Pub Date : 2024-03-01DOI: 10.26599/JICV.2023.9210033
Lingshu Zhong;Ho Sheau En;Mingyang Pei;Jingwen Xiong;Tao Wang
As the adoption of Electric Vehicles (EVs) intensifies, two primary challenges emerge: limited range due to battery constraints and extended charging times. The traditional charging stations, particularly those near highways, exacerbate these issues with necessary detours, inconsistent service levels, and unpredictable waiting durations. The emerging technology of dynamic wireless charging lanes (DWCLs) may alleviate range anxiety and eliminate long charging stops; however, the driving speed on DWCL significantly affects charging efficiency and effective charging time. Meanwhile, the existing research has addressed load balancing optimization on Dynamic Wireless Charging (DWC) systems to a limited extent. To address this critical issue, this study introduces an innovative eco-driving speed control strategy, providing a novel solution to the multi-objective optimization problem of speed control on DWCL. We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs. Three objective functions are formulated to tackle the challenges at hand: reducing travel time, increasing charging efficiency, and achieving load balancing on DWCL, which corresponds to four control strategies. The results of numerical tests indicate that a comprehensive control strategy, which considers all objectives, achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing. Furthermore, by defining the energy demand and speed range through an upper operation limit, a relatively superior speed control strategy can be selected. This work contributes to the discourse on DWCL integration into modern transportation systems, enhancing the EV driving experience on major roads.
{"title":"Optimized Speed Control for Electric Vehicles on Dynamic Wireless Charging Lanes: An Eco-Driving Approach","authors":"Lingshu Zhong;Ho Sheau En;Mingyang Pei;Jingwen Xiong;Tao Wang","doi":"10.26599/JICV.2023.9210033","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210033","url":null,"abstract":"As the adoption of Electric Vehicles (EVs) intensifies, two primary challenges emerge: limited range due to battery constraints and extended charging times. The traditional charging stations, particularly those near highways, exacerbate these issues with necessary detours, inconsistent service levels, and unpredictable waiting durations. The emerging technology of dynamic wireless charging lanes (DWCLs) may alleviate range anxiety and eliminate long charging stops; however, the driving speed on DWCL significantly affects charging efficiency and effective charging time. Meanwhile, the existing research has addressed load balancing optimization on Dynamic Wireless Charging (DWC) systems to a limited extent. To address this critical issue, this study introduces an innovative eco-driving speed control strategy, providing a novel solution to the multi-objective optimization problem of speed control on DWCL. We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs. Three objective functions are formulated to tackle the challenges at hand: reducing travel time, increasing charging efficiency, and achieving load balancing on DWCL, which corresponds to four control strategies. The results of numerical tests indicate that a comprehensive control strategy, which considers all objectives, achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing. Furthermore, by defining the energy demand and speed range through an upper operation limit, a relatively superior speed control strategy can be selected. This work contributes to the discourse on DWCL integration into modern transportation systems, enhancing the EV driving experience on major roads.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 1","pages":"52-63"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10499223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.26599/JICV.2023.9210032
Jia Shi;Yugong Luo;Pengfei Li;Jiawei Wang;Keqiang Li
The on-ramp merging in multi-lane highway scenarios presents challenges due to the complexity of coordinating vehicles' merging and lane-changing behaviors, while ensuring safety and optimizing traffic flow. However, there are few studies that have addressed the merging problem of ramp vehicles and the cooperative lane-change problem of mainline vehicles within a unified framework and proposed corresponding optimization strategies. To tackle this issue, this study adopts a cyber-physical integration perspective and proposes a graph-based solution approach. First, the information of vehicle groups in the physical plane is mapped to the cyber plane, and a dynamic conflict graph is introduced in the cyber space to describe the conflict relationships among vehicle groups. Subsequently, graph decomposition and search strategies are employed to obtain the optimal solution, including the set of mainline vehicles changing lanes, passing sequences for each route, and corresponding trajectories. Finally, the proposed dynamic conflict graph-based algorithm is validated through simulations in continuous traffic with various densities, and its performance is compared with the default algorithm in SUMO. The results demonstrate the effectiveness of the proposed approach in improving vehicle safety and traffic efficiency, particularly in high traffic density scenarios, providing valuable insights for future research in multi-lane merging strategies.
