906 research outputs found
Intent prediction of vulnerable road users for trusted autonomous vehicles
This study investigated how future autonomous vehicles could be further trusted by vulnerable road users (such as pedestrians and cyclists) that they would be interacting with in urban traffic environments. It focused on understanding the behaviours of such road users on a deeper level by predicting their future intentions based solely on vehicle-based sensors and AI techniques. The findings showed that personal/body language attributes of vulnerable road users besides their past motion trajectories and physics attributes in the environment led to more accurate predictions about their intended actions
Anomaly detection with machine learning for automotive cyber-physical systems
2022 Spring.Includes bibliographical references.Today's automotive systems are evolving at a rapid pace and there has been a seismic shift in automotive technology in the past few years. Automakers are racing to redefine the automobile as a fully autonomous and connected system. As a result, new technologies such as advanced driver assistance systems (ADAS), vehicle-to-vehicle (V2V), 5G vehicle to infrastructure (V2I), and vehicle to everything (V2X), etc. have emerged in recent years. These advances have resulted in increased responsibilities for the electronic control units (ECUs) in the vehicles, requiring a more sophisticated in-vehicle network to address the growing communication needs of ECUs with each other and external subsystems. This in turn has transformed modern vehicles into a complex distributed cyber-physical system. The ever-growing connectivity to external systems in such vehicles is introducing new challenges, related to the increasing vulnerability of such vehicles to various cyber-attacks. A malicious actor can use various access points in a vehicle, e.g., Bluetooth and USB ports, telematic systems, and OBD-II ports, to gain unauthorized access to the in-vehicle network. These access points are used to gain access to the network from the vehicle's attack surface. After gaining access to the in-vehicle network through an attack surface, a malicious actor can inject or alter messages on the network to try to take control of the vehicle. Traditional security mechanisms such as firewalls only detect simple attacks as they do not have the ability to detect more complex attacks. With the increasing complexity of vehicles, the attack surface increases, paving the way for more complex and novel attacks in the future. Thus, there is a need for an advanced attack detection solution that can actively monitor the in-vehicle network and detect complex cyber-attacks. One of the many approaches to achieve this is by using an intrusion detection system (IDS). Many state-of-the-art IDS employ machine learning algorithms to detect cyber-attacks for its ability to detect both previously observed as well as novel attack patterns. Moreover, the large availability of in-vehicle network data and increasing computational power of the ECUs to handle emerging complex automotive tasks facilitates the use of machine learning models. Therefore, due to its large spectrum of attack coverage and ability to detect complex attack patterns, we adopt and propose two novel machine learning based IDS frameworks (LATTE and TENET) for in-vehicle network anomaly detection. Our proposed LATTE framework uses sequence models, such as LSTMs, in an unsupervised setting to learn the normal system behavior. LATTE leverages the learned information at runtime to detect anomalies by observing for any deviations from the learned normal behavior. Our proposed LATTE framework aims to maximize the anomaly detection accuracy, precision, and recall while minimizing the false-positive rate. The increased complexity of automotive systems has resulted in very long term dependencies between messages which cannot be effectively captured by LSTMs. Hence to overcome this problem, we proposed a novel IDS framework called TENET. TENET employs a novel convolutional neural attention (TCNA) based architecture to effectively learn very-long term dependencies between messages in an in-vehicle network during the training phase and leverage the learned information in combination with a decision tree classifier to detect anomalous messages. Our work aims to efficiently detect a multitude of attacks in the in-vehicle network with low memory and computational overhead on the ECU
Using Machine Learning for Handover Optimization in Vehicular Fog Computing
Smart mobility management would be an important prerequisite for future fog
computing systems. In this research, we propose a learning-based handover
optimization for the Internet of Vehicles that would assist the smooth
transition of device connections and offloaded tasks between fog nodes. To
accomplish this, we make use of machine learning algorithms to learn from
vehicle interactions with fog nodes. Our approach uses a three-layer
feed-forward neural network to predict the correct fog node at a given location
and time with 99.2 % accuracy on a test set. We also implement a dual stacked
recurrent neural network (RNN) with long short-term memory (LSTM) cells capable
of learning the latency, or cost, associated with these service requests. We
create a simulation in JAMScript using a dataset of real-world vehicle
movements to create a dataset to train these networks. We further propose the
use of this predictive system in a smarter request routing mechanism to
minimize the service interruption during handovers between fog nodes and to
anticipate areas of low coverage through a series of experiments and test the
models' performance on a test set
Deep Learning Overloaded Vehicle Identification for Long Span Bridges Based on Structural Health Monitoring Data
Overloaded vehicles bring great harm to transportation infrastructures. BWIM
(bridge weigh-in-motion) method for overloaded vehicle identification is
getting more popular because it can be implemented without interruption to the
traffic. However, its application is still limited because its effectiveness
largely depends on professional knowledge and extra information, and is
susceptible to occurrence of multiple vehicles. In this paper, a deep learning
based overloaded vehicle identification approach (DOVI) is proposed, with the
purpose of overloaded vehicle identification for long-span bridges by the use
of structural health monitoring data. The proposed DOVI model uses temporal
convolutional architectures to extract the spatial and temporal features of the
input sequence data, thus provides an end-to-end overloaded vehicle
identification solution which neither needs the influence line nor needs to
obtain velocity and wheelbase information in advance and can be applied under
the occurrence of multiple vehicles. Model evaluations are conducted on a
simply supported beam and a long-span cable-stayed bridge under random traffic
flow. Results demonstrate that the proposed deep-learning overloaded vehicle
identification approach has better effectiveness and robustness, compared with
other machine learning and deep learning approaches
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
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