A Survey on Deep Learning-Based Vehicular Communication Applications

Lin, Chia Hung; Lin, Yu; Wu, Yen‐Jung; Chung, Wei Ho; Lee, Ta Sung

doi:10.1007/s11265-020-01587-2

Cited by 18 publications

(9 citation statements)

References 49 publications

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“…33,34 Recent developments of nn4mc include support for Gated Recurrent Units (GRU) layers and other functionalities. Other academic-and industry-led efforts towards deployed neural network intelligence include MCUNet, 8 which is a framework that jointly designs and trains quantized neural network architectures for increased accuracy. Other efforts towards machine learning on embedded devices include Tensorflow Lite, 7,35 CMix-NN , 36 MicroTVM 37 and tinyML.…”

Section: Related Workmentioning

confidence: 99%

Toward smart composites: Small-scale, untethered prediction and control for soft sensor/actuator systems

Manzano

Sundaram

et al. 2022

Journal of Composite Materials

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We present formulation and open-source tools to achieve in-material model predictive control of sensor/actuator systems using learned forward kinematics and on-device computation. Microcontroller units that compute the prediction and control task while colocated with the sensors and actuators enable in-material untethered behaviors. In this approach, small parameter size neural network models learn forward kinematics offline. Our open-source compiler, nn4mc, generates code to offload these predictions onto MCUs. A Newton-Raphson solver then computes the control input in real time. We first benchmark this nonlinear control approach against a PID controller on a mass-spring-damper simulation. We then study experimental results on two experimental rigs with different sensing, actuation and computational hardware: a tendon-based platform with embedded LightLace sensors and a HASEL-based platform with magnetic sensors. Experimental results indicate effective high-bandwidth tracking of reference paths (≥120 Hz) with a small memory footprint (≤6.4% of flash memory). The measured path following error does not exceed 2mm in the tendon-based platform. The simulated path following error does not exceed 1mm in the HASEL-based platform. The mean power consumption of this approach in an ARM Cortex-M4f device is 45.4 mW. This control approach is also compatible with Tensorflow Lite models and equivalent on-device code. In-material intelligence enables a new class of composites that infuse autonomy into structures and systems with refined artificial proprioception.

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Section: Related Workmentioning

confidence: 99%

Toward smart composites: Small-scale, untethered prediction and control for soft sensor/actuator systems

Manzano

Sundaram

et al. 2022

Journal of Composite Materials

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“…[24]. Among other ML techniques, Deep Reinforcement Learning (DRL) is a potential solution for many complex vehicular scenarios, which allows exploiting Deep Neural Networks (DNN) for analyzing VNs data without requiring any prior knowledge of the VN environment, which is hard to capture [54], e.g., correct state transmission matrix over VN states for Markov Decision Processes (MDP) based solutions. ML solutions outperform the heuristic and one-shot-based optimization techniques with better long-term performance.…”

Section: Machine Learningmentioning

confidence: 99%

Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges

Shinde

Tarchi

2021

Smart Cities

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Modern cities require a tighter integration with Information and Communication Technologies (ICT) for bringing new services to the citizens. The Smart City is the revolutionary paradigm aiming at integrating the ICT with the citizen life; among several urban services, transports are one of the most important in modern cities, introducing several challenges to the Smart City paradigm. In order to satisfy the stringent requirements of new vehicular applications and services, Edge Computing (EC) is one of the most promising technologies when integrated into the Vehicular Networks (VNs). EC-enabled VNs can facilitate new latency-critical and data-intensive applications and services. However, ground-based EC platforms (i.e., Road Side Units—RSUs, 5G Base Stations—5G BS) can only serve a reduced number of Vehicular Users (VUs), due to short coverage ranges and resource shortage. In the recent past, several new aerial platforms with integrated EC facilities have been deployed for achieving global connectivity. Such air-based EC platforms can complement the ground-based EC facilities for creating a futuristic VN able to deploy several new applications and services. The goal of this work is to explore the possibility of creating a novel joint air-ground EC platform within a VN architecture for helping VUs with new intelligent applications and services. By exploiting most modern technologies, with particular attention towards network softwarization, vehicular edge computing, and machine learning, we propose here three possible layered air-ground EC-enabled VN scenarios. For each of the discussed scenarios, a list of the possible challenges is considered, as well possible solutions allowing to overcome all or some of the considered challenges. A proper comparison is also done, through the use of tables, where all the proposed scenarios, and the proposed solutions, are discussed.

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“…For each operating band, we generate 50000, 10000, and 10000 samples and corresponding labels for training, validation, and testing sets, respectively. Note that all the inputs and labels are normalized as [1,0] to prevent computational issues of DL-based algorithms. All the reported results are the average values over the testing set.…”

Section: ) Transportation Modulementioning

confidence: 99%

“…With the promising connectivity provided by nextgeneration communication systems, such as 5G and 6G, several novel applications are developing in full flourish. Among those applications, intelligent transportation system (ITS) and autonomous vehicles are anticipated to bring new experiences with enhanced efficiency and safety to road users in the near future [1]. In macroscopic scale, with the support of ITS algorithms, the data collected from road users can be used to perform traffic flow prediction, optimal path planning, traffic light control, and so on, leading to the improved transportation efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to traditional optimization-based algorithms, which often involves complex matrix operations and iterations, DL models can be used to construct efficient algorithm with URLLC and adaptability since exhausted computation tasks can be finished in offline training phase. Hence, DL-based algorithms are widely adopted in various vehicular communications applications [1]. However, to train DL-based algorithms, abundant amount of data with high quality should be provided.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A C-V2X Platform Using Transportation Data and Spectrum-Aware Sidelink Access

Chen

Lin

Chien-Yuan

et al. 2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments.

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A Survey on Deep Learning-Based Vehicular Communication Applications

Cited by 18 publications

References 49 publications

Toward smart composites: Small-scale, untethered prediction and control for soft sensor/actuator systems

Toward smart composites: Small-scale, untethered prediction and control for soft sensor/actuator systems

Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges

A C-V2X Platform Using Transportation Data and Spectrum-Aware Sidelink Access

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