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Artificial Intelligence for Connected and Automated Vehicles

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 20802

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Special Issue Editors

Prof. Dr. Stratis Kanarachos

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Guest Editor

1. School of Mechanical, Aerospace and Automotive Engineering, Faculty of Engineering, Environment and Computing, Coventry University, Coventry CV1 5FB, UK
2. Head of School of Engineering, Coventry University Egypt branch
Interests: motion control; active dynamics and self-learning systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Aristotelis Naniopoulos

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Guest Editor

Transportation Systems, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: intelligent transport systems

Dr. Dimitrios Nalmpantis

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Guest Editor

School of Civil Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, P.O. Box 452, 541 24 Thessaloniki, Greece
Interests: transportation; mobility; road safety; accessibility; intelligent transportation systems; traffic psychology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Vasile Palade

E-Mail Website
Guest Editor

Research Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, UK
Interests: artificial Intelligence; data science; machine learning; computational intelligence; neural networks; deep learning; neuro-fuzzy systems; various nature-inspired algorithms
Special Issues, Collections and Topics in MDPI journals

Dr. Islam Babaev

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Co-Guest Editor

Head of Functional Safety Arrival/Roborace, Saint Petersburg, Russia
Interests: functional safety; autonomous driving; vehicle & lightweight truck design; safety; reliability

Special Issue Information

Dear Colleagues,

Connected and automated vehicles (CAVs) will provide greater transport convenience and interconnectivity, increase mobility options for young and elderly people, and reduce trafﬁc accidents, congestion and emissions by exploiting Artificial Intelligence and communication technologies. At the same time, major barriers towards the public deployment of CAVs and the realization of smart cities exist, including the safety evaluation and validation of Artificial Intelligence-based vehicle functions. This Special Issue aims to bring together recent advances in methods and tools in the areas of deep learning, knowledge discovery and forecasting, as well as testing and validation, to make connected and automated vehicles efficient and safe. Particularly, we invite contributions that identify and provide insight into the limitations of Artificial Intelligence-based functions for connected and automated vehicles and/or advance the state of the art by breaking existing limitations.

Prof. Dr. Stratis Kanarachos
Prof. Dr. Aristotelis Naniopoulos
Dr. Dimitrios Nalmpantis
Dr. Vasile Palade
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Deep Learning
Deep Neural Networks
Connected Vehicles
Automated Vehicles
Big Data
Smart Cities

Published Papers (6 papers)

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Research

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23 pages, 3995 KiB

Open AccessArticle

Learning to Localise Automated Vehicles in Challenging Environments Using Inertial Navigation Systems (INS)

by Uche Onyekpe, Vasile Palade and Stratis Kanarachos

Appl. Sci. 2021, 11(3), 1270; https://0-doi-org.brum.beds.ac.uk/10.3390/app11031270 - 30 Jan 2021

Cited by 23 | Viewed by 3528

Abstract

An approach based on Artificial Neural Networks is proposed in this paper to improve the localisation accuracy of Inertial Navigation Systems (INS)/Global Navigation Satellite System (GNSS) based aided navigation during the absence of GNSS signals. The INS can be used to continuously position autonomous vehicles during GNSS signal losses around urban canyons, bridges, tunnels and trees, however, it suffers from unbounded exponential error drifts cascaded over time during the multiple integrations of the accelerometer and gyroscope measurements to position. More so, the error drift is characterised by a pattern dependent on time. This paper proposes several efficient neural network-based solutions to estimate the error drifts using Recurrent Neural Networks, such as the Input Delay Neural Network (IDNN), Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), and Gated Recurrent Unit (GRU). In contrast to previous papers published in literature, which focused on travel routes that do not take complex driving scenarios into consideration, this paper investigates the performance of the proposed methods on challenging scenarios, such as hard brake, roundabouts, sharp cornering, successive left and right turns and quick changes in vehicular acceleration across numerous test sequences. The results obtained show that the Neural Network-based approaches are able to provide up to 89.55% improvement on the INS displacement estimation and 93.35% on the INS orientation rate estimation. Full article

