Related papers: V2X-Sim: Multi-Agent Collaborative Perception Data…
Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions. However, existing semantic perception datasets…
Due to the lack of enough real multi-agent data and time-consuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for training and validating. However, the perception performance…
Deep learning has been widely used in the perception (e.g., 3D object detection) of intelligent vehicle driving. Due to the beneficial Vehicle-to-Vehicle (V2V) communication, the deep learning based features from other agents can be shared…
Multi-agent collaborative perception as a potential application for vehicle-to-everything communication could significantly improve the perception performance of autonomous vehicles over single-agent perception. However, several challenges…
Large vision-language models (VLMs) have garnered increasing interest in autonomous driving areas, due to their advanced capabilities in complex reasoning tasks essential for highly autonomous vehicle behavior. Despite their potential,…
Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where…
6G technology targets to revolutionize the mobility industry by revamping the role of wireless connections. In this article, we draw out our vision on an intelligent, cooperative, and sustainable mobility environment of the future,…
Modern driver assistance systems as well as autonomous vehicles take their decisions based on local maps of the environment. These maps include, for example, surrounding moving objects perceived by sensors as well as routes and navigation…
Current Vehicle-to-Everything (V2X) systems have significantly enhanced 3D object detection using LiDAR and camera data. However, these methods suffer from performance degradation in adverse weather conditions. The weather-robust 4D radar…
Autonomous driving is a popular research area within the computer vision research community. Since autonomous vehicles are highly safety-critical, ensuring robustness is essential for real-world deployment. While several public multimodal…
As a pivotal technology for autonomous driving, collaborative perception enables vehicular agents to exchange perceptual data through vehicle-to-everything (V2X) communications, thereby enhancing perception accuracy of all collaborators.…
Endowed with automation and connectivity, Connected and Automated Vehicles are meant to be a revolutionary promoter for Cooperative Driving Automation. Nevertheless, CAVs need high-fidelity perception information on their surroundings,…
Recent advancements in V2X communications have greatly increased the flexibility of the physical and medium access control (MAC) layers. This increases the complexity when investigating the system from a network perspective to evaluate the…
Cooperative perception is a promising technique for intelligent and connected vehicles through vehicle-to-everything (V2X) cooperation, provided that accurate pose information and relative pose transforms are available. Nevertheless,…
As autonomous vehicles edge closer to widespread adoption, enhancing road safety through collision avoidance and minimization of collateral damage becomes imperative. Vehicle-to-everything (V2X) technologies, which include…
Infrastructure-to-Vehicle (I2V) and Vehicle-to-Infrastructure (V2I) communication is likely to be a key-enabling technology for automated driving in the future. Using externally placed sensors, the digital infrastructure can support the…
With the development of V2X technology, multiple vehicles cooperative control has been widely studied. However, filed testing is rarely conducted due to financial and safety considerations. To solve this problem, this study proposes a…
This paper proposes V2Sim, an open source Pythonbased simulation platform designed for advanced vehicle-to-grid (V2G) analysis in coupled urban power and transportation networks. By integrating a microscopic urban transportation network…
The recent surge in interest in autonomous driving stems from its rapidly developing capacity to enhance safety, efficiency, and convenience. A pivotal aspect of autonomous driving technology is its perceptual systems, where core algorithms…
Vehicle-to-everything (V2X) perception describes a suite of technologies used to enable vehicles to perceive their surroundings and communicate with various entities, such as other road users, infrastructure, or the network/cloud. With the…