{"title":"Collaborative Multi-Lane on-Ramp Merging Strategy for Connected and Automated Vehicles Using Dynamic Conflict Graph","authors":"Jia Shi;Yugong Luo;Pengfei Li;Jiawei Wang;Keqiang Li","doi":"10.26599/JICV.2023.9210032","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210032","url":null,"abstract":"The on-ramp merging in multi-lane highway scenarios presents challenges due to the complexity of coordinating vehicles' merging and lane-changing behaviors, while ensuring safety and optimizing traffic flow. However, there are few studies that have addressed the merging problem of ramp vehicles and the cooperative lane-change problem of mainline vehicles within a unified framework and proposed corresponding optimization strategies. To tackle this issue, this study adopts a cyber-physical integration perspective and proposes a graph-based solution approach. First, the information of vehicle groups in the physical plane is mapped to the cyber plane, and a dynamic conflict graph is introduced in the cyber space to describe the conflict relationships among vehicle groups. Subsequently, graph decomposition and search strategies are employed to obtain the optimal solution, including the set of mainline vehicles changing lanes, passing sequences for each route, and corresponding trajectories. Finally, the proposed dynamic conflict graph-based algorithm is validated through simulations in continuous traffic with various densities, and its performance is compared with the default algorithm in SUMO. The results demonstrate the effectiveness of the proposed approach in improving vehicle safety and traffic efficiency, particularly in high traffic density scenarios, providing valuable insights for future research in multi-lane merging strategies.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 1","pages":"38-51"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10499221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the proliferation and development of automated container terminal continue, the issues of efficiency and safety become increasingly significant. The container yard is one of the most crucial cargo distribution centers in a terminal. Automated Guided Vehicles (AGVs) that carry materials of varying hazard levels through these yards without compromising on the safe transportation of hazardous materials, while also maximizing efficiency, is a complex challenge. This research introduces an algorithm that integrates Long Short-Term Memory (LSTM) neural network with reinforcement learning techniques, specifically Deep Q-Network (DQN), for routing an AGV carrying hazardous materials within a container yard. The objective is to ensure that the AGV carrying hazardous materials efficiently reaches its destination while effectively avoiding AGVs carrying non-hazardous materials. Utilizing real data from the Meishan Port in Ningbo, Zhejiang, China, the actual yard is first abstracted into an undirected graph. Since LSTM neural network can efficiently conveys and represents information in long time sequences and do not causes useful information before long time to be ignored, a two-layer LSTM neural network with 64 neurons per layer was constructed for predicting the motion trajectory of AGVs carrying non-hazardous materials, which are incorporated into the map as background AGVs. Subsequently, DQN is employed to plan the route for an AGV transporting hazardous materials, aiming to reach its destination swiftly while avoiding encounters with other AGVs. Experimental tests have shown that the route planning algorithm proposed in this study improves the level of avoidance of hazardous material AGV in relation to non-hazardous material AGVs. Compared to the method where hazardous material AGV follow the shortest path to their destination, the avoidance efficiency was enhanced by 3.11%. This improvement demonstrates potential strategies for balancing efficiency and safety in automated terminals. Additionally, it provides insights for designing avoidance schemes for autonomous driving AGVs, offering solutions for complex operational environments where safety and efficient navigation are paramount.
{"title":"Enhancing Safety and Efficiency in Automated Container Terminals: Route Planning for Hazardous Material AGV Using LSTM Neural Network and Deep Q-Network","authors":"Fei Li;Junchi Cheng;Zhiqi Mao;Yuhao Wang;Pingfa Feng","doi":"10.26599/JICV.2023.9210041","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210041","url":null,"abstract":"As the proliferation and development of automated container terminal continue, the issues of efficiency and safety become increasingly significant. The container yard is one of the most crucial cargo distribution centers in a terminal. Automated Guided Vehicles (AGVs) that carry materials of varying hazard levels through these yards without compromising on the safe transportation of hazardous materials, while also maximizing efficiency, is a complex challenge. This research introduces an algorithm that integrates Long Short-Term Memory (LSTM) neural network with reinforcement learning techniques, specifically Deep Q-Network (DQN), for routing an AGV carrying hazardous materials within a container yard. The objective is to ensure that the AGV carrying hazardous materials efficiently reaches its destination while effectively avoiding AGVs carrying non-hazardous materials. Utilizing real data from the Meishan Port in Ningbo, Zhejiang, China, the actual yard is first abstracted into an undirected graph. Since LSTM neural network can efficiently conveys and represents information in long time sequences and do not causes useful information before long time to be ignored, a two-layer LSTM neural network with 64 neurons per layer was constructed for predicting the motion trajectory of AGVs carrying non-hazardous materials, which are incorporated into the map as background AGVs. Subsequently, DQN is employed to plan the route for an AGV transporting hazardous materials, aiming to reach its destination swiftly while avoiding encounters with other AGVs. Experimental tests have shown that the route planning algorithm proposed in this study improves the level of avoidance of hazardous material AGV in relation to non-hazardous material AGVs. Compared to the method where hazardous material AGV follow the shortest path to their destination, the avoidance efficiency was enhanced by 3.11%. This improvement demonstrates potential strategies for balancing efficiency and safety in automated terminals. Additionally, it provides insights for designing avoidance schemes for autonomous driving AGVs, offering solutions for complex operational environments where safety and efficient navigation are paramount.