Decision-Making and Planning Method for Autonomous Vehicles Based on Motivation and Risk Assessment

Wang, Yisong; Wang, Chunyan; Zhao, Wanzhong; Xu, Can

doi:10.1109/tvt.2021.3049794

Cited by 51 publications

(18 citation statements)

References 27 publications

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“…In order to save energy and reduce emissions in the production process, some multiobjective optimization methods and algorithms have been reported at home and abroad. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. However, these studies are only static calculations of different types of production equipment, reporting qualitative analyses of multiple types of equipment.…”

Section: Comparison Of Different Equipment: Optimization Resultsmentioning

confidence: 99%

“…Fuzzy analytic hierarchy process (FAHP) and TOPSIS were used to solve the model. Wang et al [14] proposed a motivation and risk-assessment decision and planning method; the method can effectively advocate real-time decision-driving behavior according to the current environment. Jiskani et al [15] proposed and tested an indicator framework for analyzing and prioritizing the identified indicators in order to render technical assistance for the implementation of green and climate-smart mining.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Evaluation of Energy Consumption and Carbon Emission Levels of Products Produced by Different Kinds of Equipment Based on Green Development Concept

et al. 2022

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Energy consumption and carbon emission levels in the production process constitute an important basis for the selection of production equipment. The energy consumption and carbon emission levels of the same product produced by different kinds equipment vary greatly from one tool to another. Unfortunately, traditional modes of selection only give qualitative results, so that it is difficult to provide a quantitative reference to enable enterprises to choose appropriate modes of production in the context of the green development concept (GDC). In order to solve this problem, a calculation method for multiple energy consumption and carbon-emission objectives for commodity production is proposed. The focus of this paper is to research the difference between the energy consumption and carbon emission levels of the same product produced by different kinds of equipment. The energy consumption and carbon emissions of different kinds of equipment can be calculated by gray wolf algorithm. The results show that the proposed method can calculate the optimal values of energy consumption and carbon emissions in the same kinds of products produced by different equipment, which can provide assistance for enterprises in choosing the production equipment that best conforms to the green development concept.

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Section: Comparison Of Different Equipment: Optimization Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and Evaluation of Energy Consumption and Carbon Emission Levels of Products Produced by Different Kinds of Equipment Based on Green Development Concept

et al. 2022

View full text Add to dashboard Cite

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“…Study [16] has proposed a path following and yaw controllers based on model predictive control (MPC) for A-EVs for double lane routes. Authors in [17] have proposed a real-time decision-making process based on real drivers' motivation on changing driving plans. Further, a risk-assessment model based on trajectory prediction of nearby drivers has been done in this work.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, in [13], multiple parameters in the trajectory planning have been considered which tuning of the weights associated with each parameter is challenging. Majority of previous works concerning A-EVs, such as [3], [16], [17], [21], [22], [34], have analyzed A-EVs for sensors, signal processing and related accurate path following issues, while trajectory planning and charging scheduling of A-EVs have not been well explored. Based on the presented motivation and existing gaps, the main contributions of the current work are.…”

Section: A Research Gaps and Contributionsmentioning

confidence: 99%

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Hierarchical User-Driven Trajectory Planning and Charging Scheduling of Autonomous Electric Vehicles

Mansour-Saatloo

Mehrabi

Marzband

et al. 2023

IEEE Trans. Transp. Electrific.

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Autonomous electric vehicles (A-EVs), regarded as one of the innovations to accelerate transportation electrification, have sparked a flurry of interest in trajectory planning and charging scheduling. In this regard, this work employs mobile edge computing (MEC) to design a decentralized hierarchical algorithm for finding an optimal path to the nearby A-EV parking lots (PL), selecting the best PL, and executing an optimal charging scheduling. The proposed model makes use of unmanned aerial vehicles (UAVs) to assist edge servers in trajectory planning by surveying road traffic flow in real-time. Further, the target PLs are selected using a user-driven multi-objective problem to minimize the cost and waiting time of A-EVs. To tackle the complexity of the optimization problem, a greedy-based algorithm has been developed. Finally, charging/discharging power is scheduled using a local optimizer based on the PLs' real-time loads which minimizes the deviation of the charging/discharging power from the average load. The obtained results show that the proposed model can handle charging/discharging requests of on-move A-EVs and bring fiscal and non-fiscal benefits for A-EVs and the power grid, respectively. Moreover, it observed that user satisfaction in terms of traveling time and traveling distance are increased by using the edge-UAV model.

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Decision‐Making and Planning Methods for Autonomous Vehicles Based on Multistate Estimations and Game Theory

Yuan,

Yan,

Yin

et al. 2023

Advanced Intelligent Systems

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A core issue inherent to decision‐making and path‐planning tasks is managing the uncertainties in the motion of dynamic obstacles. Therefore, this article proposes a new decision‐making and path‐planning framework, based on game theory, that considers the multistate future actions of surrounding vehicles. First, multistate future actions of neighboring vehicles, whose driving styles vary, are estimated and fed into a decision‐making module for risk assessment. Then, based on the Stackelberg game theory, the ego vehicle and the rear object vehicle are modeled as two players in the game, and their optimal decisions are obtained. In addition, the path‐planning model incorporates a potential‐field model that utilizes several potential functions to explain the varied styles and physical limitations of the surrounding vehicles. Finally, the trajectory of the ego vehicle is obtained through model predictive control that is based on the outputs of the decision‐making and constructed potential‐field models. The results of simulation experiments that used designed scenarios demonstrate that the proposed method effectively manages various social interactions and generates safe and appropriate trajectories for autonomous vehicles. In addition, the simulation results demonstrate that considering multistate trajectories caused the decision‐making and path‐planning modules to be appropriate for unpredictable environmental conditions.

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Decision-Making and Planning Method for Autonomous Vehicles Based on Motivation and Risk Assessment

Cited by 51 publications

References 27 publications

Analysis and Evaluation of Energy Consumption and Carbon Emission Levels of Products Produced by Different Kinds of Equipment Based on Green Development Concept

Analysis and Evaluation of Energy Consumption and Carbon Emission Levels of Products Produced by Different Kinds of Equipment Based on Green Development Concept

Hierarchical User-Driven Trajectory Planning and Charging Scheduling of Autonomous Electric Vehicles

Decision‐Making and Planning Methods for Autonomous Vehicles Based on Multistate Estimations and Game Theory

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