Related papers: Infrastructure Node-based Vehicle Localization for…
Localization between a swarm of AUVs can be entirely estimated through the use of range measurements between neighboring AUVs via a class of techniques commonly referred to as sensor network localization. However, the localization quality…
Localization for autonomous vehicles on highways remains under-explored compared to urban roads, and state-of-the-art methods for urban scenes degrade when directly applied to highways. We identify key challenges including environment…
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…
The development of self-driving cars has garnered significant attention from researchers, universities, and industries worldwide. Autonomous vehicles integrate numerous subsystems, including lane tracking, object detection, and vehicle…
Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which…
To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side…
This article presents a novel framework for performing visual inspection around 3D infrastructures, by establishing a team of fully autonomous Micro Aerial Vehicles (MAVs) with robust localization, planning and perception capabilities. The…
With the development of autonomous driving, the improvement of autonomous driving technology for individual vehicles has reached the bottleneck. The advancement of vehicle-road cooperation autonomous driving technology can expand the…
Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global…
This paper proposes a low-level visual navigation algorithm to improve visual localization of a mobile robot. The algorithm, based on artificial potential fields, associates each feature in the current image frame with an attractive or…
Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be…
One of the main paths towards the reduction of traffic accidents is the increase in vehicle safety through driver assistance systems or even systems with a complete level of autonomy. In these types of systems, tasks such as obstacle…
High-accurate localization is crucial for the safety and reliability of autonomous driving, especially for the information fusion of collective perception that aims to further improve road safety by sharing information in a communication…
Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This paper addresses the challenge of road graph construction for…
Despite the continual advances in Advanced Driver Assistance Systems (ADAS) and the development of high-level autonomous vehicles (AV), there is a general consensus that for the short to medium term, there is a requirement for a human…
Autonomous vehicles require accurate and robust localization and mapping algorithms to navigate safely and reliably in urban environments. We present a novel sensor fusion-based pipeline for offline mapping and online localization based on…
The combination of data from multiple sensors, also known as sensor fusion or data fusion, is a key aspect in the design of autonomous robots. In particular, algorithms able to accommodate sensor fusion techniques enable increased accuracy,…
In this paper, we propose an accurate and robust perception module for Autonomous Vehicles (AVs) for drivable space extraction. Perception is crucial in autonomous driving, where many deep learning-based methods, while accurate on benchmark…
Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility…
Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sustainability issues of contemporary transportation systems.…