(This article belongs to the Special Issue Artificial Intelligence for Connected and Automated Vehicles)

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23 pages, 644 KiB

Open AccessArticle

Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes

by Felix Batsch, Alireza Daneshkhah, Vasile Palade and Madeline Cheah

Appl. Sci. 2021, 11(2), 775; https://0-doi-org.brum.beds.ac.uk/10.3390/app11020775 - 15 Jan 2021

Cited by 13 | Viewed by 3073

Abstract

Assuring the safety of automated vehicles is essential for their timely introduction and acceptance by policymakers and the public. To assess their safe design and robust decision making in response to all possible scenarios, new methods that use a scenario-based testing approach are needed, as testing on public roads in normal traffic would require driving millions of kilometres. We make use of the scenario-based testing approach and propose a method to model simulated scenarios using Gaussian Process based models to predict untested scenario outcomes. This enables us to efficiently determine the performance boundary, where the safe and unsafe scenarios can be evidently distinguished from each other. We present an iterative method that optimises the parameter space of a logical scenario towards the most critical scenarios on this performance boundary. Additionally, we conduct a novel probabilistic sensitivity analysis by efficiently computing several variance-based sensitivity indices using the Gaussian Process models and evaluate the relative importance of the scenario input parameters on the scenario outcome. We critically evaluate and investigate the usefulness of the proposed Gaussian Process based approach as a very efficient surrogate model, which can model the logical scenarios effectively in the presence of uncertainty. The proposed approach is applied on an exemplary logical scenario and shows viability in finding concrete critical scenarios. The reported results, derived from the proposed approach, could pave the way to more efficient testing of automated vehicles and instruct further physical tests on the determined critical scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence for Connected and Automated Vehicles)

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21 pages, 1080 KiB

Open AccessArticle

Generation of Pedestrian Crossing Scenarios Using Ped-Cross Generative Adversarial Network

by James Spooner, Vasile Palade, Madeline Cheah, Stratis Kanarachos and Alireza Daneshkhah

Appl. Sci. 2021, 11(2), 471; https://0-doi-org.brum.beds.ac.uk/10.3390/app11020471 - 06 Jan 2021

Cited by 11 | Viewed by 2333

Abstract

The safety of vulnerable road users is of paramount importance as transport moves towards fully automated driving. The richness of real-world data required for testing autonomous vehicles is limited and furthermore, available data do not present a fair representation of different scenarios and rare events. Before deploying autonomous vehicles publicly, their abilities must reach a safety threshold, not least with regards to vulnerable road users, such as pedestrians. In this paper, we present a novel Generative Adversarial Networks named the Ped-Cross GAN. Ped-Cross GAN is able to generate crossing sequences of pedestrians in the form of human pose sequences. The Ped-Cross GAN is trained with the Pedestrian Scenario dataset. The novel Pedestrian Scenario dataset, derived from existing datasets, enables training on richer pedestrian scenarios. We demonstrate an example of its use through training and testing the Ped-Cross GAN. The results show that the Ped-Cross GAN is able to generate new crossing scenarios that are of the same distribution from those contained in the Pedestrian Scenario dataset. Having a method with these capabilities is important for the future of transport, as it will allow for the adequate testing of Connected and Autonomous Vehicles on how they correctly perceive the intention of pedestrians crossing the street, ultimately leading to fewer pedestrian casualties on our roads. Full article

(This article belongs to the Special Issue Artificial Intelligence for Connected and Automated Vehicles)

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18 pages, 2002 KiB

Open AccessArticle

Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways

by Christian Knies and Frank Diermeyer

Appl. Sci. 2020, 10(22), 8154; https://0-doi-org.brum.beds.ac.uk/10.3390/app10228154 - 18 Nov 2020