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 1","pages":"64-77"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10520170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aims to propose a decision-making method based on artificial potential fields (APFs) and finite state machines (FSMs) in emergency conditions. This study presents a decision-making method based on APFs and FSMs for emergency conditions. By modeling the longitudinal and lateral potential energy fields of the vehicle, the driving state is identified, and the trigger conditions are provided for path planning during lane changing. In addition, this study also designed the state transition rules based on the longitudinal and lateral virtual forces. It established the vehicle decision-making model based on the finite state machine to ensure driving safety in emergency situations. To illustrate the performance of the decision-making model by considering APFs and finite state machines. The version of the model in the co-simulation platform of MATLAB and CarSim shows that the developed decision model in this study accurately generates driving behaviors of the vehicle at different time intervals. The contributions of this study are two-fold. A hierarchical vehicle state machine decision model is proposed to enhance driving safety in emergency scenarios. Mathematical models for determining the transition thresholds of lateral and longitudinal vehicle states are established based on the vehicle potential field model, leading to the formulation of transition rules between different states of autonomous vehicles (AVs).
{"title":"Intelligent Decision-Making Method for Vehicles in Emergency Conditions Based on Artificial Potential Fields and Finite State Machines","authors":"Xunjia Zheng;Huilan Li;Qiang Zhang;Yonggang Liu;Xing Chen;Hui Liu;Tianhong Luo;Jianjie Gao;Lihong Xia","doi":"10.26599/JICV.2023.9210025","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210025","url":null,"abstract":"This study aims to propose a decision-making method based on artificial potential fields (APFs) and finite state machines (FSMs) in emergency conditions. This study presents a decision-making method based on APFs and FSMs for emergency conditions. By modeling the longitudinal and lateral potential energy fields of the vehicle, the driving state is identified, and the trigger conditions are provided for path planning during lane changing. In addition, this study also designed the state transition rules based on the longitudinal and lateral virtual forces. It established the vehicle decision-making model based on the finite state machine to ensure driving safety in emergency situations. To illustrate the performance of the decision-making model by considering APFs and finite state machines. The version of the model in the co-simulation platform of MATLAB and CarSim shows that the developed decision model in this study accurately generates driving behaviors of the vehicle at different time intervals. The contributions of this study are two-fold. A hierarchical vehicle state machine decision model is proposed to enhance driving safety in emergency scenarios. Mathematical models for determining the transition thresholds of lateral and longitudinal vehicle states are established based on the vehicle potential field model, leading to the formulation of transition rules between different states of autonomous vehicles (AVs).","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 1","pages":"19-29"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10506788","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.26599/JICV.2023.9210029
Chunlei Zheng;Yiping Yan;Yang Liu
Throughout much of human history, the vast majority of people lived in small communities. However, in the last few centuries, and particularly in recent decades, there has been a dramatic shift. A massive migration has moved populations from rural to urban areas. United Nations reports state that over 4.3 billion individuals now inhabit urban regions, which accounts for more than half (55% as of 2017) of the global population. In most high-income nations, including Western Europe, the Americas, Australia, Japan, and the Middle East, over 80% of people live in urban areas. This figure ranges from 50% to 80% in upper-middle-income countries like Eastern Europe, East Asia, North Africa, South Africa, and South America (United Nations, Department of Economic and Social Affairs, Population Division, 2019). The urban population is anticipated to rise across all countries in the coming decades, albeit at different rates. By 2050, the global population is expected to reach approximately 9.8 billion, with about 6.7 billion residing in cities and 3.1 billion in rural areas. Despite this rapid urbanization, only around 1% of the Earth's land is allocated for urban and infrastructure development. While urbanization has spurred socio-economic growth, it has also led to significant challenges such as traffic congestion and air pollution. In China, the swift growth of cities has notably expanded urban areas and extended the commuting times of residents. The “2022 Commuting Monitoring Report of Major Chinese Cities” reveals that in 2022, over 14 million people in 44 major Chinese cities experienced extreme commuting, with upwards of 13% spending over an hour in transit (Baidu Maps, 2023). Beijing recorded the highest rate, where 26% of commuters faced this issue.
