相关论文: How Simulation Helps Autonomous Driving:A Survey o…

As the manufacturing industry shifts from mass production to mass customization, there is a growing emphasis on adopting agile, resilient, and human-centric methodologies in line with the directives of Industry 5.0. Central to this…

Digital twins (DTs) enable powerful predictive analytics, but persistent discrepancies between simulations and real systems--known as the reality gap--undermine their reliability. Coined in robotics, the term now applies to DTs, where…

This paper presents the results of tests of interactions between real humans and simulated vehicles in a virtual scenario. Human activity is inserted into the virtual world via a virtual reality interface for pedestrians. The autonomous…

With the increasing safety validation requirements for the release of a self-driving car, alternative approaches, such as simulation-based testing, are emerging in addition to conventional real-world testing. In order to rely on virtual…

Optical communication is developing rapidly in the directions of hardware resource diversification, transmission system flexibility, and network function virtualization. Its proliferation poses a significant challenge to traditional optical…

Digital twinning of vehicles is an iconic application of digital twins, as the concept of twinning dates back to the twinning of NASA space vehicles. Although digital twins (DTs) in the automotive industry have been recognized for their…

Autonomous vehicles (AVs) have the potential to significantly revolutionize society by providing a secure and efficient mode of transportation. Recent years have witnessed notable advancements in autonomous driving perception and…

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…

LiDAR-based perception in intelligent transportation systems (ITS) relies on deep neural networks trained with large-scale labeled datasets. However, creating such datasets is expensive, time-consuming, and labor-intensive, limiting the…

Training self-driving cars is often challenging since they require a vast amount of labeled data in multiple real-world contexts, which is computationally and memory intensive. Researchers often resort to driving simulators to train the…

In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting…

Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe and controlled environment for evaluating AV performance in varied conditions. However,…

Road inspection is crucial for maintaining road serviceability and ensuring traffic safety, as road defects gradually develop and compromise functionality. Traditional inspection methods, which rely on manual evaluations, are…

Using Deep Reinforcement Learning (DRL) can be a promising approach to handle various tasks in the field of (simulated) autonomous driving. However, recent publications mainly consider learning in unusual driving environments. This paper…

Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the integration of real-world data into simulation environments…

The role of simulation in autonomous driving is becoming increasingly important due to the need for rapid prototyping and extensive testing. The use of physics-based simulation involves multiple benefits and advantages at a reasonable cost…

Owing to the typical long-tail data distribution issues, simulating domain-gap-free synthetic data is crucial in robotics, photogrammetry, and computer vision research. The fundamental challenge pertains to credibly measuring the difference…

Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI…

We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state…

Reinforcement Learning (RL) has been widely explored in Traffic Signal Control (TSC) applications, however, still no such system has been deployed in practice. A key barrier to progress in this area is the reality gap, the discrepancy that…