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Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

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What this work says

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Influence of driver characteristics on emissions and fuel consumption
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Learning the human longitudinal control behavior with a modular hierarchical Bayesian Mixture-of-Behaviors model
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Multi-vehicle motion coordination using V2V communication
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Inverse Reinforcement Learning via Neural Network in Driver Behavior Modeling
inferredcites →https://doi.org/10.1109/jproc.2006.888405openalex-snapshot
Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification
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Three Decades of Driver Assistance Systems: Review and Future Perspectives
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IsV2C: An Integrated Road Traffic-Network-Cloud Simulator for V2C Connected Car Services
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Driver Behavior Characteristics Identification Strategies Based on Bionic Intelligent Algorithms
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Optimal Energy Management for HEVs in Eco-Driving Applications Using Bi-Level MPC
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Energetic Impacts Evaluation of Eco-Driving on Mixed Traffic With Driver Behavioral Diversity
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Inverse Reinforcement Learning Based Driver Behavior Analysis and Fuel Economy Assessment
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Apprenticeship learning via inverse reinforcement learning
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Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers
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Maximum Entropy Inverse Reinforcement Learning
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A Predictive Control Design with Speed Previewing Information for Vehicle Fuel Efficiency Improvement
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Personalized Adaptive Cruise Control and Impacts on Mixed Traffic
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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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