Adaptive transitions for automation in cars, trucks, buses and motorcycles

Diederichs, Frederik; Knauss, Alessia; Wilbrink, Marc; Lilis, Yannis; Chrysochoou, Evangelia; Anund, Anna; Bekiaris, Evangelos; Nikolaou, Stella; Finér, Svitlana; Zanovello, Luca; Maroudis, Pantelis; Krupenia, Stas; Absér, Andreas; Dimokas, Nikos; Apoy, Camilla; Karlsson, Johan; Larsson, Annika; Zidianakis, Emmanouil; Efa, Alexander; Widlroither, Harald; Dai, Mengnuo; Teichmann, Daniel; Sanatnama, Hamid; Wendemuth, Andreas; Bischoff, Sven

doi:10.1049/iet-its.2018.5342

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Furthermore, the study of Diederichs et al. [21] investigated monitoring‐based adaptive transitions in automated driving, through a well‐established HMI framework, with respect to various types of vehicles and different use cases.…”

Section: Introductionmentioning

confidence: 99%

“…The multiclass support vector machine (MSVM) classifier was implemented for the computational analysis, while stressful traffic situations were taken into consideration. Furthermore, the study of Diederichs et al [21] investigated monitoring-based adaptive transitions in automated driving, through a well-established HMI framework, with respect to various types of vehicles and different use cases.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive selection of driving automation levels in highly automated vehicles leveraging on Bayesian networking principles

Panagiotopoulos

Dimitrakopoulos

2022

IET Intelligent Trans Sys

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Highly automated vehicles (HAVs) are expected to fundamentally change the on-road transportation field by increasing traffic flow efficiency, reducing road crashes caused by human errors, and increasing comfort and driving productivity. With the help of recent developments in cognitive management techniques and machine learning analysis, intelligent on-board computing services are gaining acceptance. The main goal of the present paper is to introduce and develop a novel on-board cognitive decision-making functionality that dynamically and automatically enables HAVs to operate each time in the best available level of driving automation (LoDA). The proposed functionality utilizes several attributes associated with the driving environment and the driver's personality characteristics and personal preferences. The cognitive nature of the proposed functionality is based on previous knowledge, turned into experience, by implementing the Naive Bayes classifier supervised machine learning method. The effectiveness of the proposed cognitive functionality, in terms of accuracy and speed of convergence, in proactively identifying the optimal LoDA, is illustrated by modelling and analysing three scenarios with regards to drivers with different profile data and to driving scenes with different environment characteristics. Therefore, it can operate as an in-car intelligent personal assistant for the drivers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cognitive selection of driving automation levels in highly automated vehicles leveraging on Bayesian networking principles

Panagiotopoulos

Dimitrakopoulos

2022

IET Intelligent Trans Sys

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“…Thus, the developed vehicle control solutions must guarantee its cooperation with the human intervention, i.e., during the operation of partially automated systems the intentions and interventions capabilities of the driver must be considered. The limited human capability must be considered especially in the operation transfer between the automated driving system and the human driving [8,9]. Moreover, several control systems actuate together with the driver, e.g., active steering systems can provide additional steering angle to the intervention of the driver [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Although the LPV design also involves the secondary performance specifications (7), it can be effectively incorporated in the RL-based control design. Thus, the role of the RL-based controller is to improve the primary performance (2) and the secondary performances (7) and (9).…”

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confidence: 99%

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Coordination of Lateral Vehicle Control Systems Using Learning-Based Strategies

Németh

2021

Energies

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The paper proposes a novel learning-based coordination strategy for lateral control systems of automated vehicles. The motivation of the research is to improve the performance level of the coordinated system compared to the conventional model-based reconfigurable solutions. During vehicle maneuvers, the coordinated control system provides torque vectoring and front-wheel steering angle in order to guarantee the various lateral dynamical performances. The performance specifications are guaranteed on two levels, i.e., primary performances are guaranteed by Linear Parameter Varying (LPV) controllers, while secondary performances (e.g., economy and comfort) are maintained by a reinforcement-learning-based (RL) controller. The coordination of the control systems is carried out by a supervisor. The effectiveness of the proposed coordinated control system is illustrated through high velocity vehicle maneuvers.

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