{"title":"Prospects of eVTOL and Modular Flying Cars in China Urban Settings","authors":"Chunlei Zheng;Yiping Yan;Yang Liu","doi":"10.26599/JICV.2023.9210029","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210029","url":null,"abstract":"Throughout much of human history, the vast majority of people lived in small communities. However, in the last few centuries, and particularly in recent decades, there has been a dramatic shift. A massive migration has moved populations from rural to urban areas. United Nations reports state that over 4.3 billion individuals now inhabit urban regions, which accounts for more than half (55% as of 2017) of the global population. In most high-income nations, including Western Europe, the Americas, Australia, Japan, and the Middle East, over 80% of people live in urban areas. This figure ranges from 50% to 80% in upper-middle-income countries like Eastern Europe, East Asia, North Africa, South Africa, and South America (United Nations, Department of Economic and Social Affairs, Population Division, 2019). The urban population is anticipated to rise across all countries in the coming decades, albeit at different rates. By 2050, the global population is expected to reach approximately 9.8 billion, with about 6.7 billion residing in cities and 3.1 billion in rural areas. Despite this rapid urbanization, only around 1% of the Earth's land is allocated for urban and infrastructure development. While urbanization has spurred socio-economic growth, it has also led to significant challenges such as traffic congestion and air pollution. In China, the swift growth of cities has notably expanded urban areas and extended the commuting times of residents. The “2022 Commuting Monitoring Report of Major Chinese Cities” reveals that in 2022, over 14 million people in 44 major Chinese cities experienced extreme commuting, with upwards of 13% spending over an hour in transit (Baidu Maps, 2023). Beijing recorded the highest rate, where 26% of commuters faced this issue.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"187-189"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10365716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.26599/JICV.2023.9210010
Changxi Ma;Yuanping Li;Wei Meng
Currently, traffic problems in urban road traffic environments remain severe, traffic pollution and congestion have not been effectively improved, and traffic accidents are still frequent. Traditional traffic signal control methods have little effect on these problems. With the continuous improvement of communication technology and network connections, vehicle speed guidance provides a new idea for solving the above problems and has gradually become a popular topic in academic research. However, its generalization has shortcomings. Therefore, this paper summarizes the research on vehicle speed control strategies in urban road environments and provides suggestions for future research. In this paper, we summarize the existing research in four parts. First, we categorize existing research based on vehicle type. Second, the vehicle speed guidance problem is divided according to the problem research scene. Third, we summarize the existing literature regarding vehicle speed. Finally, we summarize the methods used for speed guidance. Through an analysis of the existing literature, it is concluded that there is a deficiency in the existing research, and suggestions for the future of vehicle speed guidance research are suggested.
{"title":"A Review of Vehicle Speed Control Strategies","authors":"Changxi Ma;Yuanping Li;Wei Meng","doi":"10.26599/JICV.2023.9210010","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210010","url":null,"abstract":"Currently, traffic problems in urban road traffic environments remain severe, traffic pollution and congestion have not been effectively improved, and traffic accidents are still frequent. Traditional traffic signal control methods have little effect on these problems. With the continuous improvement of communication technology and network connections, vehicle speed guidance provides a new idea for solving the above problems and has gradually become a popular topic in academic research. However, its generalization has shortcomings. Therefore, this paper summarizes the research on vehicle speed control strategies in urban road environments and provides suggestions for future research. In this paper, we summarize the existing research in four parts. First, we categorize existing research based on vehicle type. Second, the vehicle speed guidance problem is divided according to the problem research scene. Third, we summarize the existing literature regarding vehicle speed. Finally, we summarize the methods used for speed guidance. Through an analysis of the existing literature, it is concluded that there is a deficiency in the existing research, and suggestions for the future of vehicle speed guidance research are suggested.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"190-201"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.26599/JICV.2023.9210018
Quan Yuan;Haixu Shi;Ashton Tan Yu Xuan;Ming Gao;Qing Xu;Jianqiang Wang
In autonomous driving, target tracking is essential to environmental perception. The study of target tracking algorithms can improve the accuracy of an autonomous driving vehicle's perception, which is of great significance in ensuring the safety of autonomous driving and promoting the landing of technical applications. This study focuses on the fusion tracking algorithm based on visible and infrared images. The proposed approach utilizes a feature-level image fusion method, dividing the tracking process into two components: image fusion and target tracking. An unsupervised network, Visible and Infrared image Fusion Network (VIF-net), is employed for visible and infrared image fusion in the image fusion part. In the target tracking part, Siamese Region Proposal Network (SiamRPN), based on deep learning, tracks the target with fused images. The fusion tracking algorithm is trained and evaluated on the visible infrared image dataset RGBT234. Experimental results demonstrate that the algorithm outperforms training networks solely based on visible images, proving that the fusion of visible and infrared images in the target tracking algorithm can improve the accuracy of the target tracking even if it is like tracking-based visual images. This improvement is also attributed to the algorithm's ability to extract infrared image features, augmenting the target tracking accuracy.
