{"title":"基于 PSO-LSTM 预测的高能效曲线拼接轨迹规划","authors":"Jian Wang , Zhongxing Li , Chaofeng Pan","doi":"10.1016/j.conengprac.2024.106009","DOIUrl":null,"url":null,"abstract":"<div><p>Energy-efficient trajectory planning aims to optimize the economic performance for autonomous vehicles on the premise of ensuring driving safety, which excavate the energy saving potential and further improve the driving mileage. In this research, a curve splicing energy-efficient trajectory planning method based on surrounding vehicles trajectory prediction is presented. The long short-term memory (LSTM) neural network is adopted to construct the trajectory prediction model, and the hyperparameters of the LSTM are optimized by particle swarm optimization (PSO). To make the energy-efficient decision, the energy-efficient estimation model with motor MAP is developed by the correlation between vehicle driving energy consumption and motor efficiency, and the energy-efficient decision function was designed based on the average efficiency of behavior switching and the target behavior efficiency. Furthermore, a trajectory planning method with hierarchical planning of guide line and vehicle speed is presented based on B-spline curve and rolling dynamic programming (RDP). Via the traversal test, the dynamic adjustment of the guide line structure parameters is realized, and the RDP speed optimization objective function is designed with the goal of energy-efficiency. To precisely and rapidly control the EVs to track the reference trajectory, a model predictive control (MPC) with the goal of traceability was proposed. Eventually, the effectiveness of the energy-efficient trajectory planning algorithm is verified in the urban and the expressway condition respectively. The results show that the energy-efficient performance of the algorithm application is obvious in the expressway condition, and the average energy consumption improving rate is 11.11%.</p></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy-efficient trajectory planning with curve splicing based on PSO-LSTM prediction\",\"authors\":\"Jian Wang , Zhongxing Li , Chaofeng Pan\",\"doi\":\"10.1016/j.conengprac.2024.106009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Energy-efficient trajectory planning aims to optimize the economic performance for autonomous vehicles on the premise of ensuring driving safety, which excavate the energy saving potential and further improve the driving mileage. In this research, a curve splicing energy-efficient trajectory planning method based on surrounding vehicles trajectory prediction is presented. The long short-term memory (LSTM) neural network is adopted to construct the trajectory prediction model, and the hyperparameters of the LSTM are optimized by particle swarm optimization (PSO). To make the energy-efficient decision, the energy-efficient estimation model with motor MAP is developed by the correlation between vehicle driving energy consumption and motor efficiency, and the energy-efficient decision function was designed based on the average efficiency of behavior switching and the target behavior efficiency. Furthermore, a trajectory planning method with hierarchical planning of guide line and vehicle speed is presented based on B-spline curve and rolling dynamic programming (RDP). Via the traversal test, the dynamic adjustment of the guide line structure parameters is realized, and the RDP speed optimization objective function is designed with the goal of energy-efficiency. To precisely and rapidly control the EVs to track the reference trajectory, a model predictive control (MPC) with the goal of traceability was proposed. Eventually, the effectiveness of the energy-efficient trajectory planning algorithm is verified in the urban and the expressway condition respectively. The results show that the energy-efficient performance of the algorithm application is obvious in the expressway condition, and the average energy consumption improving rate is 11.11%.</p></div>\",\"PeriodicalId\":50615,\"journal\":{\"name\":\"Control Engineering Practice\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering Practice\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0967066124001692\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066124001692","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Energy-efficient trajectory planning with curve splicing based on PSO-LSTM prediction
Energy-efficient trajectory planning aims to optimize the economic performance for autonomous vehicles on the premise of ensuring driving safety, which excavate the energy saving potential and further improve the driving mileage. In this research, a curve splicing energy-efficient trajectory planning method based on surrounding vehicles trajectory prediction is presented. The long short-term memory (LSTM) neural network is adopted to construct the trajectory prediction model, and the hyperparameters of the LSTM are optimized by particle swarm optimization (PSO). To make the energy-efficient decision, the energy-efficient estimation model with motor MAP is developed by the correlation between vehicle driving energy consumption and motor efficiency, and the energy-efficient decision function was designed based on the average efficiency of behavior switching and the target behavior efficiency. Furthermore, a trajectory planning method with hierarchical planning of guide line and vehicle speed is presented based on B-spline curve and rolling dynamic programming (RDP). Via the traversal test, the dynamic adjustment of the guide line structure parameters is realized, and the RDP speed optimization objective function is designed with the goal of energy-efficiency. To precisely and rapidly control the EVs to track the reference trajectory, a model predictive control (MPC) with the goal of traceability was proposed. Eventually, the effectiveness of the energy-efficient trajectory planning algorithm is verified in the urban and the expressway condition respectively. The results show that the energy-efficient performance of the algorithm application is obvious in the expressway condition, and the average energy consumption improving rate is 11.11%.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.