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The vehicle's perception sensors radar, lidar and camera, which must work continuously and without restriction, especially with regard to automated/autonomous driving, can lose performance due to unfavourable weather conditions. This paper…
Autonomous driving systems are highly dependent on sensors like cameras, LiDAR, and inertial measurement units (IMU) to perceive the environment and estimate their motion. Among these sensors, perception-based sensors are not protected from…
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit…
Autonomous vehicles rely on a variety of sensors to gather information about their surrounding. The vehicle's behavior is planned based on the environment perception, making its reliability crucial for safety reasons. The active LiDAR…
Shuttle buses have been a popular means to move commuters sharing similar origins and destinations during periods of high travel demand. However, planning and deploying reasonable, customized service bus systems becomes challenging when the…
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…
Autonomous vehicles face significant challenges in navigating adverse weather, particularly rain, due to the visual impairment of camera-based systems. In this study, we leveraged contemporary deep learning techniques to mitigate these…
Several popular computer vision (CV) datasets, specifically employed for Object Detection (OD) in autonomous driving tasks exhibit biases due to a range of factors including weather and lighting conditions. These biases may impair a model's…
Autonomous driving is a complex undertaking. A common approach is to break down the driving task into individual subtasks through modularization. These sub-modules are usually developed and published separately. However, if these…
With the rapid growth of Vehicular Ad-Hoc Networks (VANETs), huge amounts of road condition data are constantly being generated and sent to the cloud for processing. However, this introduces a significant load on the network bandwidth…
Addressing the Integrated Timetabling and Vehicle Scheduling (TTVS) problem is important for improving transit operations. Recently, the emerging modular autonomous vehicles composed of modular autonomous units have made it possible to…
This paper presents a multi-sensor architecture with an adaptive multi-sensor management system suitable for control and navigation of autonomous maritime vessels in hazy and poor-visibility conditions. This architecture resides in the…
Reliable and weather-robust perception systems are essential for safe autonomous driving and typically employ multi-modal sensor configurations to achieve comprehensive environmental awareness. While recent automotive FMCW Radar-based…
Recent advances in automated vehicles have focused on improving perception performance under adverse weather conditions; however, research on physical hardware solutions remains limited, despite their importance for perception critical…
Recently, as many studies of autonomous vehicles have been achieved for levels 4 and 5, there has been also increasing interest in the advancement of perception, decision, and control technologies, which are the three major aspects of…
Vehicle computing represents a fundamental shift in how autonomous vehicles are designed and deployed, transforming them from isolated transportation systems into mobile computing platforms that support both safety-critical, real-time…
Perception robustness under adverse weather remains a critical challenge for autonomous driving, with the core bottleneck being the scarcity of real-world video data in adverse weather. Existing weather generation approaches struggle to…
Autonomous underwater robots typically require higher cost and time for demonstrations compared to other domains due to the complexity of the environment. Due to the limited capacity and payload flexibility, it is challenging to find…
Autonomous driving system progress has been driven by improvements in machine learning models, whose computational demands now exceed what edge devices alone can provide. The cloud offers abundant compute, but the network has long been…
Autonomous driving clouds provide essential services to support autonomous vehicles. Today these services include but not limited to distributed simulation tests for new algorithm deployment, offline deep learning model training, and…