{"title":"Enhanced Target Tracking Algorithm for Autonomous Driving Based on Visible and Infrared Image Fusion","authors":"Quan Yuan;Haixu Shi;Ashton Tan Yu Xuan;Ming Gao;Qing Xu;Jianqiang Wang","doi":"10.26599/JICV.2023.9210018","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210018","url":null,"abstract":"In autonomous driving, target tracking is essential to environmental perception. The study of target tracking algorithms can improve the accuracy of an autonomous driving vehicle's perception, which is of great significance in ensuring the safety of autonomous driving and promoting the landing of technical applications. This study focuses on the fusion tracking algorithm based on visible and infrared images. The proposed approach utilizes a feature-level image fusion method, dividing the tracking process into two components: image fusion and target tracking. An unsupervised network, Visible and Infrared image Fusion Network (VIF-net), is employed for visible and infrared image fusion in the image fusion part. In the target tracking part, Siamese Region Proposal Network (SiamRPN), based on deep learning, tracks the target with fused images. The fusion tracking algorithm is trained and evaluated on the visible infrared image dataset RGBT234. Experimental results demonstrate that the algorithm outperforms training networks solely based on visible images, proving that the fusion of visible and infrared images in the target tracking algorithm can improve the accuracy of the target tracking even if it is like tracking-based visual images. This improvement is also attributed to the algorithm's ability to extract infrared image features, augmenting the target tracking accuracy.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"237-249"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.26599/JICV.2023.9210007
Nalin Kumar Sekar;Vinayak Malaghan;Digvijay S. Pawar
Several past studies showed that Autonomous Vehicles (AVs) can reduce crash risk, stop-and-go traffic, and travel time. To analyze the safety benefits of AVs, most of the researchers proposed algorithms and simulation-based techniques. However, these studies have not assessed the safety benefits of AVs for different vehicle types under heterogeneous conditions. With this opportunity, this study focuses on the benefits of AVs in terms of safety for different penetration rates under heterogeneous conditions. This study considered three driving logics during peak hour conditions to assess the performance of AVs in terms of safety. In VISSIM, default driving behavior models for AVs were adopted to consider cautious and all-knowing driving logic and the third driving logic (Atkins) was modeled in VISSIM using parameters adopted from the previous studies. To this end, using VISSIM, the travel time output results were obtained. Also, using Surrogate Safety Assessment Model (SSAM), conflicts were extracted from output trajectory files (VISSIM). The results suggest that “cautious driving logic” reduced travel time and crash risk significantly when compared to the other two driving logics during peak hour conditions. Furthermore, the statistical analysis clearly demonstrated that “cautious driving logic” differs significantly from the other two driving logics. When Market Penetration Rates (MPR) were 50% or greater, the “cautious driving logic” significantly outperforms the other two driving logics. The results highlight that adopting “cautious driving logic” at an expressway may significantly increase safety at higher AV penetration rates (above 50%).
