scholarly journals Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

2021 ◽  
Vol 2 ◽  
Author(s):  
Ovidiu Vermesan ◽  
Reiner John ◽  
Patrick Pype ◽  
Gerardo Daalderop ◽  
Kai Kriegel ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Gap Analysis ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Edge Computing ◽  
The Internet ◽  
High Definition ◽  
Data Infrastructure ◽  
Traffic Lights ◽  
Digital Infrastructure

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

Blockchain-Autonomous Driving Systems

2021 ◽  
pp. 87-114
Author(s):  
P. Lalitha Surya Kumari
Keyword(s):  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Privacy And Security ◽  
Trust Network ◽  
Vehicle Communication ◽  
Blockchain Technology ◽  
Vehicle Data

Blockchain is the upcoming new information technology that could have quite a lot of significant future applications. In this chapter, the communication network for the reliable environment of intelligent vehicle systems is considered along with how the blockchain technology generates trust network among intelligent vehicles. It also discusses different factors that are effecting or motivating automotive industry, data-driven intelligent transportation system (D2ITS), structure of VANET, framework of intelligent vehicle data sharing based on blockchain used for intelligent vehicle communication and decentralized autonomous vehicles (DAV) network. It also talks about the different ways the autonomous vehicles use blockchain. Block-VN distributed architecture is discussed in detail. The different challenges of research and privacy and security of vehicular network are discussed.

scholarly journals Road-Aware Trajectory Prediction for Autonomous Driving on Highways

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4703
Author(s):  
Yookhyun Yoon ◽  
Taeyeon Kim ◽  
Ho Lee ◽  
Jahnghyon Park
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Prediction Method ◽  
Autonomous Driving ◽  
Structural Constraints ◽  
High Definition ◽  
Trajectory Prediction ◽  
The Road ◽  
Road Geometry ◽  
Efficient Learning

For driving safely and comfortably, the long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory prediction, deep-learning-based approaches have been proposed previously. An on-road vehicle must obey road geometry, i.e., it should run within the constraint of the road shape. Herein, we present a novel road-aware trajectory prediction method which leverages the use of high-definition maps with a deep learning network. We developed a data-efficient learning framework for the trajectory prediction network in the curvilinear coordinate system of the road and a lane assignment for the surrounding vehicles. Then, we proposed a novel output-constrained sequence-to-sequence trajectory prediction network to incorporate the structural constraints of the road. Our method uses these structural constraints as prior knowledge for the prediction network. It is not only used as an input to the trajectory prediction network, but is also included in the constrained loss function of the maneuver recognition network. Accordingly, the proposed method can predict a feasible and realistic intention of the driver and trajectory. Our method has been evaluated using a real traffic dataset, and the results thus obtained show that it is data-efficient and can predict reasonable trajectories at merging sections.

scholarly journals Research Progress and Prospects of Vehicle Driving Behavior Prediction

2021 ◽  
Vol 12 (2) ◽  
pp. 88
Author(s):  
Xinghua Hu ◽  
Mintanyu Zheng
Keyword(s):  
Autonomous Driving ◽  
Driving Behavior ◽  
Transportation Systems ◽  
Intelligent Vehicles ◽  
Research Progress ◽  
Future Research ◽  
The Internet ◽  
Developmental Trend ◽  
Behavior Prediction ◽  
Mixed Traffic

Autonomous driving technology is vital for intelligent transportation systems. Vehicle driving behavior prediction is the foundation and core of autonomous driving. A detailed review of the existing research on vehicle driving behavior prediction can improve the understanding of the current progress of research on autonomous driving and provide references for follow-up researchers. This paper primarily reviews and analyzes the control models of autonomous driving, prejudgment methods, on-road and intersection traffic decision-making, and shortcomings of the research about the prediction of individual intelligent vehicle driving behavior, the prediction on movements of vehicles connected via the Internet, and prediction of driving behavior in a mixed traffic environment. The deficiencies in the research on vehicle driving behavior prediction are as follows: (1) there are numerous limitations in the intelligent application scenarios of individual intelligent vehicles; (2) although the Internet of Vehicles is a significant developmental trend, the training and test datasets are not rich enough; and (3) as the research of mixed traffic flow is still in the initial stages, the comfort brought by autonomous driving in hybrid driving environments is not being considered. In addition to the above analyses and comments, the future research prospects of vehicle driving behavior prediction are discussed as well.

