25 research outputs found
An Efficient V2X Based Vehicle Localization Using Single RSU and Single Receiver
High accuracy vehicle localization information is critical for intelligent transportation systems and future autonomous vehicles. It is challenging to achieve the required centimeter-level localization accuracy, especially in urban or global navigation satellite system denied environments. Here we propose a vehicle-to-infrastructure (V2I)-based vehicle localization algorithm. First, it is low-cost and hardware requirements are simplified, the minimum requirement is a single roadside unit and single on-board receiver. Second, it is computationally efficient, the available V2I information is formulated as an over-determined system. Then, the vehicle position is estimated in a closed-form manner via the widely used weighted linear least squares (WLLS) method and meter level accuracy is achievable. Furthermore, the numerical performance of WLLS is consistent with the theoretical results in larger signal-to-noise ratio region
Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles
Self-driving vehicles have their own intelligence to drive on open roads.
However, vehicle managers, e.g., government or industrial companies, still need
a way to tell these self-driving vehicles what behaviors are encouraged or
forbidden. Unlike human drivers, current self-driving vehicles cannot
understand the traffic laws, thus rely on the programmers manually writing the
corresponding principles into the driving systems. It would be less efficient
and hard to adapt some temporary traffic laws, especially when the vehicles use
data-driven decision-making algorithms. Besides, current self-driving vehicle
systems rarely take traffic law modification into consideration. This work aims
to design a road traffic law adaptive decision-making method. The
decision-making algorithm is designed based on reinforcement learning, in which
the traffic rules are usually implicitly coded in deep neural networks. The
main idea is to supply the adaptability to traffic laws of self-driving
vehicles by a law-adaptive backup policy. In this work, the natural
language-based traffic laws are first translated into a logical expression by
the Linear Temporal Logic method. Then, the system will try to monitor in
advance whether the self-driving vehicle may break the traffic laws by
designing a long-term RL action space. Finally, a sample-based planning method
will re-plan the trajectory when the vehicle may break the traffic rules. The
method is validated in a Beijing Winter Olympic Lane scenario and an overtaking
case, built in CARLA simulator. The results show that by adopting this method,
the self-driving vehicles can comply with new issued or updated traffic laws
effectively. This method helps self-driving vehicles governed by digital
traffic laws, which is necessary for the wide adoption of autonomous driving
A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation
Monocular Re-Localization (MRL) is a critical component in autonomous
applications, estimating 6 degree-of-freedom ego poses w.r.t. the scene map
based on monocular images. In recent decades, significant progress has been
made in the development of MRL techniques. Numerous algorithms have
accomplished extraordinary success in terms of localization accuracy and
robustness. In MRL, scene maps are represented in various forms, and they
determine how MRL methods work and how MRL methods perform. However, to the
best of our knowledge, existing surveys do not provide systematic reviews about
the relationship between MRL solutions and their used scene map representation.
This survey fills the gap by comprehensively reviewing MRL methods from such a
perspective, promoting further research. 1) We commence by delving into the
problem definition of MRL, exploring current challenges, and comparing ours
with existing surveys. 2) Many well-known MRL methods are categorized and
reviewed into five classes according to the representation forms of utilized
map, i.e., geo-tagged frames, visual landmarks, point clouds, vectorized
semantic map, and neural network-based map. 3) To quantitatively and fairly
compare MRL methods with various map, we introduce some public datasets and
provide the performances of some state-of-the-art MRL methods. The strengths
and weakness of MRL methods with different map are analyzed. 4) We finally
introduce some topics of interest in this field and give personal opinions.
This survey can serve as a valuable referenced materials for MRL, and a
continuously updated summary of this survey is publicly available to the
community at: https://github.com/jinyummiao/map-in-mono-reloc.Comment: 33 pages, 10 tables, 16 figures, under revie
A survey on 5G massive MIMO Localization
Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field
MMW Radar-Based Technologies in Autonomous Driving: A Review
With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies