相关论文: Open-Source Autonomous Driving Software Platforms:…
With the great achievement of artificial intelligence, vehicle technologies have advanced significantly from human centric driving towards fully automated driving. An intelligent vehicle should be able to understand the driver's perception…
Autonomous vehicle (AV) stacks have traditionally relied on decomposed approaches, with separate modules handling perception, prediction, and planning. However, this design introduces information loss during inter-module communication,…
In recent years, the number and importance of autonomous racing leagues, and consequently the number of studies on them, has been growing. The seamless integration between different series has gained attention due to the scene's diversity.…
While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that,…
The engineering community currently encounters significant challenges in the development of intelligent transportation algorithms that can be transferred from simulation to reality with minimal effort. This can be achieved by robustifying…
This paper introduces RoboCar, an open-source research platform for autonomous driving developed at the University of Luxembourg. RoboCar provides a modular, cost-effective framework for the development of experimental Autonomous Driving…
Autonomous driving vehicles provide a vast potential for realizing use cases in the on-road and off-road domains. Consequently, remarkable solutions exist to autonomous systems' environmental perception and control. Nevertheless, proof of…
In the past two decades, autonomous driving has been catalyzed into reality by the growing capabilities of machine learning. This paradigm shift possesses significant potential to transform the future of mobility and reshape our society as…
The concept of autonomy is key to the IoT vision promising increasing integration of smart services and systems minimizing human intervention. This vision challenges our capability to build complex open trustworthy autonomous systems. We…
The rapid adoption of micromobility solutions, particularly two-wheeled vehicles like e-scooters and e-bikes, has created an urgent need for reliable autonomous riding (AR) technologies. While autonomous driving (AD) systems have matured…
Safety analysis is used to identify hazards and build knowledge during the design phase of safety-relevant functions. This is especially true for complex AI-enabled and software intensive systems such as Autonomous Drive (AD).…
Recently, an increasingly growing number of companies is focusing on achieving self-driving systems towards SAE level 3 and higher. Such systems will have much more complex capabilities than today's advanced driver assistance systems (ADAS)…
Reliable testing of autonomous driving systems requires simulation environments that combine large-scale traffic modeling with realistic 3D perception and terrain. Existing tools rarely capture real-world elevation, limiting their…
With increasing complexity and heterogeneity of computing devices, it has become crucial for system to be autonomous, adaptive to dynamic environment, robust, flexible, and having so called self-*properties. These autonomous systems are…
Autonomous driving systems (ADS) are increasingly deployed in real traffic, yet testing remains fundamentally challenging due to open environments, complex scenarios, and the lack of established processes and metrics. Despite extensive…
The technological advancements of recent years have steadily increased the complexity of vehicle-internal software systems, and the ongoing development towards autonomous driving will further aggravate this situation. This is leading to a…
The state-of-the-art driving automation system demands extreme computational resources to meet rigorous accuracy and latency requirements. Though emerging driving automation computing platforms are based on ASIC to provide better…
Robotic middleware serves as the foundational infrastructure, enabling complex robotic systems to operate in a coordinated and modular manner. In data-intensive robotic applications, especially in industrial scenarios, communication…
Teleoperation allows a human operator to remotely interact with and control a mobile robot in a dangerous or inaccessible area. Besides well-known applications such as space exploration or search and rescue operations, the application of…
The spread of autonomous systems into safety-critical areas has increased the demand for their formal verification, not only due to stronger certification requirements but also to public uncertainty over these new technologies. However, the…