{"title":"Micro-Simulation Insights into the Safety and Operational Benefits of Autonomous Vehicles","authors":"Nalin Kumar Sekar;Vinayak Malaghan;Digvijay S. Pawar","doi":"10.26599/JICV.2023.9210007","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210007","url":null,"abstract":"Several past studies showed that Autonomous Vehicles (AVs) can reduce crash risk, stop-and-go traffic, and travel time. To analyze the safety benefits of AVs, most of the researchers proposed algorithms and simulation-based techniques. However, these studies have not assessed the safety benefits of AVs for different vehicle types under heterogeneous conditions. With this opportunity, this study focuses on the benefits of AVs in terms of safety for different penetration rates under heterogeneous conditions. This study considered three driving logics during peak hour conditions to assess the performance of AVs in terms of safety. In VISSIM, default driving behavior models for AVs were adopted to consider cautious and all-knowing driving logic and the third driving logic (Atkins) was modeled in VISSIM using parameters adopted from the previous studies. To this end, using VISSIM, the travel time output results were obtained. Also, using Surrogate Safety Assessment Model (SSAM), conflicts were extracted from output trajectory files (VISSIM). The results suggest that “cautious driving logic” reduced travel time and crash risk significantly when compared to the other two driving logics during peak hour conditions. Furthermore, the statistical analysis clearly demonstrated that “cautious driving logic” differs significantly from the other two driving logics. When Market Penetration Rates (MPR) were 50% or greater, the “cautious driving logic” significantly outperforms the other two driving logics. The results highlight that adopting “cautious driving logic” at an expressway may significantly increase safety at higher AV penetration rates (above 50%).","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"202-210"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.26599/JICV.2023.9210015
Sailesh Acharya
With the likely future of autonomous vehicles (AVs) as private, ride-hailing, and pooled vehicles, it is important to consider all forms of AVs when estimating the impacts of automation on travel behavior. To aid this, this study jointly models the public interest in three forms of AVs (owning, ride-hailing, and using pooled services) and compares the interests in owning versus ride-hailing AVs using a combination of structural equation modeling and multivariate ordered probit modeling frameworks. Using the 2019 California Vehicle Survey data, we estimate the impacts of several exogenous and latent variables on all forms of AV adoption. We find that the individual, household, travel-related, and built-environment factors are related to different forms of AV adoption directly and indirectly through attitudes toward human and automated driving. We also report that human and automated driving sentiments have the highest impact on interest in owning an AV compared to interest in ride-hailing and using pooled AVs. We discuss several policy implications by calculating the pseudo-elasticity effects of exogenous variables and the sensitivities of the impacts on latent variables on different forms of AV adoption. For example, public interest in owning private AVs can be increased by more than 7% by making them familiar with autonomous technology.
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Pub Date : 2023-12-01DOI: 10.26599/JICV.2023.9210017
Yuxiang Guo;Itsuo Kumazawa;Chuyo Kaku
The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances. The premise of advanced driver assistance system (ADAS) system for safety surveillance and safe driving is early identification of vehicle targets in front of the ego vehicle. The recognition of the same vehicle at different scales requires feature learning with scale invariance. Unlike existing feature vector methods, the normalized PCA eigenvalues calculated from feature maps are used to extract scale-invariant features. This study proposed a convolutional neural network (CNN) structure embedded with the module of multi-pooling-PCA for scale variant object recognition. The validation of the proposed network structure is verified by scale variant vehicle image dataset. Compared with scale invariant network algorithms of Scale-invariant feature transform (SIFT) and FSAF as well as miscellaneous networks, the proposed network can achieve the best recognition accuracy tested by the vehicle scale variant dataset. To testify the practicality of this modified network, the testing of public dataset ImageNet is done and the comparable results proved its effectiveness in general purpose of applications.
{"title":"Scale Variant Vehicle Object Recognition by CNN Module of Multi-Pooling-PCA Process","authors":"Yuxiang Guo;Itsuo Kumazawa;Chuyo Kaku","doi":"10.26599/JICV.2023.9210017","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210017","url":null,"abstract":"The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances. The premise of advanced driver assistance system (ADAS) system for safety surveillance and safe driving is early identification of vehicle targets in front of the ego vehicle. The recognition of the same vehicle at different scales requires feature learning with scale invariance. Unlike existing feature vector methods, the normalized PCA eigenvalues calculated from feature maps are used to extract scale-invariant features. This study proposed a convolutional neural network (CNN) structure embedded with the module of multi-pooling-PCA for scale variant object recognition. The validation of the proposed network structure is verified by scale variant vehicle image dataset. Compared with scale invariant network algorithms of Scale-invariant feature transform (SIFT) and FSAF as well as miscellaneous networks, the proposed network can achieve the best recognition accuracy tested by the vehicle scale variant dataset. To testify the practicality of this modified network, the testing of public dataset ImageNet is done and the comparable results proved its effectiveness in general purpose of applications.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"6 4","pages":"227-236"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10409228","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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