Tactical Decisions for Lane Changes or Lane Following: Assessment of Automated Driving Styles Under Real-World Conditions

Ossig, Johannes; Cramer, Stephanie; Eckl, Anna; Bengler, Klaus

doi:10.1109/tiv.2022.3161570

Cited by 14 publications

(5 citation statements)

References 26 publications

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“…(2023) proposed a system that detects obstacles on roads, such as cars and potholes, utilizing video data. Ossig et al. (2023) analyzed automated driving styles, taking into account external factors such as time pressure and specific driving scenarios.…”

Section: Methodsmentioning

confidence: 99%

“…Indrabayu et al (2023) proposed a system that detects obstacles on roads, such as cars and potholes, utilizing video data. Ossig et al (2023) analyzed automated driving styles, taking into account external factors such as time pressure and specific driving scenarios. Notomista and Botsch (2017) presented a segmentation system specifically tailored to parking maneuvers, using a small-scale vehicle.…”

Section: Related Workmentioning

confidence: 99%

“…The need to promptly assess complex road environments highlights the crucial role of self-driving vehicles in replicating human driving behavior while selecting modes and lanes (Ossig et al, 2023;Notomista and Botsch, 2017;Herrmann et al, 2021). This dual requirement demands real-time analysis of complex traffic situations, and now there are several popular options for solving this problem.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Procedure for describing traffic situation scene development

Korotysheva,

Zhukov

2024

IJIUS

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PurposeThis study aims to comprehensively address the challenge of delineating traffic scenarios in video footage captured by an embedded camera within an autonomous vehicle.Design/methodology/approachThis methodology involves systematically elucidating the traffic context by leveraging data from the object recognition subsystem embedded in vehicular road infrastructure. A knowledge base containing production rules and logical inference mechanism was developed. These components enable real-time procedures for describing traffic situations.FindingsThe production rule system focuses on semantically modeling entities that are categorized as traffic lights and road signs. The effectiveness of the methodology was tested experimentally using diverse image datasets representing various meteorological conditions. A thorough analysis of the results was conducted, which opens avenues for future research.Originality/valueOriginality lies in the potential integration of the developed methodology into an autonomous vehicle’s control system, working alongside other procedures that analyze the current situation. These applications extend to driver assistance systems, harmonized with augmented reality technology, and enhance human decision-making processes.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Procedure for describing traffic situation scene development

Korotysheva,

Zhukov

2024

IJIUS

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“…So, the effect it achieves is still limited and cannot meet the needs of realistic intelligent assistance systems. In recent years, research on identifying driving style [7][8][9] has gradually become mainstream, but the definition of driving style is not unified due to various factors. Existing research methods mainly focus on driving style analysis based on questionnaires, which have the characteristics of high economy, short periodicity, easy quantification of results, and easy statistical analysis, and this allows for better classification and prediction of driving violations while being applied at scale.…”

Section: Introductionmentioning

confidence: 99%

Driving Violation Prediction Based on an Emotional Style Transfer Network

Wang

2023

Sustainability

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Emotions are closely related to driving behavior, and drivers with different emotions have different degrees of bad driving behavior. In order to explore the relationship between emotions and driving violations, a prediction model based on an emotional style transfer network is proposed. First, inspired by the idea of generative adversarial networks (GAN), the eigenvalues of emotions are extracted. Secondly, the one-way propagation method of the GAN network is improved to cyclic generation, which avoids the problems of non-convergence and long periods in the data training process, improving the utilization of training data. Thirdly, a driving violation prediction model is designed. In this model, the emotion factors are designed as time-related sequences, and by improving the Long Short-Term Memory (LSTM) model, the encoding and decoding processes of the time-related sequences are added to form the context, which improves the accuracy of prediction. Finally, the experimental and simulation data show that the proposed model has significant advantages in loss value, accuracy rate, macro-average score, and other indicators. At the same time, an emotion-induction scheme is given to reduce the possibility of driving violations. Furthermore, the proposed model can provide a theoretical basis for the impact of emotions on driving safety.

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