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Machine Learning for Connected and Autonomous Vehicle for Mixed Traffic Environment

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 10389

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Special Issue Editors

Dr. G. G. Md. Nawaz Ali

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Guest Editor

Department of Computer Science and Information Systems, Bradley University, Peoria, IL 61625, USA
Interests: vehicular networks; V2X communication; connected and autonomous vehicle; broadcasting; network coding; data analytics

Prof. Dr. Peter Han Joo Chong

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Guest Editor

Department of Electrical and Electronic Engineering, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand
Interests: machine learning techniques applied to software defined vehicular networks; wireless/mobile communications systems including radio resource management, multiple access, MANETs/VANETs, green radio networks, and 5G-V2X networks
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Special Issue Information

Dear Colleagues,

Connected and autonomous vehicles (CAVs) are an imperative emerging technology for the future intelligent transportation systems (ITSs). Early research shows that CAVs can alleviate vehicle collisions, fatalities, traffic congestion, and energy consumption, and can enhance driving comfort. However, undeniably, the future deployment of CAVs will be alongside the pre-existing human-driven vehicles (HDVs) in the legacy road and traffic infrastructure. There are many existing research challenges in CAVs, including reliable communication in intermittent vehicular networks, accurate vehicle state estimation, safe longitudinal and lateral movements, efficient vehicle routing, and energy efficiency, to name but a few. Mixed traffic introduces even more challenges.

Recently, machine learning (ML) has been used for classifying and training CAVs in different aspects. Both supervised and unsupervised ML algorithms are being used in solving different issues in CAVs. Certainly, there is still a huge research gap and opportunities to develop improved ML-based techniques to solve many existing challenges in CAVs. Some of the challenges of ML-based techniques for CAVs are efficient computation, neural architecture, reward functions design, adaptability, generalization, verification and validation, safety, etc.

This Special Issue covers the following relevant areas (not an exhaustive list):

Vehicle trajectory prediction;
Driving behavior prediction;
Safe and efficient longitudinal and lateral movements;
Route planning in mixed traffic;
Vehicle routing in mixed traffic;
Reliable vehicular communications;
Edge/fog-computing-assisted traffic management;
Driving CAVs under legacy road and traffic situations;
Vehicle clustering;
Vehicle state estimation;
CAV communication technologies (WiFi/DSRC/5G LTE/cognitive communications);
Efficient ML (deep learning, reinforcement learning, etc.)-based CAV computation and architecture;
Co-operative and distributed federated learning for CAVs;
ML-assisted security and privacy in CAVs;
Energy efficiency in mixed traffic.

Dr. G. G. Md. Nawaz Ali
Prof. Dr. Peter Han Joo Chong
Guest Editors

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Keywords

Connected and autonomous vehicles (CAVs) Machine learning (ML)
Mixed traffic
Connected vehicles
Vehicular communication
Prediction
Vehicle trajectory

Published Papers (4 papers)

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Research

14 pages, 2070 KiB

Open AccessArticle

Detection and Risk Analysis with Lane-Changing Decision Algorithms for Autonomous Vehicles

by Amin Mechernene, Vincent Judalet, Ahmed Chaibet and Moussa Boukhnifer

Sensors 2022, 22(21), 8148; https://doi.org/10.3390/s22218148 - 24 Oct 2022

Viewed by 1908

Abstract

Despite the great technological advances in ADAS, autonomous driving still faces many challenges. Among them is improving decision-making algorithms so that vehicles can make the right decision inspired by human driving. Not only must these decisions ensure the safety of the car occupants and the other road users, but they have to be understandable by them. This article focuses on decision-making algorithms for autonomous vehicles, specifically for lane changing on highways and sub-urban roads. The challenge to overcome is to develop a decision-making algorithm that combines fidelity to human behavior and that is based on machine learning, with a global structure that allows understanding the behavior of the algorithm and that is not opaque such as black box algorithms. To this end, a three-step decision-making method was developed: trajectory prediction of the surrounding vehicles, risk and gain computation associated with the maneuver and based on the predicted trajectories, and finally decision making. For the decision making, three algorithms: decision tree, random forest, and artificial neural network are proposed and compared based on a naturalistic driving database and a driving simulator. Full article

