A Literature Review of the Research on Take-Over Situation in Autonomous Driving

Xin, Xin; Zhao, Min; Zhou, Moli; Lu, Shaohua; Guan, Daisong; Wang, Qianyi; Zhang, Yuezhou

doi:10.1007/978-3-030-23538-3_12

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Another research branch on downward ToCs has placed emphasis on determining human and AV system factors that affect driving performance during the ToC-preparation and post-ToC phases. References [20], [21] conducted literature review studies to identify factors that influence response time to ToRs (available lead time, involvement in secondary tasks, take-over request functionality etc.) and post-takeover vehicle control (braking-steering) in different traffic situations.…”

Section: Related Work a Transitions Of Controlmentioning

confidence: 99%

Reinforcement Learning-Based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

Alms

Noulis

Mintsis

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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An important aspect of automated driving is to handle situations where it fails or is not allowed in specific traffic situations. This case study explores means, by which control transitions in a mixed autonomy system can be organized in order to minimize their adverse impact on traffic flow. We assess a number of different approaches for a coordinated management of transitions, covering classic traffic management paradigms and AI-driven controls. We demonstrate that they yield excellent results when compared to a do-nothing scenario. This text further details a model for control transitions that is the basis for the simulation study presented. The results encourage the deployment of reinforcement learning on the control problem for a scenario with mandatory take-over requests.Index Terms-connected automated vehicles (CAV), reinforcement learning (RL), take-over request (ToR), traffic management (TM), transition of control (ToC) NOMENCLATURE AV Automated vehicle CAV Connected automated vehicle CV Connected vehicle LoD Level of demand MDP Markov decision process MRM Minimum risk manoeuvre MV Manual vehicle No-AD No automated driving RL Reinforcement learning RSI Roadside infrastructure TM Traffic management TMC Traffic management center ToC Transition of control ToR Take-over request

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Section: Related Work a Transitions Of Controlmentioning

confidence: 99%

Reinforcement Learning-Based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

Alms

Noulis

Mintsis

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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show abstract

“…Under certain driving situations, as surveyed by Xin et al [14], a HAV's autonomous driving ability can be challenged. In somes cases, it can lead to a take over by a human driver, which can be regarded as a factor of discomfort for passengers.…”

Section: A Comfort Knowledge Modelmentioning

confidence: 99%

A Definition and Framework for Vehicular Knowledge Networking

Deveaux¹,

Higuchi²,

Uçar³

et al. 2020

Preprint

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To operate intelligent vehicular applications such as automated driving, machine learning, artificial intelligence and other mechanisms are used to abstract from information what is commonly referred to as knowledge. Defined as a state of understanding obtained through experience and analysis of collected information, knowledge is promising for vehicular applications. However, it lacks a unified framework to be cooperatively created and shared to achieve its full potential. This paper investigates on the meaning and scope of knowledge applied to vehicular networks, and suggests a structure for vehicular knowledge description, storage and sharing. Through the example of passenger comfort-based rerouting, it exposes the potential benefits for network load and delay of such knowledge structuring.

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Practical and Provable Secure Vehicular Component Protection Scheme

Zhang

Chen

2022

Communications in Computer and Information Science

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A Literature Review of the Research on Take-Over Situation in Autonomous Driving

Cited by 6 publications

References 14 publications

Reinforcement Learning-Based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

Reinforcement Learning-Based Traffic Control: Mitigating the Adverse Impacts of Control Transitions

A Definition and Framework for Vehicular Knowledge Networking

Practical and Provable Secure Vehicular Component Protection Scheme

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