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Evolution towards optimal driving strategies for large‐scale autonomous vehicles

Runsong Jiang, Zhangjie Liu, Huiyun Li
2021 IET Intelligent Transport Systems  
With rapidly developing autonomous vehicle (AV) technologies, the optimal driving strategy should consider multi-objective optimization problems of large-scale transportation systems, including safety  ...  The results of multi-vehicle and multi-objective coevolution are enlightening in designing optimal driving strategy with AVs.  ...  ACKNOWLEDGEMENTS This work was supported by CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen Engineering Laboratory for Autonomous  ... 
doi:10.1049/itr2.12076 fatcat:kmtq7e63vrdcbelo4srt3qte4e

Guest Editorial Introduction to the Special Issue on Deep Learning Models for Safe and Secure Intelligent Transportation Systems

Alireza Jolfaei, Neeraj Kumar, Min Chen, Krishna Kant
2021 IEEE transactions on intelligent transportation systems (Print)  
From 2008 to 2013, he served as the Program Director of the NSF, where he managed the Computer Systems Research Program and was instrumental in the development and running of NSF-wide sustainability initiative  ...  He was a Research Professor with the Center for Secure Information Systems, George Mason University.  ...  In the article entitled "Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in the 5G-enabled intelligent transportation system," Yu et al. propose a deep learning-based  ... 
doi:10.1109/tits.2021.3090721 fatcat:c2o2vno6bjbnxdn6y4zm7ztmvq

Reinforcement Learning-Based Motion Planning for Automatic Parking System

Jiren Zhang, Hui Chen, Shaoyu Song, Fengwei Hu
2020 IEEE Access  
The learned strategies ensure the multi-objective optimality of above requirements in the parking process.  ...  Based on this proposed method, we can get rid of human experience largely and learn parking strategies autonomously and quickly.  ...  For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited.  ... 
doi:10.1109/access.2020.3017770 fatcat:wg6wxobyfbdpfmwsrohhdcvv5m

Human-like Driving Decision at Unsignalized Intersections Based on Game Theory [article]

Daofei Li, Guanming Liu, Bin Xiao
2022 arXiv   pre-print
Unsignalized intersection driving is challenging for automated vehicles. For safe and efficient performances, the diverse and dynamic behaviors of interacting vehicles should be considered.  ...  Based on a game-theoretic framework, a human-like payoff design methodology is proposed for the automated decision at unsignalized intersections.  ...  Acknowledgements The authors appreciated Linhui Chen for his help in preparing the video abstract.  ... 
arXiv:2112.06415v2 fatcat:mlkohqi32nbn5gt43udlf4dhtu

Safe Reinforcement Learning for a Robot Being Pursued but with Objectives Covering More Than Capture-avoidance [article]

Huanhui Cao, Zhiyuan Cai, Hairuo Wei, Wenjie Lu, Lin Zhang, Hao Xiong
2022 arXiv   pre-print
To address the safety issue of the self-driven vehicle in this scenario, this paper conducts a preliminary study based on a system of robots.  ...  Simulations and experiments are conducted based on the system of robots to evaluate the effectiveness of the developed safe RL framework.  ...  For safety-critical applications such as self-driven vehicles, the failure of a learned policy could result in collisions of vehicles and injury or death of humans [5] .  ... 
arXiv:2207.00842v1 fatcat:ijdtlea5zzhp3o2f54hatlpkem

Abstracts

2021 IEEE Transactions on Intelligent Vehicles  
This is mainly due to recent advances in machine learning and deep learning, allowing the development of promising methods for autonomous driving.  ...  First, Modular systems that combine a pipeline of methods with each solving one specific sub-task of driving.  ...  A Multimodality Fusion Deep Neural Network and Safety Test Strategy for Intelligent Vehicles Jian Nie, Jun Yan, Huilin Yin, Lei Ren, and Qian Meng doi: 10.1109/TIV.2020.3027319 Multimodality fusion based  ... 
doi:10.1109/tiv.2021.3059618 fatcat:mwkjzjdi2zb65goidppyknkpfq

Deep Reinforcement Learning-Based Driving Strategy for Avoidance of Chain Collisions and Its Safety Efficiency Analysis in Autonomous Vehicles

Abu Jafar Md Muzahid, Syafiq Fauzi Kamarulzaman, Md. Arafatur Rahman, Ali H. Alenezi
2022 IEEE Access  
Then, we consider the problem of chain collision avoidance as a Markov Decision Process problem in order to propose a reinforcement learning-based decision-making strategy and analyse the safety efficiency  ...  Finally, in the safety efficiency analysis phase, we investigated the safety efficiency performance of the agent vehicle in both single-agent and multi-agent autonomous driving environments.  ...  ACKNOWLEDGMENT The authors would like to thank the Ministry of Higher Education of Malaysia for promoting this research and Universiti Malaysia Pahang for providing the laboratory facility and financial  ... 
doi:10.1109/access.2022.3167812 fatcat:monnrkfamvazfchsnm2ivlidvi

Clustering in VANET: Algorithms and Challenges [article]

