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

Most driving recommendation and assistance systems, such as Advanced Driving Assistance Systems (ADAS), are usually designed based on the behavior of an average driver. Nevertheless, personalized driving systems that can be adapted to different driving styles and recognize individual needs and prefe...

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Bibliographic Details
Main Authors:	Mantouka, E.G (Author), Vlahogianni, E.I (Author)
Format:	Article
Language:	English
Published:	Elsevier Ltd 2022
Subjects:	Advanced driver assistance systems Automobile drivers Behavioral research Clustering Clusterings DDPG Deep learning Deep reinforcement learning Driving behavior Driving behaviour Driving recommendation Driving recommendations Driving styles Learning algorithms Learning systems Microsimulation Model assessment Motor transportation Naturalistic driving Recommender systems Reinforcement learning Reinforcement learnings Safety engineering
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View Fulltext in Publisher

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

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