相关论文: Surround-View Cameras based Holistic Visual Percep…
Research on coastal regions traditionally involves methods like manual sampling, monitoring buoys, and remote sensing, but these methods face challenges in spatially and temporally diverse regions of interest. Autonomous surface vehicles…
Trajectory prediction is, naturally, a key task for vehicle autonomy. While the number of traffic rules is limited, the combinations and uncertainties associated with each agent's behaviour in real-world scenarios are nearly impossible to…
Surround-view cameras are a primary sensor for automated driving, used for near-field perception. It is one of the most commonly used sensors in commercial vehicles primarily used for parking visualization and automated parking. Four…
Panoptic perception represents a forefront advancement in autonomous driving technology, unifying multiple perception tasks into a singular, cohesive framework to facilitate a thorough understanding of the vehicle's surroundings. This…
While the most visible part of the safety verification process of automated vehicles concerns the planning and control system, it is often overlooked that safety of the latter crucially depends on the fault-tolerance of the preceding…
Automated driving (AD) is promising, but the transition to fully autonomous driving is, among other things, subject to the real, ever-changing open world and the resulting challenges. However, research in the field of AD demonstrates the…
Visually-guided underwater robots are deployed alongside human divers for cooperative exploration, inspection, and monitoring tasks in numerous shallow-water and coastal-water applications. The most essential capability of such companion…
Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards.…
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…
The primary focus of autonomous driving research is to improve driving accuracy. While great progress has been made, state-of-the-art algorithms still fail at times. Such failures may have catastrophic consequences. It therefore is…
Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. However, these tasks are typically independently…
Surround View fisheye cameras are commonly deployed in automated driving for 360\deg{} near-field sensing around the vehicle. This work presents a multi-task visual perception network on unrectified fisheye images to enable the vehicle to…
Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians and other relevant entities. Safety concerns and the need…
With the development of autonomous driving technology, sensor calibration has become a key technology to achieve accurate perception fusion and localization. Accurate calibration of the sensors ensures that each sensor can function properly…
Simultaneous localisation and mapping (SLAM) is the problem of autonomous robots to construct or update a map of an undetermined unstructured environment while simultaneously estimate the pose in it. The current trend towards self-driving…
We study the problem of target stabilization with robust obstacle avoidance in robots and vehicles that have access only to vision-based sensors for the purpose of realtime localization. This problem is particularly challenging due to the…
Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary…
With the advancement in computer vision deep learning, systems now are able to analyze an unprecedented amount of rich visual information from videos to enable applications such as autonomous driving, socially-aware robot assistant and…
Autonomous vehicles can enhance overall performance and implement safety measures in ways that are impossible with conventional automobiles. These functions are executed through vehicle control systems, which have been the subject of…
The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision…