For plug-in hybrid electric vehicle, an optimal energy management strategy can maximize its potential to achieve high efficiency. However, energy management strategy without condition information cannot achieve optimal fuel economy in real-time. In order to obtain higher efficiency and adapt to unexpected situation, we develop an energy management strategy based on velocity prediction using digital map information. The detailed model of the hybrid powertrain system such as engine, battery pack and vehicle model are established. The typical driving cycles are constructed to minimize the fuel consumption with equivalent consumption minimization strategy. To adapt to sudden congestions, a realtime strategy based on velocity prediction is proposed. Results indicates that equivalent consumption minimization strategy with velocity prediction is more efficient than the traditional equivalent consumption minimization strategy.
{"title":"Energy management strategy based on velocity prediction for parallel plug-in hybrid electric bus","authors":"P. Dong, Sihao Wu, Fusheng Wang, Yinshu Wang, X. Xu, Shuhan Wang, Yanfang Liu, Wei Guo","doi":"10.1109/CVCI51460.2020.9338649","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338649","url":null,"abstract":"For plug-in hybrid electric vehicle, an optimal energy management strategy can maximize its potential to achieve high efficiency. However, energy management strategy without condition information cannot achieve optimal fuel economy in real-time. In order to obtain higher efficiency and adapt to unexpected situation, we develop an energy management strategy based on velocity prediction using digital map information. The detailed model of the hybrid powertrain system such as engine, battery pack and vehicle model are established. The typical driving cycles are constructed to minimize the fuel consumption with equivalent consumption minimization strategy. To adapt to sudden congestions, a realtime strategy based on velocity prediction is proposed. Results indicates that equivalent consumption minimization strategy with velocity prediction is more efficient than the traditional equivalent consumption minimization strategy.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114672485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338473
Cong Liu, Hui Liu, Lijin Han, C. Xiang, Bin Xu
In order to improve the accuracy of trajectory tracking and handling stability for high-speed autonomous vehicle in extreme conditions, a novel trajectory tracking layered coordinated control strategy based on future driving state prediction for autonomous four-wheel independent drive electric vehicle (A-4WID-EV) is proposed, For the upper controller, a driving state prediction algorithm based on the variable-order Markov model with dynamic window is proposed to predict the driving state in the future. For the lower controller, an active front wheel angle control strategy based on multi-scale model predictive control (MPC) is designed to provide vehicle front wheel angle. Meanwhile, a coordinated four-wheel drive torque control strategy based on the future driving state is proposed to ensure the lateral stability during the trajectory tracking. Finally, through the CarSim-Matlab/Simulink co-simulations, the results show that the proposed controller can effectively improve accuracy trajectory tracking and lateral stability of highspeed A-4WID-EV in extreme conditions.
{"title":"A Layered Coordinated Trajectory Tracking for High- Speed A-4WID-EV in Extreme Conditions","authors":"Cong Liu, Hui Liu, Lijin Han, C. Xiang, Bin Xu","doi":"10.1109/CVCI51460.2020.9338473","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338473","url":null,"abstract":"In order to improve the accuracy of trajectory tracking and handling stability for high-speed autonomous vehicle in extreme conditions, a novel trajectory tracking layered coordinated control strategy based on future driving state prediction for autonomous four-wheel independent drive electric vehicle (A-4WID-EV) is proposed, For the upper controller, a driving state prediction algorithm based on the variable-order Markov model with dynamic window is proposed to predict the driving state in the future. For the lower controller, an active front wheel angle control strategy based on multi-scale model predictive control (MPC) is designed to provide vehicle front wheel angle. Meanwhile, a coordinated four-wheel drive torque control strategy based on the future driving state is proposed to ensure the lateral stability during the trajectory tracking. Finally, through the CarSim-Matlab/Simulink co-simulations, the results show that the proposed controller can effectively improve accuracy trajectory tracking and lateral stability of highspeed A-4WID-EV in extreme conditions.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115063544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338669
Bingwei Xu, Tao Wu
Image-based autonomous driving control is one of the important research directions in the field of autonomous driving. Most of the existing image-based control algorithms use end-to-end mapping from image to vehicle control amount, which is not explanatory enough, and the control amount is not intuitive enough to effectively implement human-machine collaborative control and incremental learning of models. This paper proposes an incremental learning algorithm for driving vehicle control based on fast pursuit point estimation. We establish a model to calculate the mapping of image to the pursuit point, and then get the actual control amount of the vehicle throttle value and front-wheel rotation angle value by the pursuit point. Combining the features of pursuit point which can be observed intuitively and has obvious physical meaning, we propose an incremental model updating method based on man-machine collaborative control, which can incrementally improve the model performance in the actual driving process of vehicles. Finally, the experiment of automatic control is carried out on the Carla simulation platform. The experimental results show that the algorithm can incrementally improve the performance of the automatic control model, with the average calculation speed over 50fps. The autonomous driving system realizes automatic cruise in the real campus environment.
{"title":"Incremental Automatic Vehicle Control Algorithm Based on Fast Pursuit Point Estimation","authors":"Bingwei Xu, Tao Wu","doi":"10.1109/CVCI51460.2020.9338669","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338669","url":null,"abstract":"Image-based autonomous driving control is one of the important research directions in the field of autonomous driving. Most of the existing image-based control algorithms use end-to-end mapping from image to vehicle control amount, which is not explanatory enough, and the control amount is not intuitive enough to effectively implement human-machine collaborative control and incremental learning of models. This paper proposes an incremental learning algorithm for driving vehicle control based on fast pursuit point estimation. We establish a model to calculate the mapping of image to the pursuit point, and then get the actual control amount of the vehicle throttle value and front-wheel rotation angle value by the pursuit point. Combining the features of pursuit point which can be observed intuitively and has obvious physical meaning, we propose an incremental model updating method based on man-machine collaborative control, which can incrementally improve the model performance in the actual driving process of vehicles. Finally, the experiment of automatic control is carried out on the Carla simulation platform. The experimental results show that the algorithm can incrementally improve the performance of the automatic control model, with the average calculation speed over 50fps. The autonomous driving system realizes automatic cruise in the real campus environment.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128396485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338624
Yu Tang, Hai-Lin Xiu, Hong Shu
Automated vehicle testing and evaluation is an important guarantee for vehicle safety and reliability. The current L3 automated vehicle evaluation and evaluation procedures are not yet perfect. For the field test of L3 automated vehicles, we proposed to establish a comprehensive evaluation index system from the five dimensions of safety, intelligence, experience, energy consumption, and efficiency. A scientific method was designed to select and screen indicators in each dimension, and to preprocess behavior indicators based on effect size. The analytical hierarchy process and entropy method were used to determine the index weight, and the BP neural network and grey relation analysis were used to establish two comprehensive evaluation models for automated vehicles. Taking the comprehensive evaluation of the safety of automated vehicles in highway conditions as an example, two comprehensive evaluation models were established to verify the effectiveness of the models.
{"title":"Study on Comprehensive Evaluation of L3 Automated Vehicles","authors":"Yu Tang, Hai-Lin Xiu, Hong Shu","doi":"10.1109/CVCI51460.2020.9338624","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338624","url":null,"abstract":"Automated vehicle testing and evaluation is an important guarantee for vehicle safety and reliability. The current L3 automated vehicle evaluation and evaluation procedures are not yet perfect. For the field test of L3 automated vehicles, we proposed to establish a comprehensive evaluation index system from the five dimensions of safety, intelligence, experience, energy consumption, and efficiency. A scientific method was designed to select and screen indicators in each dimension, and to preprocess behavior indicators based on effect size. The analytical hierarchy process and entropy method were used to determine the index weight, and the BP neural network and grey relation analysis were used to establish two comprehensive evaluation models for automated vehicles. Taking the comprehensive evaluation of the safety of automated vehicles in highway conditions as an example, two comprehensive evaluation models were established to verify the effectiveness of the models.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133585838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338665
Guo Baicang, Jin Lisheng, Shi Jian, Zhang Shunran
The non-driving related operation behavior in driving process has a significant impact on road traffic status and driving safety, but there is less systematic study on the main characteristics and influence mechanism of such behaviors. Aiming at this problem, four types of typical behaviors of normal and abnormal driving are monitored and recorded by real vehicle test. The cognitive distracted driving behavior is taken as the research object, and the influence mechanism and prediction method of distracted driving are studied by using the driver's physiological state and vehicle running state. This paper focuses on the changes and statistical characteristics of driver's physiological state parameters and vehicle running state parameters during distracted driving, and then explores the influence mechanism of different types of distracted driving tasks with different loads on driver's state. This paper analyzes the influence mechanism from two aspects of human and vehicle. Based on the comparison of behavior criterion and load criterion, the parameter system of cognitive distracted driving behavior considering driving load is obtained after cross analysis. The prediction model is established as the training sample of LSTM model, and the model is tested with the data collected from real vehicle test After 100000 iterations, the training accuracy is 90.2% on the training set and 74% on the test set. The results showed that the cross-comparison method is scientific and reasonable, and the prediction model of distracted driving behavior based on physiological state and vehicle running state has good accuracy.
{"title":"A risky prediction model of driving behaviors: especially for cognitive distracted driving behaviors","authors":"Guo Baicang, Jin Lisheng, Shi Jian, Zhang Shunran","doi":"10.1109/CVCI51460.2020.9338665","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338665","url":null,"abstract":"The non-driving related operation behavior in driving process has a significant impact on road traffic status and driving safety, but there is less systematic study on the main characteristics and influence mechanism of such behaviors. Aiming at this problem, four types of typical behaviors of normal and abnormal driving are monitored and recorded by real vehicle test. The cognitive distracted driving behavior is taken as the research object, and the influence mechanism and prediction method of distracted driving are studied by using the driver's physiological state and vehicle running state. This paper focuses on the changes and statistical characteristics of driver's physiological state parameters and vehicle running state parameters during distracted driving, and then explores the influence mechanism of different types of distracted driving tasks with different loads on driver's state. This paper analyzes the influence mechanism from two aspects of human and vehicle. Based on the comparison of behavior criterion and load criterion, the parameter system of cognitive distracted driving behavior considering driving load is obtained after cross analysis. The prediction model is established as the training sample of LSTM model, and the model is tested with the data collected from real vehicle test After 100000 iterations, the training accuracy is 90.2% on the training set and 74% on the test set. The results showed that the cross-comparison method is scientific and reasonable, and the prediction model of distracted driving behavior based on physiological state and vehicle running state has good accuracy.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125240264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper numerically compares the existing capillary pressure correlations of gas diffusion layer with a three-dimensional PEMFC model. The cell performance and liquid water distributions are calculated with different pc-s correlations under the same conditions. The results indicate that the applicability of these correlations are not consistent. Polynomials with higher orders predict the polarization curve better. Exponential correlations tends to overestimate the capillary pressure and liquid water saturation. Therefore, the uniformity of oxygen concentration and fuel cell performance are underestimated.
{"title":"A comparative study on capillary pressure correlations of water transport in PEMFC gas diffusion layer","authors":"Yujie Ding, Liangfei Xu, Yangbin Shao, Zunyan Hu, Jianqiu Li, Tong Shen, M. Ouyang","doi":"10.1109/CVCI51460.2020.9338586","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338586","url":null,"abstract":"This paper numerically compares the existing capillary pressure correlations of gas diffusion layer with a three-dimensional PEMFC model. The cell performance and liquid water distributions are calculated with different pc-s correlations under the same conditions. The results indicate that the applicability of these correlations are not consistent. Polynomials with higher orders predict the polarization curve better. Exponential correlations tends to overestimate the capillary pressure and liquid water saturation. Therefore, the uniformity of oxygen concentration and fuel cell performance are underestimated.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130222656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338584
Jie Xu, Xiaofei Pei, Kexuan Lv
In recent years, machine learning is widely used in many fields. Compared with the rule-based method, machine learning plays a more excellent role in the decision-making of the autonomous vehicle. Some complex situations are often met in our daily life. To this end, Safe reinforcement learning(RL) is introduced to ensure that safer actions are selected. Constant Turn Rate and Acceleration(CTRA) model is first used to predict the future trajectories of surrounding vehicles. Then Double Deep Q-Learning(DDQN) method is used to make decisions and ensure the autonomous vehicle can move at the desired speed as much as possible. In order to achieve a safer decision-making, some safety rules are introduced. Finally, the algorithm is demonstrated in Simulation of Urban Mobility(SUMO) and has been proved to have an outstanding performance on such a complex scenario.
{"title":"Decision-Making for Complex Scenario using Safe Reinforcement Learning","authors":"Jie Xu, Xiaofei Pei, Kexuan Lv","doi":"10.1109/CVCI51460.2020.9338584","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338584","url":null,"abstract":"In recent years, machine learning is widely used in many fields. Compared with the rule-based method, machine learning plays a more excellent role in the decision-making of the autonomous vehicle. Some complex situations are often met in our daily life. To this end, Safe reinforcement learning(RL) is introduced to ensure that safer actions are selected. Constant Turn Rate and Acceleration(CTRA) model is first used to predict the future trajectories of surrounding vehicles. Then Double Deep Q-Learning(DDQN) method is used to make decisions and ensure the autonomous vehicle can move at the desired speed as much as possible. In order to achieve a safer decision-making, some safety rules are introduced. Finally, the algorithm is demonstrated in Simulation of Urban Mobility(SUMO) and has been proved to have an outstanding performance on such a complex scenario.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130320301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a novel observer-based sliding mode control (SMC) to enhance the performance of autonomous vehicles (AVs) rollover behavior under various road profile input. The model of half-car system is first established to describe the AVs rollover behavior by considering nonlinear dynamics of tire force and controllable suspension force under various movement conditions. Moreover, an unscented Kalman Filter (UKF) algorithm is proposed to identify the sprung mass. Combing with the interacting multiple model (IMM) approach and Markov Chain Monte Carlo (MCMC) theory, a novel interacting multiple model unscented Kalman Filters (IMMUKF) observer based is developed to estimate the movement state of AVs system. Then, an adaptive observer-based sliding mode control (AOSMC) strategy is proposed to constrain the AVs roll performance under the various external input. The stability of the proposed algorithm is proved by using Lyapunov function. Finally, simulations and validations are performed on a high-fidelity CarSim® software by using J-turn scenario under various road excitation, to validate the proposed algorithm for AVs system, and the results illustrate that the improved roll states are more than 15% compared with the traditional SMC algorithm.
{"title":"Observer-based Adaptive Sliding Mode Control of Autonomous Vehicle Rollover Behavior Combing with Markovian Switching","authors":"Zhenfeng Wang, Fei Li, Lixin Jing, Yechen Qin, Yiwei Huang","doi":"10.1109/CVCI51460.2020.9338593","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338593","url":null,"abstract":"This paper proposes a novel observer-based sliding mode control (SMC) to enhance the performance of autonomous vehicles (AVs) rollover behavior under various road profile input. The model of half-car system is first established to describe the AVs rollover behavior by considering nonlinear dynamics of tire force and controllable suspension force under various movement conditions. Moreover, an unscented Kalman Filter (UKF) algorithm is proposed to identify the sprung mass. Combing with the interacting multiple model (IMM) approach and Markov Chain Monte Carlo (MCMC) theory, a novel interacting multiple model unscented Kalman Filters (IMMUKF) observer based is developed to estimate the movement state of AVs system. Then, an adaptive observer-based sliding mode control (AOSMC) strategy is proposed to constrain the AVs roll performance under the various external input. The stability of the proposed algorithm is proved by using Lyapunov function. Finally, simulations and validations are performed on a high-fidelity CarSim® software by using J-turn scenario under various road excitation, to validate the proposed algorithm for AVs system, and the results illustrate that the improved roll states are more than 15% compared with the traditional SMC algorithm.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127436433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338500
Jiaxin Zhang, Yong-ming Li
In this paper, an adaptive fuzzy control scheme is proposed for one-quarter automotive active suspension system with full sate constraints and stochastic disturbance. In the considered active suspension system, to further improve the driving security and comfort, the problems of stochastic perturbation and full state constraints are considered simultaneously. In the framework of backstepping, the barrier Lyapunov function is proposed to constrain full state variables. Consequently, by combing the Itô differential formula and stochastic control theory, an adaptive controller is designed to adopt the uneven pavement surface. Ultimately, on the basis of Lyapunov stability theory, it proves that the designed controller not only can constrain the bodywork, the displacement of tires, the current of the electromagnetic actuator, the speeds of the car body and the tires within boundaries, but also can eliminate the stochastic disturbance.
{"title":"Adaptive Fuzzy Control for Active Suspension Systems with Stochastic Disturbance and Full State Constraints*","authors":"Jiaxin Zhang, Yong-ming Li","doi":"10.1109/CVCI51460.2020.9338500","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338500","url":null,"abstract":"In this paper, an adaptive fuzzy control scheme is proposed for one-quarter automotive active suspension system with full sate constraints and stochastic disturbance. In the considered active suspension system, to further improve the driving security and comfort, the problems of stochastic perturbation and full state constraints are considered simultaneously. In the framework of backstepping, the barrier Lyapunov function is proposed to constrain full state variables. Consequently, by combing the Itô differential formula and stochastic control theory, an adaptive controller is designed to adopt the uneven pavement surface. Ultimately, on the basis of Lyapunov stability theory, it proves that the designed controller not only can constrain the bodywork, the displacement of tires, the current of the electromagnetic actuator, the speeds of the car body and the tires within boundaries, but also can eliminate the stochastic disturbance.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129107921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-18DOI: 10.1109/CVCI51460.2020.9338469
Li Yan, Lixin Qi, Kan Feiran, Chen Guang, Chen Xinbo
This paper proposes an improved global path planning algorithm to generate the optimal global path that satisfies the kinematic constraints of parking robots. The estimation function is improved through BP neural network, which improves the planning efficiency of finding the shortest path. Improve the drivability of the planned route by setting up the prohibited area and the route backtracking. A simulation platform is built based on ROS, and the path planning effect of the traditional A* algorithm is compared with the effect of the improved global path planning algorithm. The results show that the improved algorithm has a shorter path length and better drivability. The overall deviation of the simulated trajectory driving along this path is small. The improved algorithm is used to conduct multiple terminal path planning experiments. The results show that the total length of the path generated by the algorithm is close to the global optimum, the path is smooth and easy to track.
{"title":"A Study of Improved Global Path Planning Algorithm for Parking Robot Based on ROS","authors":"Li Yan, Lixin Qi, Kan Feiran, Chen Guang, Chen Xinbo","doi":"10.1109/CVCI51460.2020.9338469","DOIUrl":"https://doi.org/10.1109/CVCI51460.2020.9338469","url":null,"abstract":"This paper proposes an improved global path planning algorithm to generate the optimal global path that satisfies the kinematic constraints of parking robots. The estimation function is improved through BP neural network, which improves the planning efficiency of finding the shortest path. Improve the drivability of the planned route by setting up the prohibited area and the route backtracking. A simulation platform is built based on ROS, and the path planning effect of the traditional A* algorithm is compared with the effect of the improved global path planning algorithm. The results show that the improved algorithm has a shorter path length and better drivability. The overall deviation of the simulated trajectory driving along this path is small. The improved algorithm is used to conduct multiple terminal path planning experiments. The results show that the total length of the path generated by the algorithm is close to the global optimum, the path is smooth and easy to track.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129172534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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