Pub Date : 2024-07-16DOI: 10.26599/JICV.2023.9210049
Yongsheng Zhang;Chen Tu;Kun Gao;Liang Wang
As urban transportation increasingly impacts daily life, efficiently utilizing traffic resources and developing public transportation have become crucial for addressing issues such as congestion, frequent accidents, and noise pollution. The rapid advancement of intelligent autonomous driving technologies, particularly environmental perception technologies, offers new directions for solving these problems. This review discusses the application of multisensor information fusion technology in environmental perception for intelligent vehicles, analyzing the components and performance of various sensors and their specific applications in autonomous driving. Through multisensor information fusion, the accuracy of environmental perception is enhanced, optimizing decision support for autonomous driving systems and thereby improving vehicle safety and driving efficiency. This study also discusses the challenges faced by information fusion technology and future development trends, providing references for further research and application in intelligent transportation systems.
{"title":"Multisensor Information Fusion: Future of Environmental Perception in Intelligent Vehicles","authors":"Yongsheng Zhang;Chen Tu;Kun Gao;Liang Wang","doi":"10.26599/JICV.2023.9210049","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210049","url":null,"abstract":"As urban transportation increasingly impacts daily life, efficiently utilizing traffic resources and developing public transportation have become crucial for addressing issues such as congestion, frequent accidents, and noise pollution. The rapid advancement of intelligent autonomous driving technologies, particularly environmental perception technologies, offers new directions for solving these problems. This review discusses the application of multisensor information fusion technology in environmental perception for intelligent vehicles, analyzing the components and performance of various sensors and their specific applications in autonomous driving. Through multisensor information fusion, the accuracy of environmental perception is enhanced, optimizing decision support for autonomous driving systems and thereby improving vehicle safety and driving efficiency. This study also discusses the challenges faced by information fusion technology and future development trends, providing references for further research and application in intelligent transportation systems.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"163-176"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10600093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324356","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}
Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems. In a complex traffic environment, the signal of the Global Navigation Satellite System (GNSS) will be blocked, leading to inaccurate vehicle positioning. To ensure the security of automatic electric campus vehicles, this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain (LEGO-LOAM) algorithm with a monocular vision system added. An algorithm framework based on Lidar-IMU-Camera (Lidar means light detection and ranging) fusion was proposed. A lightweight monocular vision odometer model was used, and the LEGO-LOAM system was employed to initialize monocular vision. The visual odometer information was taken as the initial value of the laser odometer. At the back-end opti9mization phase error state, the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning. The visual word bag model was applied to perform loopback detection. Taking the test results into account, the laser radar loopback detection was further optimized, reducing the accumulated positioning error. The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment. The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav. Compared with the LEGO-LOAM algorithm, the results show that the proposed algorithm can effectively reduce map drift, improve map resolution, and output more accurate driving trajectory information.
{"title":"Localization and Mapping Algorithm Based on Lidar-IMU-Camera Fusion","authors":"Yibing Zhao;Yuhe Liang;Zhenqiang Ma;Lie Guo;Hexin Zhang","doi":"10.26599/JICV.2023.9210027","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210027","url":null,"abstract":"Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems. In a complex traffic environment, the signal of the Global Navigation Satellite System (GNSS) will be blocked, leading to inaccurate vehicle positioning. To ensure the security of automatic electric campus vehicles, this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain (LEGO-LOAM) algorithm with a monocular vision system added. An algorithm framework based on Lidar-IMU-Camera (Lidar means light detection and ranging) fusion was proposed. A lightweight monocular vision odometer model was used, and the LEGO-LOAM system was employed to initialize monocular vision. The visual odometer information was taken as the initial value of the laser odometer. At the back-end opti9mization phase error state, the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning. The visual word bag model was applied to perform loopback detection. Taking the test results into account, the laser radar loopback detection was further optimized, reducing the accumulated positioning error. The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment. The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav. Compared with the LEGO-LOAM algorithm, the results show that the proposed algorithm can effectively reduce map drift, improve map resolution, and output more accurate driving trajectory information.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"97-107"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543845","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 : 2024-06-01DOI: 10.26599/JICV.2023.9210037
Miklós Lukovics;Szabolcs Prónay;Barbara Nagy
Previous studies have identified trust as one of the key factors in the technology acceptance of autonomous vehicles. As these studies mostly investigated the population in general, little is known about segment-specific differences. Furthermore, the widely used survey methods are less able to capture the deeper forms of trust—which neuroscientific methods are much better suited to capture. The main objective of our research is to study trust as one of the key factors of technology acceptance related to autonomous vehicles by using neuroscientific methods for specific consumer segments. Real-time eye-tracking tests were applied to a sample of 113 participants, combined with a posttest self-report. The tests were carried out under laboratory conditions during which our subjects watched videos recorded with the internal cameras of autonomous vehicles. Based on the fixation count, total fixation duration, and pupil dilation, we empirically verified that the trust level of all five identified segments is relatively low, while the trust level of the “traditional rejecting” segment is the lowest. An increase in trust level can be shown if the subjects receive extra information about the journey. Another important finding is that the self-reported trust level is not always congruent with the eye-tracking analysis results; therefore, combined approaches can lead to greater measurement validity.
