4 research outputs found
Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles
Advancements in LiDAR technology have led to more cost-effective production
while simultaneously improving precision and resolution. As a result, LiDAR has
become integral to vehicle localization, achieving centimeter-level accuracy
through techniques like Normal Distributions Transform (NDT) and other advanced
3D registration algorithms. Nonetheless, these approaches are reliant on
high-definition 3D point cloud maps, the creation of which involves significant
expenditure. When such maps are unavailable or lack sufficient features for 3D
registration algorithms, localization accuracy diminishes, posing a risk to
road safety. To address this, we proposed to use LiDAR-equipped roadside unit
and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the
connected autonomous vehicle's position and help the vehicle when its
self-localization is not accurate enough. Our simulation results indicate that
this method outperforms traditional NDT scan matching-based approaches in terms
of localization accuracy.Comment: Accepted by 2023 International Conference on Intelligent Computing
and its Emerging Application
Multi-Sensor Data Fusion Approach for Kinematic Quantities
A theoretical framework to implement multi-sensor data fusion methods for kinematic quantities is proposed. All methods defined through the framework allow the combination of signals obtained from position, velocity and acceleration sensors addressing the same target, and improvement in the observation of the kinematics of the target. Differently from several alternative methods, the considered ones need no dynamic and/or error models to operate and can be implemented with low computational burden. In fact, they gain measurements by summing filtered versions of the heterogeneous kinematic quantities. In particular, in the case of position measurement, the use of filters with finite impulse responses, all characterized by finite gain throughout the bandwidth, in place of straightforward time-integrative operators, prevents the drift that is typically produced by the offset and low-frequency noise affecting velocity and acceleration data. A simulated scenario shows
that the adopted method keeps the error in a position measurement, obtained indirectly from an accelerometer affected by an offset equal to 1 ppm on the full scale, within a few ppm of the full-scale position. If the digital output of the accelerometer undergoes a second-order time integration, instead, the measurement error would theoretically rise up to 1 n(n + 1) ppm in the full scale at the n-th 2 discrete time instant. The class of methods offered by the proposed framework is therefore interesting in those applications in which the direct position measurements are characterized by poor accuracy and one has also to look at the velocity and acceleration data to improve the tracking of a target
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid
development of communication technologies, sensor technologies, and the
Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of
the vehicle networks, it is rather challenging to make timely and accurate
decisions of vehicle behaviors. Moreover, in the presence of mobile wireless
communications, the privacy and security of vehicle information are at constant
risk. In this context, a new paradigm is urgently needed for various
applications in dynamic vehicle environments. As a distributed machine learning
technology, federated learning (FL) has received extensive attention due to its
outstanding privacy protection properties and easy scalability. We conduct a
comprehensive survey of the latest developments in FL for ITS. Specifically, we
initially research the prevalent challenges in ITS and elucidate the
motivations for applying FL from various perspectives. Subsequently, we review
existing deployments of FL in ITS across various scenarios, and discuss
specific potential issues in object recognition, traffic management, and
service providing scenarios. Furthermore, we conduct a further analysis of the
new challenges introduced by FL deployment and the inherent limitations that FL
alone cannot fully address, including uneven data distribution, limited storage
and computing power, and potential privacy and security concerns. We then
examine the existing collaborative technologies that can help mitigate these
challenges. Lastly, we discuss the open challenges that remain to be addressed
in applying FL in ITS and propose several future research directions
Cooperative Vehicle Perception and Localization Using Infrastructure-based Sensor Nodes
Reliable and accurate Perception and Localization (PL) are necessary for safe intelligent transportation
systems. The current vehicle-based PL techniques in autonomous vehicles are vulnerable to occlusion
and cluttering, especially in busy urban driving causing safety concerns. In order to avoid such safety
issues, researchers study infrastructure-based PL techniques to augment vehicle sensory systems.
Infrastructure-based PL methods rely on sensor nodes that each could include camera(s), Lidar(s),
radar(s), and computation and communication units for processing and transmitting the data. Vehicle
to Infrastructure (V2I) communication is used to access the sensor node processed data to be fused with
the onboard sensor data.
In infrastructure-based PL, signal-based techniques- in which sensors like Lidar are used- can provide
accurate positioning information while vision-based techniques can be used for classification.
Therefore, in order to take advantage of both approaches, cameras are cooperatively used with Lidar in
the infrastructure sensor node (ISN) in this thesis. ISNs have a wider field of view (FOV) and are less
likely to suffer from occlusion. Besides, they can provide more accurate measurements since they are
fixed at a known location. As such, the fusion of both onboard and ISN data has the potential to improve
the overall PL accuracy and reliability.
This thesis presents a framework for cooperative PL in autonomous vehicles (AVs) by fusing ISN
data with onboard sensor data. The ISN includes cameras and Lidar sensors, and the proposed camera Lidar fusion method combines the sensor node information with vehicle motion models and kinematic
constraints to improve the performance of PL. One of the main goals of this thesis is to develop a wind induced motion compensation module to address the problem of time-varying extrinsic parameters of
the ISNs. The proposed module compensates for the effect of the motion of ISN posts due to wind or
other external disturbances. To address this issue, an unknown input observer is developed that uses
the motion model of the light post as well as the sensor data.
The outputs of the ISN, the positions of all objects in the FOV, are then broadcast so that autonomous
vehicles can access the information via V2I connectivity to fuse with their onboard sensory data through
the proposed cooperative PL framework. In the developed framework, a KCF is implemented as a
distributed fusion method to fuse ISN data with onboard data. The introduced cooperative PL
incorporates the range-dependent accuracy of the ISN measurements into fusion to improve the overall
PL accuracy and reliability in different scenarios. The results show that using ISN data in addition to onboard sensor data improves the performance and reliability of PL in different scenarios, specifically
in occlusion cases