The key technology toward the self-driving car
J Zhao, B Liang, Q Chen - International journal of intelligent …, 2018 - emerald.com
Purpose The successful and commercial use of self-driving/driverless/unmanned/automated
car will make human life easier. The paper aims to discuss this issue. Design/methodology …
car will make human life easier. The paper aims to discuss this issue. Design/methodology …
When intelligent transportation systems sensing meets edge computing: Vision and challenges
The widespread use of mobile devices and sensors has motivated data-driven applications
that can leverage the power of big data to benefit many aspects of our daily life, such as …
that can leverage the power of big data to benefit many aspects of our daily life, such as …
Robust vehicular localization and map matching in urban environments through IMU, GNSS, and cellular signals
A framework for ground vehicle localization that uses cellular signals of opportunity (SOPs),
a digital map, an inertial measurement unit (IMU), and a Global Navigation Satellite System …
a digital map, an inertial measurement unit (IMU), and a Global Navigation Satellite System …
Traffic congestion prediction based on GPS trajectory data
S Sun, J Chen, J Sun - International Journal of Distributed …, 2019 - journals.sagepub.com
Since speed sensors are not as widely used as GPS devices, the traffic congestion level is
predicted based on processed GPS trajectory data in this article. Hidden Markov model is …
predicted based on processed GPS trajectory data in this article. Hidden Markov model is …
Map matching for low-sampling-rate GPS trajectories by exploring real-time moving directions
YL Hsueh, HC Chen - Information Sciences, 2018 - Elsevier
Map matching is the process of matching a series of recorded geographic coordinates (eg, a
GPS trajectory) to a road network. Due to GPS positioning errors and the sampling …
GPS trajectory) to a road network. Due to GPS positioning errors and the sampling …
Trembr: Exploring road networks for trajectory representation learning
In this article, we propose a novel representation learning framework, namely TRajectory
EMBedding via Road networks (Trembr), to learn trajectory embeddings (low-dimensional …
EMBedding via Road networks (Trembr), to learn trajectory embeddings (low-dimensional …
Transformer-based map-matching model with limited labeled data using transfer-learning approach
In many spatial trajectory-based applications, it is necessary to map raw trajectory data
points onto road networks in digital maps, which is commonly referred to as a map-matching …
points onto road networks in digital maps, which is commonly referred to as a map-matching …
Fl-amm: Federated learning augmented map matching with heterogeneous cellular moving trajectories
Map matching is a fundamental component for location-based services (LBSs), such as
vehicle mobility analysis, navigation services, traffic scheduling, etc. In this paper, we …
vehicle mobility analysis, navigation services, traffic scheduling, etc. In this paper, we …
Ground vehicle navigation in GNSS-challenged environments using signals of opportunity and a closed-loop map-matching approach
A ground vehicle navigation approach in a global navigation satellite system (GNSS)-
challenged environments is developed, which uses signals of opportunity (SOPs) in a …
challenged environments is developed, which uses signals of opportunity (SOPs) in a …
DMM: Fast map matching for cellular data
Map matching for cellular data is to transform a sequence of cell tower locations to a
trajectory on a road map. It is an essential processing step for many applications, such as …
trajectory on a road map. It is an essential processing step for many applications, such as …