{"title":"Segmented Trust Assessment in Autonomous Vehicles via Eye-Tracking","authors":"Miklós Lukovics;Szabolcs Prónay;Barbara Nagy","doi":"10.26599/JICV.2023.9210037","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210037","url":null,"abstract":"Previous studies have identified trust as one of the key factors in the technology acceptance of autonomous vehicles. As these studies mostly investigated the population in general, little is known about segment-specific differences. Furthermore, the widely used survey methods are less able to capture the deeper forms of trust—which neuroscientific methods are much better suited to capture. The main objective of our research is to study trust as one of the key factors of technology acceptance related to autonomous vehicles by using neuroscientific methods for specific consumer segments. Real-time eye-tracking tests were applied to a sample of 113 participants, combined with a posttest self-report. The tests were carried out under laboratory conditions during which our subjects watched videos recorded with the internal cameras of autonomous vehicles. Based on the fixation count, total fixation duration, and pupil dilation, we empirically verified that the trust level of all five identified segments is relatively low, while the trust level of the “traditional rejecting” segment is the lowest. An increase in trust level can be shown if the subjects receive extra information about the journey. Another important finding is that the self-reported trust level is not always congruent with the eye-tracking analysis results; therefore, combined approaches can lead to greater measurement validity.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"151-161"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543846","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 : 2024-06-01DOI: 10.26599/JICV.2023.9210036
Zihao Sheng;Zilin Huang;Sikai Chen
Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the safety and efficiency of automated driving in highly interactive traffic environments. Numerous studies in this area have focused on physics-based approaches because they can clearly interpret the dynamic evolution of trajectories. However, physics-based methods often suffer from limited accuracy. Recent learning-based methods have demonstrated better performance, but they cannot be fully trusted due to the insufficient incorporation of physical constraints. To mitigate the limitations of purely physics-based and learning-based approaches, this study proposes a kinematics-aware multigraph attention network (KA-MGAT) that incorporates physics models into a deep learning framework to improve the learning process of neural networks. Besides, we propose a residual prediction module to further refine the trajectory predictions and address the limitations arising from simplified assumptions in kinematic models. We evaluate our proposed model through experiments on two challenging trajectory datasets, namely, ApolloScape and NGSIM. Our findings from the experiments demonstrate that our model outperforms various kinematics-agnostic models with respect to prediction accuracy and learning efficiency.