scholarly journals Automatic Building and Labeling of HD Maps with Deep Learning

2020 ◽  
Vol 34 (08) ◽  
pp. 13255-13260
Author(s):  
Mahdi Elhousni ◽  
Yecheng Lyu ◽  
Ziming Zhang ◽  
Xinming Huang
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
New Technology ◽  
Autonomous Driving ◽  
Sensor Data ◽  
High Definition ◽  
Test Vehicle ◽  
Novel Method ◽  
Using Data

In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and labeling high-definition (HD) maps accurately and efficiently. Today, the process of creating HD maps requires a lot of human input, which takes time and is prone to errors. In this paper, we propose a novel method capable of generating labelled HD maps from raw sensor data. We implemented and tested our methods on several urban scenarios using data collected from our test vehicle. The results show that the proposed deep learning based method can produce highly accurate HD maps. This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicles.

scholarly journals Creation of a model traffic of autonomous vehicles based on Arduino

2019 ◽  
Vol 7 (4) ◽  
pp. 1-9
Author(s):  
Nataliia Valko ◽  
Tymofii Bolharin ◽  
Kateryna Valko
Keyword(s):  
Autonomous Vehicles ◽  
Transport Model ◽  
Experimental Testing ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Process Automation ◽  
Movement Model ◽  
Traffic Lights ◽  
The Creation ◽  
Autonomous Movement

The development of technologies and their integration into society makes the creation of models and algorithms of process automation relevant. One of these tasks is the creation of an autonomous vehicle. The article describes an example of create a self-driving transport model based on the Arduino board. The statement of the task is done, the technical characteristics of the model, the conditions of autonomous movement, and also external obstacles, such as traffic lights, pedestrians, traffic signs, are determined. The model uses linear light sensor for control itself. An empirical study of the concept of autonomy of vehicles was carried out: collecting information about various implementations of autonomous driving and its supporting systems, analyzing the technical characteristics of the hardware for building the system, developing options for autonomous driving using various equipment, experimental testing of hypotheses about the suitability of an autonomous control system, testing and evaluation car work. The autonomous movement model was tested on a radio-controlled car (scale 1:10). To control the movement, the Arduino MEGA microcontroller was selected.

scholarly journals From Traditional to Autonomous Vehicles: A Systematic Review of Data Availability

2021 ◽  
pp. 036119812110575
Author(s):  
Leandro Masello ◽  
Barry Sheehan ◽  
Finbarr Murphy ◽  
German Castignani ◽  
Kevin McDonnell ◽  
...  
Keyword(s):  
Systematic Review ◽  
Risk Assessment ◽  
Transportation Planning ◽  
Road Safety ◽  
Autonomous Vehicles ◽  
Gap Analysis ◽  
Autonomous Driving ◽  
Data Availability ◽  
Principal Characteristics ◽  
Risk Assessment Models

The increasing accessibility of mobility datasets has enabled research in green mobility, road safety, vehicular automation, and transportation planning and optimization. Many stakeholders have leveraged vehicular datasets to study conventional driving characteristics and self-driving tasks. Notably, many of these datasets have been made publicly available, fostering collaboration, scientific comparability, and replication. As these datasets encompass several study domains and contain distinctive characteristics, selecting the appropriate dataset to investigate driving aspects might be challenging. To the best of the authors’ knowledge, this is the first paper that performs a systematic review of a substantial number of vehicular datasets covering various automation levels. In total, 103 datasets have been reviewed, 35 of which focused on naturalistic driving, and 68 on self-driving tasks. The paper gives researchers the possibility of analyzing the datasets’ principal characteristics and their study domains. Most naturalistic datasets have been centered on road safety and driver behavior, although transportation planning and eco-driving have also been studied. Furthermore, datasets for autonomous driving have been analyzed according to their target self-driving tasks. A particular focus has been placed on data-driven risk assessment for the vehicular ecosystem. It is observed that there exists a lack of relevant publicly available datasets that challenge the creation of new risk assessment models for semi- and fully automated vehicles. Therefore, this paper conducts a gap analysis to identify possible approaches using existing datasets and, additionally, a set of relevant vehicular data fields that could be incorporated in future data collection campaigns to address the challenge.

scholarly journals MMW Radar-Based Technologies in Autonomous Driving: A Review

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7283
Author(s):  
Taohua Zhou ◽  
Mengmeng Yang ◽  
Kun Jiang ◽  
Henry Wong ◽  
Diange Yang
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Radar Data ◽  
Intelligent Vehicles ◽  
Data Types ◽  
Automated Driving ◽  
Driving Assistance ◽  
High Level

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.

