Related papers: Toward a Full-Stack Co-Simulation Platform for Tes…
We argue that it is beneficial to tightly couple the widely-used Robot Operating System with Conda, a cross-platform, language-agnostic package manager, and Jupyter, a web-based interactive computational environment affording scientific…
The rapid growth in terms of the availability of transportation data provides great potential for the introduction of emerging data-driven methodologies into transportation-related research and development efforts. However, advanced…
Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simulations come with a series of benefits compared to real-world…
Autonomous racing offers a rigorous setting to stress test perception, planning, and control under high speed and uncertainty. This paper proposes an approach to design and evaluate a software stack for an autonomous race car in CARLA: Car…
We introduce OpenCAMS (Open-Source Connected and Automated Mobility Co-Simulation Platform), an open-source, synchronized, and extensible co-simulation framework that tightly couples three best-in-class simulation tools: (i) SUMO, (ii)…
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…
Ensuring the functional correctness and safety of autonomous vehicles is a major challenge for the automotive industry. However, exhaustive physical test drives are not feasible, as billions of driven kilometers would be required to obtain…
Simulation is a prospective method for generating diverse and realistic traffic scenarios to aid in the development of driving decision-making systems. However, existing simulators often fall short in diverse scenarios or interactive…
With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and…
The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally…
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…
Scenario-based testing is envisioned as a key approach for the safety assurance of autonomous vehicles. In scenario-based testing, relevant (driving) scenarios are the basis of tests. Many recent works focus on specification, variation,…
Simulation-based testing remains the main approach for validating Autonomous Driving Systems. We propose a rigorous test method based on breaking down scenarios into simple ones, taking into account the fact that autopilots make decisions…
We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or highly realistic simulation. Focusing on a roundabout scenario,…
With the advancement of modern robotics, autonomous agents are now capable of hosting sophisticated algorithms, which enables them to make intelligent decisions. But developing and testing such algorithms directly in real-world systems is…
Regulatory approval and safety guarantees for autonomous vehicles facing frequent functional updates and complex software stacks, including artificial intelligence, are a challenging topic. This paper proposes a concept and guideline for…
We introduce a new software toolbox for agent-based simulation. Facilitating rapid prototyping by offering a user-friendly Python API, its core rests on an efficient C++ implementation to support simulation of large-scale multi-agent…
Self-driving technology is expected to revolutionize different sectors and is seen as the natural evolution of road vehicles. In the last years, real-world validation of designed and virtually tested solutions is growing in importance since…
The way to full autonomy of public road vehicles requires the step-by-step replacement of the human driver, with the ultimate goal of replacing the driver completely. Eventually, the driving software has to be able to handle all situations…
Modern autonomous systems require extensive testing to ensure reliability and build trust in ground vehicles. However, testing these systems in the real-world is challenging due to the lack of large and diverse datasets, especially in edge…