Cited by 12 | Viewed by 2530

Abstract

Future automated vehicles will have to meet the challenge of anticipating the intentions of other road users in order to plan their own behavior without compromising safety and efficiency of the surrounding road traffic. Therefore, the research area of cooperative driving deals with maneuver-planning algorithms that enable vehicles to behave cooperatively in interactive traffic scenarios. To prove the functionality of these algorithms, single test scenarios are used in the current body of literature. The use of a single, exemplary scenario bears the risk that the presented approach only works in the presented scenario and thus no general statement can be made about the performance of the algorithm. Furthermore, there is a risk that fictitious traffic scenarios may be solved which do not occur in reality. Therefore, we present a procedure for generating test scenarios based on real-world traffic datasets that require cooperation of at least one of the involved vehicles and thus are challenging from the perspective of cooperation. This procedure is applied to a large highway traffic dataset, resulting in a test scenario catalog that allows a comprehensive performance evaluation. The extracted scenarios are clustered according to the cooperative actions used to solve the respective scenario, which enables a more detailed understanding of the underlying cooperative mechanisms. In order to serve as a basis for making comparisons between different behavior planners and thus contribute to the development of future maneuver planning algorithms, a tool to extract the test scenarios from the used traffic dataset is made publicly available. Full article

(This article belongs to the Special Issue Artificial Intelligence for Connected and Automated Vehicles)

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20 pages, 6471 KiB

Open AccessArticle

A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

by Hanbing Wei, Yanhong Wu, Xing Chen and Jin Xu

Appl. Sci. 2020, 10(7), 2626; https://0-doi-org.brum.beds.ac.uk/10.3390/app10072626 - 10 Apr 2020

Cited by 4 | Viewed by 2636

Abstract

For investigating driver characteristic as well as control authority allocation during the process of human–vehicle shared control (HVSC) for an autonomous vehicle (AV), a HVSC dynamic mode with a driver’s neuromuscular (NMS) state parameters was proposed in this paper. It takes into account the driver’s NMS characteristics such as stretch reflection and reflex stiffness. By designing a model predictive control (MPC) controller, the vehicle’s state feedback and driver’s state are incorporated to construct the HVSC dynamic model. For the validation of the model, a field experiment was conducted. The vehicle state signals are collected by V-BOX, and the driver’s state signals are obtained with the electromyography instrument. Subsequently, the hierarchical least square (HLS) parameter identification algorithm was implemented to identify the parameters of the model based on the experimental results. Moreover, the Unscented Kalman Filter (UKF) was utilized to estimate the important NMS parameters which cannot be measured directly. The experimental results showed that the model we proposed has excellent accuracy in characterizing the vehicle’s dynamic state and estimating the driver’s NMS parameter. This paper will serve as a theoretical basis for the new control strategy allocation between human and vehicle for L3 class AVs. Full article

(This article belongs to the Special Issue Artificial Intelligence for Connected and Automated Vehicles)

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Review

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26 pages, 490 KiB

Open AccessReview

Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions

by Filip Vrbanić, Edouard Ivanjko, Krešimir Kušić and Dino Čakija

Appl. Sci. 2021, 11(6), 2574; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062574 - 13 Mar 2021

Cited by 34 | Viewed by 5267

Abstract

The trend of increasing traffic demand is causing congestion on existing urban roads, including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and an increase in fuel consumption. Lack of space and non-compliance with cities’ sustainable urban plans prevent the expansion of new transport infrastructure in some urban areas. To alleviate the aforementioned problems, appropriate solutions come from the domain of Intelligent Transportation Systems by implementing traffic control services. Those services include Variable Speed Limit (VSL) and Ramp Metering (RM) for urban motorways. VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging. VSL and RM can reduce traffic congestion on urban motorways, especially so in the case of mixed traffic flows where AVs and CAVs can fully comply with the control system output. Currently, there is no existing overview of control algorithms and applications for VSL and RM in mixed traffic flows. Therefore, we present a comprehensive survey of VSL and RM control algorithms including the most recent reinforcement learning-based approaches. Best practices for mixed traffic flow control are summarized and new viewpoints and future research directions are presented, including an overview of the currently open research questions. Full article

(This article belongs to the Special Issue Artificial Intelligence for Connected and Automated Vehicles)

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