{"title":"Kinematics-Aware Multigraph Attention Network with Residual Learning for Heterogeneous Trajectory Prediction","authors":"Zihao Sheng;Zilin Huang;Sikai Chen","doi":"10.26599/JICV.2023.9210036","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210036","url":null,"abstract":"Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the safety and efficiency of automated driving in highly interactive traffic environments. Numerous studies in this area have focused on physics-based approaches because they can clearly interpret the dynamic evolution of trajectories. However, physics-based methods often suffer from limited accuracy. Recent learning-based methods have demonstrated better performance, but they cannot be fully trusted due to the insufficient incorporation of physical constraints. To mitigate the limitations of purely physics-based and learning-based approaches, this study proposes a kinematics-aware multigraph attention network (KA-MGAT) that incorporates physics models into a deep learning framework to improve the learning process of neural networks. Besides, we propose a residual prediction module to further refine the trajectory predictions and address the limitations arising from simplified assumptions in kinematic models. We evaluate our proposed model through experiments on two challenging trajectory datasets, namely, ApolloScape and NGSIM. Our findings from the experiments demonstrate that our model outperforms various kinematics-agnostic models with respect to prediction accuracy and learning efficiency.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"138-150"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543876","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 : 2024-06-01DOI: 10.26599/JICV.2023.9210040
Yang Fei;Peng Shi;Yang Liu;Liang Wang
In recent years, advancements in onboard computing hardware and wireless communication technology have remarkably stimulated the development of intelligent and connected vehicles (ICVs). Specifically, some researchers have investigated the issue of employing various advanced control techniques to optimize the performance of autonomous vehicles in practice (Sun et al., 2023; Zhang et al., 2023a, 2023b). Therefore, this article aims to discuss why and how control engineering plays an essential role in the development of ICVs.
{"title":"Critical Roles of Control Engineering in the Development of Intelligent and Connected Vehicles","authors":"Yang Fei;Peng Shi;Yang Liu;Liang Wang","doi":"10.26599/JICV.2023.9210040","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210040","url":null,"abstract":"In recent years, advancements in onboard computing hardware and wireless communication technology have remarkably stimulated the development of intelligent and connected vehicles (ICVs). Specifically, some researchers have investigated the issue of employing various advanced control techniques to optimize the performance of autonomous vehicles in practice (Sun et al., 2023; Zhang et al., 2023a, 2023b). Therefore, this article aims to discuss why and how control engineering plays an essential role in the development of ICVs.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"79-85"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543874","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 : 2024-06-01DOI: 10.26599/JICV.2023.9210026
Qianwen Li;Peng Zhang;Handong Yao;Zhiwei Chen;Xiaopeng Li
Motivated by the promising benefits of connected and autonomous vehicles (CAVs) in improving fuel efficiency, mitigating congestion, and enhancing safety, numerous theoretical models have been proposed to plan CAV multiple-step trajectories (time-specific speed/location trajectories) to accomplish various operations. However, limited efforts have been made to develop proper trajectory control techniques to regulate vehicle movements to follow multiple-step trajectories and test the performance of theoretical trajectory planning models with field experiments. Without an effective control method, the benefits of theoretical models for CAV trajectory planning can be difficult to harvest. This study proposes an online learning-based model predictive vehicle trajectory control structure to follow time-specific speed and location profiles. Unlike single-step controllers that are dominantly used in the literature, a multiple-step model predictive controller is adopted to control the vehicle's longitudinal movements for higher accuracy. The model predictive controller output (speed) cannot be interpreted by vehicles. A reinforcement learning agent is used to convert the speed value to the vehicle's direct control variable (i.e., throttle/brake). The reinforcement learning agent captures real-time changes in the operating environment. This is valuable in saving parameter calibration resources and improving trajectory control accuracy. A line tracking controller keeps vehicles on track. The proposed control structure is tested using reduced-scale robot cars. The adaptivity of the proposed control structure is demonstrated by changing the vehicle load. Then, experiments on two fundamental CAV platoon operations (i.e., platooning and split) show the effectiveness of the proposed trajectory control structure in regulating robot movements to follow time-specific reference trajectories.
