Yazar "Langari, Reza" seçeneğine göre listele

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    A Game Theoretic Model Predictive Controller With Aggressiveness Estimation for Mandatory Lane Change
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Zhang, Qingyu; Langari, Reza; Tseng, H. Eric; Filev, Dimitar; Szwabowski, Steven; Coskun, Serdar
    In this article, we develop a game theoretic model predictive controller (GTMPC) with aggressiveness estimation to deal with the mandatory lane change (MLC) problem in presence of several surrounding vehicles. GTMPC is responsible for driving a subject vehicle (SV) to a desired longitudinal position and executing lane-changing at the optimal moment. Specifically, GTMPC constantly establishes and solves Stackelberg games corresponding to multiple game candidate vehicles (GCV) within the game scope when SV is able to interact with GCVs by signaling a lane change intention through turn signal. GTMPC first selects one target vehicle (TV) within multiple GCVs based on the Stackelberg equilibrium, followed by estimating TV's aggressiveness based on the interaction between SV and TV, then completes the maneuver through MPC. GTMPC performance is compared with level-k game theoretic controller. Human-in-the-Loop results showed that GTMPC is capable to safely complete the MLC by properly assessing the aggressiveness of surrounding vehicles, driven either by intelligent driver model or human drivers.
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    Hierarchical Optimization of Speed Planning and Energy Management for Connected Hybrid Electric Vehicles Under Multi-Lane and Signal Lights Aware Scenarios
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Peng, Jinghui; Zhang, Fengqi; Coskun, Serdar; Hu, Xiaosong; Yang, Yalian; Langari, Reza; He, Jinsong
    Connected and automated vehicle technology via vehicle-to-everything communication, can assist in improving energy efficiency for hybrid electric vehicles (HEVs). In particular, information about the timing of traffic lights and surrounding vehicles can be exchanged between traffic vehicles and in conjunction with vehicle state information, to improve the fuel economy of HEVs significantly. To this end, we propose a multi-lane hierarchical optimization (MLHO) algorithm based on a predictive control framework. The dynamic behaviors of the surrounding vehicles are first predicted, and then the traffic light information (e.g., signal phasing and timing) and vehicles' state information are utilized in the design. MLHO is a two-level strategy wherein a multi-lane speed planning method for a host vehicle is formulated to plan the optimal speed and lane-change behaviors by considering vehicle power demand, driving comfort, and safety in the upper level. In the lower level, dynamic programming is adopted to devise energy management by tracking the optimal speed. Simulation results under real routes using the traffic simulation software Simulation of Urban Mobility show that the fuel economy of MLHO is improved by 32% on average compared to speed profile driven by a human driver model. In addition, traffic efficiency is enhanced significantly, i.e., different traffic occupancy results on the road indicate that the proposed MLHO is less affected by the traffic flow density. With different traffic densities, the maximum fuel consumption difference under the three considered scenarios is only 0.645L/100km.
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    Incorporated vehicle lateral control strategy for stability and enhanced energy saving in distributed drive hybrid bus
    (Elsevier, 2021) Li, Lin; Coskun, Serdar; Langari, Reza; Xi, Junqiang
    Vehicle stability and energy efficiency are important considerations in vehicle engineering. In this context, the current paper presents an energy saving strategy for hybrid electric vehicles that incorporates vehicle lateral dynamic control in conjunction with energy efficiency. To this end, we first model the nonlinear vehicle lateral dynamics of a hybrid electric bus via a Takagi-Sugeno approach and combine this model with an H-infinity state-feedback controller via parallel distributed compensation. The controller matrices are obtained using linear matrix inequalities through an optimal energy-to-energy performance norm of the nonlinear vehicle model. Second, we propose a reference side-slip angle generating method and a set of tire force distribution rules, which under the premise of ensuring vehicle stability, minimize the overall energy consumption of the vehicle. Finally, we put forward a new speed prediction method based on vehicle lateral dynamics for hybrid electric vehicle energy saving. Human-in-the-loop simulated driving experiments are conducted where the bus performs lane-changing maneuvers with enhanced control properties under various driving conditions, demonstrating the reliability of the proposed energy-saving performance measures. (C) 2021 Published by Elsevier B.V.
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    Prediction of Traffic Flow Density and Velocity Based on Kalman Filter Fusion of the Non-Local Gas-Kinetic Model and Convolution Neural Networks-Long-Short Term Memory Model
    (Institute of Electrical and Electronics Engineers Inc., 2023) Li, Lin; Zhao, Jiahao; Coskun, Serdar; Langari, Reza
    The integrated perception, planning, and control of intelligent transportation system has become a research hotspot in academia. In this context, the safe running of an intelligent vehicle highly depends on the accurate prediction of its driving environment and working conditions. Traffic flow parameters represent the macro characteristics of an intelligent vehicle driving environment. Accurate prediction of traffic flow parameters is beneficial to improve the accuracy of trajectory prediction of vehicles in the surrounding environment. As two kinds of traffic flow parameter prediction models: the macroscopic traffic flow model and the data-based learning model have their own characteristics. To fully take advantage of these two models, our work proposes a study of the macroscopic traffic flow model based on a nonlocal gas-kinetic (GKT) and a deep learning model based on convolutional neural networks-long short term memory. Real data sets US-101 and PeMS are used respectively to predict traffic flow density and velocity. Finally, the Kalman filter is employed to fuse the results of the two models. The experiment shows that the prediction accuracy of traffic flow density and velocity can be improved as compared with the Macroscopic traffic flow model and the data-based learning model. © 2023 IEEE.
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    Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples
    (Mdpi, 2021) Li, Lin; Coskun, Serdar; Wang, Jiaze; Fan, Youming; Zhang, Fengqi; Langari, Reza
    Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.