Finding the ‘faster’ path in vehicle routing

Guo, Jun; Wu, Yaoxin; Zhang, Xuexi; Zhang, Le; Chen, Wei; Cao, Zhiguang; Lu, Zongqing; Guo, Hongliang

doi:10.1049/iet-its.2016.0288

Cited by 9 publications

(7 citation statements)

References 35 publications

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“…Driving style space should be analyzed before driver style clustering to ensure the rationality and reliability of the results of the LFCM. A driver can choose from a wide selection of varied details, generally, including time-saving [1][2][3], economic [26,27] and comfort [4,9]. For ease of discussion, we record the set of three indicators above as a style space.…”

Section: Establishment Of Driving Style Spacementioning

confidence: 99%

“…Mature navigation systems and path planning algorithms mainly focus on the fastest path [1][2][3][4], the shortest path [5][6][7][8] or the most comfortable path [4,9] between specified starting and target points. In recent years, path optimization algorithms of multi-objective optimization [10][11][12][13][14] and single-objective optimization [15,16], as well as a few traffic impact factors, have deepened [17,18] and popular routes planning [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Here, space-time paths of time geography [30] are used to excavate the driving characteristics ( Figure 2), in which the two-dimensional coordinates represent the historical space position of the vehicle, while the vertical coordinates indicate the time of the vehicle arriving at the corresponding space position. The relationships among the speed of driving at any time , the acceleration of driving , and the tangent slope of the inclined curve is shown in Formulas (1) and (2).…”

mentioning

confidence: 99%

“…The quality of the natural environment has a significant impact on the outcome of a user's path selection. The definition of the evaluation index is The relationships among the speed of driving at any time , the acceleration of driving , and the tangent slope of the inclined curve is shown in Formulas (1) and (2).…”

mentioning

confidence: 99%

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Path Planning Strategy for Vehicle Navigation Based on User Habits

et al. 2018

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Vehicle navigation is widely used in path planning of self driving travel, and it plays an increasing important role in people's daily trips. Therefore, path planning algorithms have attracted substantial attention. However, most path planning methods are based on public data, aiming at different driver groups rather than a specific user. Hence, this study proposes a personalized path decision algorithm that is based on user habits. First, the categories of driving characteristics are obtained through the investigation of public users, and the clustering results corresponding to the category space are obtained by log fuzzy C-means clustering algorithm (LFCM) based on the driving information contained in the log trajectories. Then, the road performance personalized quantization algorithm evaluation is proposed to evaluate roads from the user's field of vision. Finally, adaptive ant colony algorithm is improved and used to validate the path planning based on the road performance personalized values. Results show that the algorithm can meet the personalized requirements of the user path selection in the path decision.

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Section: Establishment Of Driving Style Spacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Path Planning Strategy for Vehicle Navigation Based on User Habits

et al. 2018

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“…Zhao et al propose an offensive strategy based on a virtual tangent circle for robotic fish competition [7]; Gao et al propose a method for path planning of robotic fish balls based on fuzzy logic and geometry [8]; Xie et al propose fuzzy control based steering control algorithm [9]. Guo et al propose several path finding and path planning methods for smart agents, which consider the criterion of 'faster' or 'arriving on tome' in the route optimization [10][11][12][13][14]. In terms of robustness research, ke haokang also proposed the corresponding stability path planning strategy [15].…”

Section: Introductionmentioning

confidence: 99%

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

et al. 2019

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To achieve fast and accurate adjustment of robotic fish, this paper proposes state prediction model based on the extreme learning machine optimized by particle swarm algorithm. The proposed model can select desirable actions for robotic fish according to precisely predicted states, "adjusting position" or "pushing ball" defined herein. Specifically, the extreme learning machine (ELM) is leveraged to predict the state of robotic fish, from the observations of current surrounding environment. As the outputs in ELM are varying with the randomly initialized parameters, particle swarm optimization (PSO) algorithm further improves the accuracy and robustness of the ELM by optimizing initial parameters. The empirical results on URWPGSim2D simulation platform indicate that the robotic fish tends to carry out appropriate actions using the state prediction model so that we can complete the game efficiently. It proves that the proposed model can make best use of the real-time information of robotic fish and water polo and derive fulfilling action strategy in various scenarios, which meet the requirements of motion control for robotic fish.

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Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

Das

Ghosh

2020

J. Comput. Sci. Technol.

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Finding the ‘faster’ path in vehicle routing

Cited by 9 publications

References 35 publications

Path Planning Strategy for Vehicle Navigation Based on User Habits

Path Planning Strategy for Vehicle Navigation Based on User Habits

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

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