Lane-Marker-Based Map Construction and Map Precision Evaluation Methods Using On-Board Cameras for Autonomous Driving

2020 ◽  
Vol 32 (3) ◽  
pp. 613-623
Author(s):  
Kenta Maeda ◽  
Junya Takahashi ◽  
Pongsathorn Raksincharoensak ◽  
◽  
Keyword(s):  
Autonomous Vehicles ◽  
Evaluation Method ◽  
Autonomous Driving ◽  
Sufficient Information ◽  
High Definition ◽  
Special Purpose Vehicle ◽  
Construction Methods ◽  
Map Construction ◽  
Mapping System ◽  
Cost Efficient

This report describes a map construction and evaluation method based on lane-marker information for autonomous driving. Autonomous driving systems typically require digital high-definition (HD) maps to correct the current position of autonomous vehicles by using localization techniques. However, an HD map is usually costly to generate because it requires a special-purpose vehicle and mapping system with precise and expensive sensors. This report presents a map construction method that uses cost-efficient on-board cameras. We implement two types of map construction methods with two different cameras in terms of range and field of view and test their performances to determine the minimum sensor specification required for autonomous driving. This report also presents a constructed map evaluation method to determine the “usability” of the map for autonomous driving. Given that the system cannot obtain “true” positions of landmarks, the method judges whether the constructed map contains sufficient information for localization via the presented indices “lateral-distance error.” The methods are verified based on mapping and localization errors determined via manual driving tests. Furthermore, the smoothness of steering maneuvers is determined by conducting autonomous driving tests on a proving ground. The results reveal the necessary conditions of sensor requirements, i.e., the constant visibility of landmarks is one of the key factors for ego-localization to conduct autonomous driving.

scholarly journals Cost Effective Mobile Mapping System for Color Point Cloud Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6536
Author(s):  
Cheng-Wei Peng ◽  
Chen-Chien Hsu ◽  
Wei-Yen Wang
Keyword(s):  
Autonomous Vehicles ◽  
Point Cloud ◽  
Cost Effective ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Fine Tuning ◽  
Measurement Unit ◽  
Practical Implementation ◽  
High Definition ◽  
Mobile Mapping

Survey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving from being affordable. As an attempt to solve this problem, we present a cost-effective MMS to generate an accurate 3D color point cloud for autonomous vehicles. Among the major processes for color point cloud reconstruction, we first synchronize the timestamps of each sensor. The calibration process between camera and Lidar is developed to obtain the translation and rotation matrices, based on which color attributes can be composed into the corresponding Lidar points. We also employ control points to adjust the point cloud for fine tuning the absolute position. To overcome the limitation of Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) positioning system, we utilize Normal Distribution Transform (NDT) localization to refine the trajectory to solve the multi-scan dispersion issue. Experimental results show that the color point cloud reconstructed by the proposed MMS has a position error in centimeter-level accuracy, meeting the requirement of high definition (HD) maps for autonomous driving usage.

scholarly journals An Efficient Task Scheduling Strategy Utilizing Mobile Edge Computing in Autonomous Driving Environment

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1221 ◽  
Author(s):  
Liu ◽  
Chen ◽  
Wu ◽  
Deng ◽  
Liu ◽  
...  
Keyword(s):  
High Speed ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Edge Computing ◽  
Prediction Algorithm ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Computing Power

With the rapid development of various new types of services, autonomous driving has received extensive attention. Due to the dense traffic flow, the limited battery life and computing power of the vehicles, intelligent vehicles are unable to support some computationally intensive and urgent tasks. Autonomous driving imposes strict requirements on the response time of the task. Due to the strong computing power and proximity to the terminal of mobile edge computing (MEC) and the arrival of 5G, the task can be unloaded to MEC, and data can be exchanged in milliseconds, which can reduce the task execution time. However, the resources of the MEC server are still very limited. Therefore we proposed a scheduling algorithm that takes into account the special task of the autopilot. Tasks will select the appropriate edge cloud execution and schedule the execution sequence on the edge cloud by the scheduling algorithm. At the same time, we take the mobility of high-speed vehicles into consideration. The position of the vehicle can be obtained by the prediction algorithm, and the task results are returned to the vehicle by means of other edge clouds. The experimental results show that with the increase of the task amount, the algorithm can effectively schedule more tasks to be completed within the specified time, and in different time slots; it can also predict the location of the vehicle and return the result to the vehicle.

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