Wang Sheng

• PhD Student at Hong Kong University of Science and Technology

Decision-making and planning in autonomous driving critically reflect the safety of the system, making effective planning imperative. Current imitation learning-based planning algorithms often merge historical trajectories with present observations to predict future candidate paths. However, these algorithms typically assess the current and histori...

Trajectory planning involves generating a series of space points to be followed in the near future. However, due to the complex and uncertain nature of the driving environment, it is impractical for autonomous vehicles~(AVs) to exhaustively design planning rules for optimizing future trajectories. To address this issue, we propose a local map repre...

Considerable research efforts have been devoted to the development of motion planning algorithms, which form a cornerstone of the autonomous driving system (ADS). Nonetheless, acquiring an interactive and secure trajectory for the ADS remains challenging due to the complex nature of interaction modeling in planning. Modern planning methods still em...

3D lane detection is essential in autonomous driving as it extracts structural and traffic information from the road in three-dimensional space, aiding self-driving cars in logical, safe, and comfortable path planning and motion control. Given the cost of sensors and the advantages of visual data in color information, 3D lane detection based on mon...

Planning is complicated by the combination of perception and map information, particularly when driving in heavy traffic. Developing an extendable and efficient representation that visualizes sensor noise and provides constraints to real-time planning tasks is desirable. We aim to develop an extendable map representation offering prior to cost in p...

In recent years, the rapid evolution of autonomous vehicles (AVs) has reshaped global transportation systems, leading to an increase in autonomous shuttle applications in people’s daily lives. Leveraging the accomplishments of our earlier endeavor, particularly Hercules [1], an autonomous logistics vehicle for transporting goods, we introduce Snow...

Drivable area segmentation is an essential component of the visual perception system for autonomous driving vehicles. Recent efforts in deep neural networks have significantly improved semantic segmentation performance for autonomous driving. However, most DNN-based methods need a large amount of data to train the models, and collecting large-scale...

Computational fluid dynamics (CFD) predictions based on machine learning methods have become an important area of turbulence and transition research. However, the otherwise efficient and low-cost transition models based on Reynolds-averaged Navier--Stokes (RANS) methods have limited capability for dealing with hypersonic conditions, owing to the st...