{"title":"Online Learning-Based Model Predictive Trajectory Control for Connected and Autonomous Vehicles: Modeling and Physical Tests","authors":"Qianwen Li;Peng Zhang;Handong Yao;Zhiwei Chen;Xiaopeng Li","doi":"10.26599/JICV.2023.9210026","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210026","url":null,"abstract":"Motivated by the promising benefits of connected and autonomous vehicles (CAVs) in improving fuel efficiency, mitigating congestion, and enhancing safety, numerous theoretical models have been proposed to plan CAV multiple-step trajectories (time-specific speed/location trajectories) to accomplish various operations. However, limited efforts have been made to develop proper trajectory control techniques to regulate vehicle movements to follow multiple-step trajectories and test the performance of theoretical trajectory planning models with field experiments. Without an effective control method, the benefits of theoretical models for CAV trajectory planning can be difficult to harvest. This study proposes an online learning-based model predictive vehicle trajectory control structure to follow time-specific speed and location profiles. Unlike single-step controllers that are dominantly used in the literature, a multiple-step model predictive controller is adopted to control the vehicle's longitudinal movements for higher accuracy. The model predictive controller output (speed) cannot be interpreted by vehicles. A reinforcement learning agent is used to convert the speed value to the vehicle's direct control variable (i.e., throttle/brake). The reinforcement learning agent captures real-time changes in the operating environment. This is valuable in saving parameter calibration resources and improving trajectory control accuracy. A line tracking controller keeps vehicles on track. The proposed control structure is tested using reduced-scale robot cars. The adaptivity of the proposed control structure is demonstrated by changing the vehicle load. Then, experiments on two fundamental CAV platoon operations (i.e., platooning and split) show the effectiveness of the proposed trajectory control structure in regulating robot movements to follow time-specific reference trajectories.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"86-96"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543844","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}
Roadside cameras play a crucial role in road traffic, serving as an indispensable part of integrated vehicle-road-cloud systems due to their extensive visibility and monitoring capabilities. Nevertheless, these cameras face challenges in continuously tracking targets across perception domains. To address the issue of tracking vehicles across nonoverlapping perception domains between cameras, we propose a cross-camera vehicle tracking method within a Vehicle-Road-Cloud system that integrates visual and spatiotemporal information. A Gaussian model with microlevel traffic features is trained using vehicle information obtained through online tracking. Finally, the association of vehicle targets is achieved through the Gaussian model combining time and visual feature information. The experimental results indicate that the proposed system demonstrates excellent performance.
{"title":"Roadside Cross-Camera Vehicle Tracking Combining Visual and Spatial-Temporal Information for a Cloud Control System","authors":"Bolin Gao;Zhuxin Li;Dong Zhang;Yanwei Liu;Jiaxing Chen;Ziyuan Lv","doi":"10.26599/JICV.2023.9210034","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210034","url":null,"abstract":"Roadside cameras play a crucial role in road traffic, serving as an indispensable part of integrated vehicle-road-cloud systems due to their extensive visibility and monitoring capabilities. Nevertheless, these cameras face challenges in continuously tracking targets across perception domains. To address the issue of tracking vehicles across nonoverlapping perception domains between cameras, we propose a cross-camera vehicle tracking method within a Vehicle-Road-Cloud system that integrates visual and spatiotemporal information. A Gaussian model with microlevel traffic features is trained using vehicle information obtained through online tracking. Finally, the association of vehicle targets is achieved through the Gaussian model combining time and visual feature information. The experimental results indicate that the proposed system demonstrates excellent performance.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"129-137"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10586907","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141544049","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 : 2024-03-22DOI: 10.26599/JICV.2023.9210028
Ali Matin;Hussein Dia
