Synthesizing Follow-Up Drive Data for Enhanced Road Safety in Intelligent Driving Function Systems

This study underscores the vital importance of intelligent driving functions in enhancing road safety and driving comfort. Central to our research is the challenge of obtaining sufficient test data for evaluating these functions, especially in high-risk, safety-critical driving scenarios. Such scenarios often suffer from a dearth of available data, primarily due to their inherent complexity and the risks involved. Addressing this gap, our research introduces a novel methodology designed to create a wide array of diverse and realistic safety-critical driving scenarios. This approach significantly broadens the testing spectrum for driver assistance systems and autonomous vehicle functions. We particularly focus on the follow-up drive scenario due to its high relevance in practical applications. Here, vehicle movements are intricately modeled using kinematic equations, incorporating factors like driver reaction times. We vary parameters to generate a spectrum of plausible driving scenarios. The utilization of the Difference Space Stopping (DSS) metric is a pivotal element in our research. This metric plays a crucial role in the safety evaluation of follow-up drives, facilitating a more thorough and comprehensive validation process. By doing so, our methodology enhances the reliability and safety assessment of driver assistance and autonomous driving systems, specifically tailored for the most challenging and safety-critical scenarios.

Exploring the Boundaries: Thorough Software Testing for Safety-Critical Driving Scenarios Based on Kinematics in the Context of Autonomous Driving

This scientific publication focuses on the efficient application of boundary value analysis in the testing of corner cases for kinematic-based safety-critical driving scenarios within the domain of autonomous driving. Corner cases, which represent infrequent and crucial situations, present notable obstacles to the reliability and safety of autonomous driving systems. This paper emphasizes the significance of employing boundary value analysis, a systematic technique for identifying critical boundaries and values, to achieve comprehensive testing coverage. By identifying and testing extreme and boundary conditions, such as minimum distances, this publication aims to improve the performance and robustness of autonomous driving systems in safety-critical scenarios. The insights and methodologies presented in this paper can serve as a guide for researchers, developers, and regulators in effectively addressing the challenges posed by corner cases and ensuring the reliability and safety of autonomous driving systems under real-world driving conditions.

Assessing the Criticality of Longitudinal Driving Scenarios using Time Series Data

Unfortunately, many people die in car accidents. To reduce these accidents, cars are equipped with driving safety systems. With autonomous vehicles, the driver's behavior becomes irrelevant as the car drives autonomously. All autonomous driving algorithms must undergo extensive testing and validation, especially for safety-critical scenarios. Therefore, the detection of safety-critical driving scenarios is essential for autonomous vehicles. This publication describes safety indicator metrics based on time series covering longitudinal driving data to detect safety-critical driving scenarios.