(This article belongs to the Special Issue Machine Learning for Connected and Autonomous Vehicle for Mixed Traffic Environment)

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14 pages, 1712 KiB

Open AccessArticle

Real-Time Adaptive Traffic Signal Control in a Connected and Automated Vehicle Environment: Optimisation of Signal Planning with Reinforcement Learning under Vehicle Speed Guidance

by Saeed Maadi, Sebastian Stein, Jinhyun Hong and Roderick Murray-Smith

Sensors 2022, 22(19), 7501; https://doi.org/10.3390/s22197501 - 03 Oct 2022

Cited by 5 | Viewed by 3299

Abstract

Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions. Full article

(This article belongs to the Special Issue Machine Learning for Connected and Autonomous Vehicle for Mixed Traffic Environment)

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19 pages, 3902 KiB

Open AccessArticle

Time-Series-Based Personalized Lane-Changing Decision-Making Model

by Ming Ye, Lei Pu, Pan Li, Xiangwei Lu and Yonggang Liu

Sensors 2022, 22(17), 6659; https://doi.org/10.3390/s22176659 - 02 Sep 2022

Cited by 3 | Viewed by 1532

Abstract

In recent years, autonomous driving technology has been changing from “human adapting to vehicle” to “vehicle adapting to human”. To improve the adaptability of autonomous driving systems to human drivers, a time-series-based personalized lane change decision (LCD) model is proposed. Firstly, according to the characteristics of the subject vehicle (SV) with respect to speed, acceleration and headway, an unsupervised clustering algorithm, namely, a Gaussian mixture model (GMM), is used to identify its three different driving styles. Secondly, considering the interaction between the SV and the surrounding vehicles, the lane change (LC) gain value is produced by developing a gain function to characterize their interaction. On the basis of the recognition of the driving style, this gain value and LC feature parameters are employed as model inputs to develop a personalized LCD model on the basis of a long short-term memory (LSTM) recurrent neural network model (RNN). The proposed method is tested using the US Open Driving Dataset NGSIM. The results show that the accuracy, F1 score, and macro-average area under the curve (macro-AUC) value of the proposed method for LC behavior prediction are 0.965, 0.951 and 0.983, respectively, and the performance is significantly better than that of other mainstream models. At the same time, the method is able to capture the LCD behavior of different human drivers, enabling personalized driving. Full article

(This article belongs to the Special Issue Machine Learning for Connected and Autonomous Vehicle for Mixed Traffic Environment)

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18 pages, 2029 KiB

Open AccessArticle

Formation Control of Automated Guided Vehicles in the Presence of Packet Loss

by Leila Sedghi, Jobish John, Md Noor-A-Rahim and Dirk Pesch

Sensors 2022, 22(9), 3552; https://doi.org/10.3390/s22093552 - 07 May 2022

Cited by 5 | Viewed by 2141

Abstract

This paper presents the formation tracking problem for non-holonomic automated guided vehicles. Specifically, we focus on a decentralized leader–follower approach using linear quadratic regulator control. We study the impact of communication packet loss—containing the position of the leader—on the performance of the presented formation control scheme. The simulation results indicate that packet loss degrades the formation control performance. In order to improve the control performance under packet loss, we propose the use of a long short-term memory neural network to predict the position of the leader by the followers in the event of packet loss. The proposed scheme is compared with two other prediction methods, namely, memory consensus protocol and gated recurrent unit. The simulation results demonstrate the efficiency of the long short-term memory in packet loss compensation in comparison with memory consensus protocol and gated recurrent unit. Full article

(This article belongs to the Special Issue Machine Learning for Connected and Autonomous Vehicle for Mixed Traffic Environment)

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Machine Learning for Connected and Autonomous Vehicle for Mixed Traffic Environment

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