Mohammad Mukhtaruzzaman, Mohammed Atiquzzaman
2020 arXiv   pre-print
Network mobility (NEMO) and multi-hop-based strategies are also used for VANET clustering.  ...  Mobility and some other clustering strategies are presented in the existing literature reviews; however, extensive study of intelligence-based, mobility-based, and multi-hop-based strategies still missing  ...  For this reason, machine learning-based algorithms, such as k-means, are not used in multi-hop-based strategies, since any member of addition or deletion changes the entire dynamic of the clusters.  ... 
arXiv:2009.01964v1 fatcat:3bfslk4dfzej7jt7pmk2bgo3gq

Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learning [article]

Tianyu Shi, Jiawei Wang, Yuankai Wu, Luis Miranda-Moreno, Lijun Sun
2021 arXiv   pre-print
Instead of learning a reliable behavior for ego automated vehicle, we focus on how to improve the outcomes of the total transportation system by allowing each automated vehicle to learn cooperation with  ...  The fast actuation time allows them having the potential to promote the efficiency and safety of the whole transportation system.  ...  and safety of the system.  ... 
arXiv:2007.02794v5 fatcat:6ybpqsfeendbnjipmcdp532llm

Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data [article]

Qin Lin, Sicco Verwer, John Dolan
2019 arXiv   pre-print
For safety-critical systems such as autonomous vehicles, it can be problematic to use controllers learned from data because they cannot be guaranteed to be collision-free.  ...  Recently, a method has been proposed for learning a multi-mode hybrid automaton cruise controller (MOHA).  ...  oracle providing unsafe counterexamples to improve the model learning part.  ... 
arXiv:1910.13526v1 fatcat:vyxkylpr2jfxnlhpc4jlthszpa

Block Chain and Big Data-Enabled Intelligent Vehicular Communication

Shahid Mumtaz, Anwer Al-Dulaimi, Haris Gacanin, Ai Bo
2021 IEEE transactions on intelligent transportation systems (Print)  
Both academia and industry have already reached a consensus that vehicular communication is a vital element to extend the sensing ability of vehicles for ensuring safety driving.  ...  systems.  ...  The article "Blockchain-based secure computation offloading in vehicular network" investigates the safety and offloading capabilities of a multi-vehicle ECCO system based on blockchain cloud services.  ... 
doi:10.1109/tits.2021.3090720 fatcat:nblknyxbzjfslna3h7zrrtozky

Quantitative Safety Analysis of a Coordinated Emergency Brake Protocol for Vehicle Platoons

Carl Bergenhem, Karl Meinke, Fabian Ström
2018 Zenodo  
In this paper, we present a general methodology to estimate safety related parameter values of cooperative cyber-physical system-of- systems.  ...  The estimation methodology is based on learning-based testing; which is an approach to automated requirements testing that combines machine learning with model checking.  ...  We express special thanks for valuable comments to Magnus Jonsson and Alexey Vinel of Halmstad University.  ... 
doi:10.5281/zenodo.2635772 fatcat:t57xydp4xbh5biwefnlflsiccy

Quantitative Safety Analysis of a Coordinated Emergency Brake Protocol for Vehicle Platoons [chapter]

Carl Bergenhem, Karl Meinke, Fabian Ström
2018 Lecture Notes in Computer Science  
In this paper, we present a general methodology to estimate safety related parameter values of cooperative cyber-physical system-ofsystems.  ...  The estimation methodology is based on learning-based testing; which is an approach to automated requirements testing that combines machine learning with model checking.  ...  We express special thanks for valuable comments to Magnus Jonsson and Alexey Vinel of Halmstad University.  ... 
doi:10.1007/978-3-030-03424-5_26 fatcat:6ys3bvbcgjg7jms2nbiys4tcga

Efficient Energy Management Strategy for Hybrid Electric Vehicles/Plug-in Hybrid Electric Vehicles: Review and Recent Advances under Intelligent Transportation System

Chao Yang, Mingjun Zha, Weida Wang, Kaijia Liu, Changle Xiang
2020 IET Intelligent Transport Systems  
In terms of single-vehicle and multi-vehicle scenarios, the EMSs for HEV/PHEV under intelligent transport system is in-depth reviewed. Finally, the challenges for future research are also identified.  ...  As the continuous development of intelligent connected vehicle technology, designing an efficient EMS with vehicle to infrastructure/ vehicle to vehicle (V2I/V2V) information for HEV/PHEV is still a challenge  ...  The authors also appreciate the support of the Beijing Institute of Technology Research Fund Program for Young Scholars. References  ... 
doi:10.1049/iet-its.2019.0606 fatcat:z55katfpqfgs5jycyvmpw4usk4

Anti-Jerk On-Ramp Merging Using Deep Reinforcement Learning [article]

Yuan Lin, John McPhee, Nasser L. Azad
2020 arXiv   pre-print
Regardless of the jerk penalty, the merging vehicle exhibited decision-making strategies such as merging ahead or behind a main-road vehicle.  ...  We investigated the relationship between collision avoidance for safety and jerk minimization for passenger comfort in the multi-objective reward function by obtaining the Pareto front.  ...  ACKNOWLEDGMENT The authors thank Toyota, Ontario Centres of Excellence, and the Natural Sciences and Engineering Research Council of Canada for the support of this work.  ... 
arXiv:1909.12967v3 fatcat:ldzb5si5pjgdta4fmcla5vn27m
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