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Sheng Wang - IEEE Xplore Author Profile

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Motion planning is a complicated task that requires the combination of perception, map information integration and prediction, particularly when driving in heavy traffic. Developing an extensible and efficient representation that visualizes sensor noise and provides basis to real-time planning tasks is desirable. We aim to develop an interpretable map representation, which offers prior of driving ...Show More
Evaluating and training autonomous driving systems require diverse and scalable corner cases. However, most existing scene generation methods lack controllability, accuracy, and versatility, resulting in unsatisfactory generation results. Inspired by DragGAN in image generation, we propose DragTraffic, a generalized, interactive, and controllable traffic scene generation framework based on conditi...Show More
Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structure, we propose DHP-Mapping, a dense mappi...Show More
This paper presents a generic trajectory planning method for wheeled robots with fixed steering axes while the steering angle of each wheel is constrained. In the existing literatures, All-Wheel-Steering (AWS) robots, incorporating modes such as rotation-free translation maneuvers, in-situ rotational maneuvers, and proportional steering, exhibit inefficient performance due to time-consuming mode s...Show More
Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose a novel trajectory prediction framework called Partial Observations Prediction (POP) for congested urban road scenarios. The framework consists of two key stages: self-supervised learning (SSL) and feature distillation. ...Show More
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 Lion, an autonomous shuttle vehicle specifically d...Show More
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 employ a uniform treatment of prediction outcomes an...Show More
Vehicle trajectory prediction is essential to autonomous driving tasks. Accurate trajectory prediction of other traffic agents can significantly improve the ability of scene understanding and eventually improve the driving safety level of autonomous vehicles. Popular trajectory prediction methods leverage traffic rules by considering maps or lane graphs as a part of the input. However, this implic...Show More
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 representation method called Velocity Field. This appro...Show More
With the development of autonomous driving, more autonomous vehicles are equipped with multiple sensors to perform better. For multi-sensor systems, accurate extrinsic calibration is a prerequisite for perception and localization systems. Extrinsic calibration aims to transform two or more sensors' coordinates into a unified spatial coordinate system. Sensors usually need to be calibrated after in...Show More
Our goal is to train a neural planner that can capture diverse driving behaviors in complex urban scenarios. We observe that even state-of-the-art neural planners are struggling to perform common maneuvers such as lane change, which is rather natural for human drivers. We propose to explore the multi-modalities in the planning problem and force the neural planner to explicitly consider different p...Show More