Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Gong, Jianwei"'
Publikováno v:
Journal of Field Robotics,2024,1-22
Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, ro
Externí odkaz:
http://arxiv.org/abs/2404.17820
Multi-vehicle coordinated motion planning has always been challenged to safely and efficiently resolve conflicts under non-holonomic dynamic constraints. Constructing spatial-temporal corridors for multi-vehicle can decouple the high-dimensional conf
Externí odkaz:
http://arxiv.org/abs/2304.00843
Developing autonomous vehicles (AVs) helps improve the road safety and traffic efficiency of intelligent transportation systems (ITS). Accurately predicting the trajectories of traffic participants is essential to the decision-making and motion plann
Externí odkaz:
http://arxiv.org/abs/2212.11167
Trajectory prediction is a fundamental problem and challenge for autonomous vehicles. Early works mainly focused on designing complicated architectures for deep-learning-based prediction models in normal-illumination environments, which fail in deali
Externí odkaz:
http://arxiv.org/abs/2211.10226
In urban environments, the complex and uncertain intersection scenarios are challenging for autonomous driving. To ensure safety, it is crucial to develop an adaptive decision making system that can handle the interaction with other vehicles. Manuall
Externí odkaz:
http://arxiv.org/abs/2207.11724
Precisely modeling interactions and accurately predicting trajectories of surrounding vehicles are essential to the decision-making and path-planning of intelligent vehicles. This paper proposes a novel framework based on ensemble learning to improve
Externí odkaz:
http://arxiv.org/abs/2202.10617
This paper presents a driver-specific risk recognition framework for autonomous vehicles that can extract inter-vehicle interactions. This extraction is carried out for urban driving scenarios in a driver-cognitive manner to improve the recognition a
Externí odkaz:
http://arxiv.org/abs/2111.06342
Prediction of Pedestrian Spatiotemporal Risk Levels for Intelligent Vehicles: A Data-driven Approach
In recent years, road safety has attracted significant attention from researchers and practitioners in the intelligent transport systems domain. As one of the most common and vulnerable groups of road users, pedestrians cause great concerns due to th
Externí odkaz:
http://arxiv.org/abs/2111.03822
Autor:
Zhao, Yuehan, Peng, Yuan, Pan, Yao, Lv, Yichen, Zhou, Hongyu, Wu, Jiahao, Gong, Jianwei, Wang, Xin
Publikováno v:
In Brain Research 15 December 2024 1845
Point cloud has been widely used in the field of autonomous driving since it can provide a more comprehensive three-dimensional representation of the environment than 2D images. Point-wise prediction based on point cloud sequence (PCS) is an essentia
Externí odkaz:
http://arxiv.org/abs/2109.07342