Lane detection based on semantic segmentation can achieve high accuracy, but, in recent years, it does not have a mobile-friendly cost, which is caused by the complex iteration and costly convolutions in convolutional neural networks (CNNs) and state-of-the-art (SOTA) models based on CNNs, such as spatial CNNs (SCNNs). Although the SCNN has shown i...
This paper suggests an adaptive car-following strategy for autonomous connected vehicles (ACVs) that integrates a robust controller with an extended disturbance estimator (EDE) and reinforcement learning (RL) to improve performance in dynamic traffic environments. Traditional car-following methods struggle to handle external disturbances and uncert...
This paper focuses on the spatiotemporal trajectory planning problem faced by autonomous driving with a dynamic on-road situation. To solve the swing problem which is caused by the motions of obstacles, a multi-area sampling method is proposed. The main idea is sampling endpoints in a series of defined areas at a fixed time interval, which will gen...
The motion control of vehicles poses distinct challenges for both vehicle stability and path tracking, especially under critical environmental and driving conditions. Overactuated vehicles can effectively utilize the available tyre–road friction potential by leveraging additional actuators, thus enhancing their stability and controllability even in...
With advancements in autonomous driving, LiDAR has become central to 3D object detection due to its precision and interference resistance. However, challenges such as point cloud sparsity and unstructured data persist. This study introduces MS3D (Multi-Scale Feature Fusion 3D Object Detection Method), a novel approach to 3D object detection that le...
Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are...
Published in Acta Electrotechnica et Informatica
We explore the use of radiance fields (RFs) to reconstruct photorealistic 3D urban scenes, creating digital twins (DTs) for autonomous driving (AD) by leveraging Nerfacto and Splatfacto models integrated with the CARLA simulator. Our research demonstrates that publicly available RFs can be utilized through Nerfstudio library to create photorealisti...
The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and ...
Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily on image-based approaches, which may not be able to fully characterize radar sensor data or efficien...
With the advancement of autonomous driving systems, the need for effective emergency avoidance path planning has become increasingly important. To enhance safety, the predicted paths of surrounding vehicles anticipate risks and incorporate them into avoidance strategies, enabling more efficient and stable driving. Although the artificial potential ...
