in the field of an automated vehicle but also to reduce the development and communication gaps. In terms of functional safety and safety of the intended functionality the number of hazardous scenarios increases that need to be reduced. Scenario reduction is a challenge that yet needs to be solved. Therefore, this paper proposes a probability approach like the Monte Carlo method at the logical scenario level. Additionally, the safety-critical vehicle parameter range has been optimized based on collision detection. Furthermore, the result realized by the Monte Carlo experiment has been used to model the concrete scenarios in CarMaker in a time-efficient manner. The approach of modeling for a specific function like transverse guidance can be utilized to build a full scenario database for the highly automated driving vehicle.

목차 (Table of Contents)

• Abstract
• 1. INTRODUCTION
• 2. SCENARIO-BASED ANALYSIS
• 3. MONTE CARLO EXPERIMENT
• 4. MODELING AND SCENARIO SIMULATION
• 3. CONCLUSION
• REFERENCES