Deep Reinforcement Learning for Personalized Driving Recommendations to Mitigate Aggressiveness and Riskiness: Modeling and Impact Assessment

Mantouka, Eleni G.; Vlahogianni, Eleni I.

doi:10.1016/j.trc.2022.103770

Cited by 16 publications

(8 citation statements)

References 57 publications

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“…''Driving style" is used for driver recognition and classification purposes rather than for driving pattern recognition [21]. Other studies refer to "personalized driving behavior", but mostly focus on the identification of driverspecific patterns for the purpose of providing personalized feedback and/or adjustment of ADAS functionalities [22]. It is therefore highlighted that in this study:…”

Section: Search Strategy and Study Selection Criteriamentioning

confidence: 99%

“…A driving pattern on the other hand is a specific driving behavior that is repetitively occurring by one or different drivers, and this should be identified at a more disaggregate level, i.e., over very short time frames (within seconds) of driving. On a separate note, the term "personalized driving behavior" refers to the investigation and analysis of the behavior of an individual driver, e.g., for driver detection or for providing personalized feedback [22].…”

Section: Suggested Future Directionsmentioning

confidence: 99%

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Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Tselentis

Papadimitriou

2023

IEEE Open J. Intell. Transp. Syst.

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This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of "optimal" human driving patterns that Automated Vehicles (AV) could 'mimic' to become safer.

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Section: Search Strategy and Study Selection Criteriamentioning

confidence: 99%

Section: Suggested Future Directionsmentioning

confidence: 99%

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Tselentis

Papadimitriou

2023

IEEE Open J. Intell. Transp. Syst.

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show abstract

“…Yang et al [28] employed a reinforcement learning tree to determine the importance of variables for real-time crash risk prediction. Mantouka et al [9] used RL, more specifically the DDPG agent, to personalize driving recommendations that improve driving safety while considering individual driving styles and preferences. Zhu et al [59] employed DDPG using a reward function that combined driving features related to safety, efficiency, and comfort, referencing human driving data.…”

Section: Lstm Lstm (Long Short-term Memorymentioning

confidence: 99%

“…Furthermore, we have categorized the recent literature based on the specific areas of analysis or control. Put simply, some researchers concentrate on enhancing the overall safety of an entire traffic network or a specific region [8,9], such as downtown New York City. Others address crash-related issues occurring on highway segments [10], on ramp/off ramp sections [11], weaving areas [12], and curved segments [13].…”

Section: Introductionmentioning

confidence: 99%

Safety in Traffic Management Systems: A Comprehensive Survey

Dash

et al. 2023

Designs

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Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

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“…As a result, users often lose their direction when faced with many online courses and cannot quickly find which courses they need. It ultimately reduces the user's learning experience and learning efficiency [13][14][15]. erefore, in view of the above problems, this paper proposes a personalized course recommendation method based on learner interest mining in the educational big data environment to solve the problem of low accuracy and limitations of the personalized course recommendation method in the current educational big data environment.…”

Section: Introductionmentioning

confidence: 99%

Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

Zhang

2022

Scientific Programming

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Aiming at the problems of low accuracy and large limitations of the current personalized course recommendation method in the educational big data environment, a personalized course recommendation method based on learner interest mining in the educational big data environment is proposed. First, a corresponding online course recommendation model framework is proposed by adopting GRU, which can effectively solve the problems of gradient disappearance and gradient explosion in the process of training the RNN neural network. Then, by introducing an auto-regressive language model, XLNet (Generalized Autoregressive Pretraining for Language Understanding), the information missing problem under the Mask mechanism in the BERT model is effectively optimized, and bidirectional prediction is achieved. Finally, by introducing a temporal attention mechanism into the model, enough attention is assigned to highlight local important information on key information, which improves the quality of hidden layer feature extraction, and a high-accuracy personalized course recommendation based on learner interest mining is realized. The proposed algorithm is compared with the other three collaborative filtering algorithms and the RNN algorithm through simulation experiments. The results show that the precision, recall, and F1-measure of the proposed algorithm in the personalized course recommendation results for different types of courses under the condition of the same database are all optimal. The largest values were 92.1%, 89.3%, and 90.7%, respectively. The overall performance is better than other comparison algorithms. This method can improve the accuracy and optimization limitations of personalized courses and can fully tap the interests of learners. It is of great significance for learners to choose personalized courses in the current educational big data environment.

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Deep Reinforcement Learning for Personalized Driving Recommendations to Mitigate Aggressiveness and Riskiness: Modeling and Impact Assessment

Cited by 16 publications

References 57 publications

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Safety in Traffic Management Systems: A Comprehensive Survey

Personalized Course Recommendation Method Based on Learner Interest Mining in Educational Big Data Environment

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