Related papers: Simulation-based Validation for Autonomous Driving…
Autonomous robots deployed in shared human environments, such as agricultural settings, require rigorous safety assurance to meet both functional reliability and regulatory compliance. These systems must operate in dynamic, unstructured…
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…
The simulation-based testing is essential for safely implementing autonomous vehicles (AV) on roads, necessitating simulated traffic environments that dynamically interact with the Vehicle Under Test (VUT). This study introduces a…
Safely interacting with humans is a significant challenge for autonomous driving. The performance of this interaction depends on machine learning-based modules of an autopilot, such as perception, behavior prediction, and planning. These…
Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV) a reality. It requires one to generate safety critical scenarios beyond what can be collected safely in the world, as many scenarios happen rarely…
Thorough testing of safety-critical autonomous systems, such as self-driving cars, autonomous robots, and drones, is essential for detecting potential failures before deployment. One crucial testing stage is model-in-the-loop testing, where…
The investigation of factors contributing at making humans trust Autonomous Vehicles (AVs) will play a fundamental role in the adoption of such technology. The user's ability to form a mental model of the AV, which is crucial to establish…
As the landscape of devices that interact with the electrical grid expands, also the complexity of the scenarios that arise from these interactions increases. Validation methods and tools are typically domain specific and are designed to…
Simulation testing is a fundamental approach for evaluating automated vehicles (AVs). To ensure its reliability, it is crucial to accurately replicate interactions between AVs and background traffic, which necessitates effective…
Safety testing serves as the fundamental pillar for the development of autonomous driving systems (ADSs). To ensure the safety of ADSs, it is paramount to generate a diverse range of safety-critical test scenarios. While existing ADS…
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness. We propose to validate machine learning models for self-driving vehicles not only with given ground truth labels, but also with…
The coordination of multiple autonomous vehicles into convoys or platoons is expected on our highways in the near future. However, before such platoons can be deployed, the new autonomous behaviors of the vehicles in these platoons must be…
Scenario-based testing has become a promising approach to overcome the complexity of real-world traffic for safety assurance of automated vehicles. Within scenario-based testing, a system under test is confronted with a set of predefined…
If autonomous vehicles are to be widely accepted, we need to ensure their safe operation. For this reason, verification and validation (V&V) approaches must be developed that are suitable for this domain. Model checking is a formal…
The safety and reliability of Automated Driving Systems (ADSs) must be validated prior to large-scale deployment. Among existing validation approaches, scenario-based testing has been regarded as a promising method to improve testing…
There have been major developments in Automated Driving (AD) and Driving Assist Systems (ADAS) in recent years. However, their safety assurance, thus methodologies for testing, verification and validation AD/ADAS safety-critical…
The selection of relevant test scenarios for the scenario-based testing and safety validation of automated driving systems (ADSs) remains challenging. An important aspect of the relevance of a scenario is the challenge it poses for an ADS.…
Testing autonomous vehicles in simulation environments is crucial. Sim-ATAV is an open-source framework developed for experimenting with different test generation techniques in simulation environments for research purposes. This document…
Understanding how people view and interact with autonomous vehicles is important to guide future directions of research. One such way of aiding understanding is through simulations of virtual environments involving people and autonomous…
Context: Competitions for self-driving cars facilitated the development and research in the domain of autonomous vehicles towards potential solutions for the future mobility. Objective: Miniature vehicles can bridge the gap between…