15,223 research outputs found
A City-Scale ITS-G5 Network for Next-Generation Intelligent Transportation Systems: Design Insights and Challenges
As we move towards autonomous vehicles, a reliable Vehicle-to-Everything
(V2X) communication framework becomes of paramount importance. In this paper we
present the development and the performance evaluation of a real-world
vehicular networking testbed. Our testbed, deployed in the heart of the City of
Bristol, UK, is able to exchange sensor data in a V2X manner. We will describe
the testbed architecture and its operational modes. Then, we will provide some
insight pertaining the firmware operating on the network devices. The system
performance has been evaluated under a series of large-scale field trials,
which have proven how our solution represents a low-cost high-quality framework
for V2X communications. Our system managed to achieve high packet delivery
ratios under different scenarios (urban, rural, highway) and for different
locations around the city. We have also identified the instability of the
packet transmission rate while using single-core devices, and we present some
future directions that will address that.Comment: Accepted for publication to AdHoc-Now 201
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
3D LiDAR scanners are playing an increasingly important role in autonomous
driving as they can generate depth information of the environment. However,
creating large 3D LiDAR point cloud datasets with point-level labels requires a
significant amount of manual annotation. This jeopardizes the efficient
development of supervised deep learning algorithms which are often data-hungry.
We present a framework to rapidly create point clouds with accurate point-level
labels from a computer game. The framework supports data collection from both
auto-driving scenes and user-configured scenes. Point clouds from auto-driving
scenes can be used as training data for deep learning algorithms, while point
clouds from user-configured scenes can be used to systematically test the
vulnerability of a neural network, and use the falsifying examples to make the
neural network more robust through retraining. In addition, the scene images
can be captured simultaneously in order for sensor fusion tasks, with a method
proposed to do automatic calibration between the point clouds and captured
scene images. We show a significant improvement in accuracy (+9%) in point
cloud segmentation by augmenting the training dataset with the generated
synthesized data. Our experiments also show by testing and retraining the
network using point clouds from user-configured scenes, the weakness/blind
spots of the neural network can be fixed
Intelligent Adaptive Motion Control for Ground Wheeled Vehicles
In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies
Integrating Generative Artificial Intelligence in Intelligent Vehicle Systems
This paper aims to serve as a comprehensive guide for researchers and
practitioners, offering insights into the current state, potential
applications, and future research directions for generative artificial
intelligence and foundation models within the context of intelligent vehicles.
As the automotive industry progressively integrates AI, generative artificial
intelligence technologies hold the potential to revolutionize user
interactions, delivering more immersive, intuitive, and personalised in-car
experiences. We provide an overview of current applications of generative
artificial intelligence in the automotive domain, emphasizing speech, audio,
vision, and multimodal interactions. We subsequently outline critical future
research areas, including domain adaptability, alignment, multimodal
integration and others, as well as, address the challenges and risks associated
with ethics. By fostering collaboration and addressing these research areas,
generative artificial intelligence can unlock its full potential, transforming
the driving experience and shaping the future of intelligent vehicles.Comment: under revie
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
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