4,821 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Visual robot navigation within large-scale, semi-structured environments
deals with various challenges such as computation intensive path planning
algorithms or insufficient knowledge about traversable spaces. Moreover, many
state-of-the-art navigation approaches only operate locally instead of gaining
a more conceptual understanding of the planning objective. This limits the
complexity of tasks a robot can accomplish and makes it harder to deal with
uncertainties that are present in the context of real-time robotics
applications. In this work, we present Topomap, a framework which simplifies
the navigation task by providing a map to the robot which is tailored for path
planning use. This novel approach transforms a sparse feature-based map from a
visual Simultaneous Localization And Mapping (SLAM) system into a
three-dimensional topological map. This is done in two steps. First, we extract
occupancy information directly from the noisy sparse point cloud. Then, we
create a set of convex free-space clusters, which are the vertices of the
topological map. We show that this representation improves the efficiency of
global planning, and we provide a complete derivation of our algorithm.
Planning experiments on real world datasets demonstrate that we achieve similar
performance as RRT* with significantly lower computation times and storage
requirements. Finally, we test our algorithm on a mobile robotic platform to
prove its advantages.Comment: 8 page
Uses and Challenges of Collecting LiDAR Data from a Growing Autonomous Vehicle Fleet: Implications for Infrastructure Planning and Inspection Practices
Autonomous vehicles (AVs) that utilize LiDAR (Light Detection and Ranging) and other sensing technologies are becoming an inevitable part of transportation industry. Concurrently, transportation agencies are increasingly challenged with the management and tracking of large-scale highway asset inventory. LiDAR has become popular among transportation agencies for highway asset management given its advantage over traditional surveying methods. The affordability of LiDAR technology is increasing day by day. Given this, there will be substantial challenges and opportunities for the utilization of big data resulting from the growth of AVs with LiDAR. A proper understanding of the data size generated from this technology will help agencies in making decisions regarding storage, management, and transmission of the data.
The original raw data generated from the sensor shrinks a lot after filtering and processing following the Cache county Road Manual and storing into ASPRS recommended (.las) file format. In this pilot study, it is found that while considering the road centerline as the vehicle trajectory larger portion of the data fall into the right of way section compared to the actual vehicle trajectory in Cache County, UT. And there is a positive relation between the data size and vehicle speed in terms of the travel lanes section given the nature of the selected highway environment
Localization and Mapping for Self-Driving Vehicles:A Survey
The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains
A Survey on Graph Neural Networks in Intelligent Transportation Systems
Intelligent Transportation System (ITS) is vital in improving traffic
congestion, reducing traffic accidents, optimizing urban planning, etc.
However, due to the complexity of the traffic network, traditional machine
learning and statistical methods are relegated to the background. With the
advent of the artificial intelligence era, many deep learning frameworks have
made remarkable progress in various fields and are now considered effective
methods in many areas. As a deep learning method, Graph Neural Networks (GNNs)
have emerged as a highly competitive method in the ITS field since 2019 due to
their strong ability to model graph-related problems. As a result, more and
more scholars pay attention to the applications of GNNs in transportation
domains, which have shown excellent performance. However, most of the research
in this area is still concentrated on traffic forecasting, while other ITS
domains, such as autonomous vehicles and urban planning, still require more
attention. This paper aims to review the applications of GNNs in six
representative and emerging ITS domains: traffic forecasting, autonomous
vehicles, traffic signal control, transportation safety, demand prediction, and
parking management. We have reviewed extensive graph-related studies from 2018
to 2023, summarized their methods, features, and contributions, and presented
them in informative tables or lists. Finally, we have identified the challenges
of applying GNNs to ITS and suggested potential future directions
Ambient-Aware LiDAR Odometry in Variable Terrains
The flexibility of Simultaneous Localization and Mapping (SLAM) algorithms in
various environments has consistently been a significant challenge. To address
the issue of LiDAR odometry drift in high-noise settings, integrating
clustering methods to filter out unstable features has become an effective
module of SLAM frameworks. However, reducing the amount of point cloud data can
lead to potential loss of information and possible degeneration. As a result,
this research proposes a LiDAR odometry that can dynamically assess the point
cloud's reliability. The algorithm aims to improve adaptability in diverse
settings by selecting important feature points with sensitivity to the level of
environmental degeneration. Firstly, a fast adaptive Euclidean clustering
algorithm based on range image is proposed, which, combined with depth
clustering, extracts the primary structural points of the environment defined
as ambient skeleton points. Then, the environmental degeneration level is
computed through the dense normal features of the skeleton points, and the
point cloud cleaning is dynamically adjusted accordingly. The algorithm is
validated on the KITTI benchmark and real environments, demonstrating higher
accuracy and robustness in different environments
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