This study investigates the attitudes and concerns of the Australian public toward connected and autonomous vehicles (CAVs), and the factors influencing their willingness to adopt this technology. Through a comprehensive survey, a diverse group of respondents provided valuable insights toward various CAV scenarios such as riding in a vehicle with no driver, self-driving public transport, self-driving taxis, and heavy vehicles without drivers. The results highlight the significant impact of safety concerns about automated vehicles on individuals' attitudes across all scenarios. Higher levels of concern were associated with more negative attitudes, and a strong correlation between concerns and opposition underlines the necessity of addressing these apprehensions to build public trust and promote CAV adoption. Interestingly, nearly 70% of respondents felt uncomfortable driving next to a CAV, but they displayed more confidence in adopting automated public transport in the near future. Additionally, around 40% of participants indicated a strong willingness to purchase a CAV, primarily driven by the desire to reduce their carbon footprint and safety considerations. Notably, respondents with health conditions or disability exhibited heightened interest (almost double those without health conditions) in CAV technology. Gender differences emerged in attitudes and preferences toward CAVs, with women expressing a greater level of concern and perceiving higher barriers to CAV deployment. This emphasizes the importance of employing targeted approaches to address the specific concerns of different demographics. The study also underscores the role of trust in technology as a significant barrier to CAV deployment, ranking high among respondents' concerns. To overcome these challenges and facilitate successful CAV deployment, various strategies are suggested, including live demonstrations, dedicated routes for automated public transport, adoption incentives, and addressing liability concerns. The findings from this study offer valuable insights for government agencies, vehicle manufacturers, and stakeholders in promoting the successful implementation of CAVs. By understanding societal acceptance and addressing concerns, decision-makers can devise effective interventions and policies to ensure the safe and widespread adoption of CAVs in Australia. Moreover, vehicle manufacturers can leverage these results to consider design aspects that align with passenger preferences, thereby facilitating the broader acceptance and adoption of CAVs in the future. Finally, this research provides a significant contribution to the understanding of public perception and acceptance of CAVs in the Australian context. By guiding decision-making and informing strategies, the study lays the foundation for a safer and more effective integration of CAVs into the country's transportation landscape.
{"title":"Public Perception of Connected and Automated Vehicles: Benefits, Concerns, and Barriers from an Australian Perspective","authors":"Ali Matin;Hussein Dia","doi":"10.26599/JICV.2023.9210028","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210028","url":null,"abstract":"This study investigates the attitudes and concerns of the Australian public toward connected and autonomous vehicles (CAVs), and the factors influencing their willingness to adopt this technology. Through a comprehensive survey, a diverse group of respondents provided valuable insights toward various CAV scenarios such as riding in a vehicle with no driver, self-driving public transport, self-driving taxis, and heavy vehicles without drivers. The results highlight the significant impact of safety concerns about automated vehicles on individuals' attitudes across all scenarios. Higher levels of concern were associated with more negative attitudes, and a strong correlation between concerns and opposition underlines the necessity of addressing these apprehensions to build public trust and promote CAV adoption. Interestingly, nearly 70% of respondents felt uncomfortable driving next to a CAV, but they displayed more confidence in adopting automated public transport in the near future. Additionally, around 40% of participants indicated a strong willingness to purchase a CAV, primarily driven by the desire to reduce their carbon footprint and safety considerations. Notably, respondents with health conditions or disability exhibited heightened interest (almost double those without health conditions) in CAV technology. Gender differences emerged in attitudes and preferences toward CAVs, with women expressing a greater level of concern and perceiving higher barriers to CAV deployment. This emphasizes the importance of employing targeted approaches to address the specific concerns of different demographics. The study also underscores the role of trust in technology as a significant barrier to CAV deployment, ranking high among respondents' concerns. To overcome these challenges and facilitate successful CAV deployment, various strategies are suggested, including live demonstrations, dedicated routes for automated public transport, adoption incentives, and addressing liability concerns. The findings from this study offer valuable insights for government agencies, vehicle manufacturers, and stakeholders in promoting the successful implementation of CAVs. By understanding societal acceptance and addressing concerns, decision-makers can devise effective interventions and policies to ensure the safe and widespread adoption of CAVs in Australia. Moreover, vehicle manufacturers can leverage these results to consider design aspects that align with passenger preferences, thereby facilitating the broader acceptance and adoption of CAVs in the future. Finally, this research provides a significant contribution to the understanding of public perception and acceptance of CAVs in the Australian context. By guiding decision-making and informing strategies, the study lays the foundation for a safer and more effective integration of CAVs into the country's transportation landscape.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 2","pages":"108-128"},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10537112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543908","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.2023.9210031
Amandeep Singh;Sarah Yahoodik;Yovela Murzello;Samuel Petkac;Yusuke Yamani;Siby Samuel
The present study examined the impact of aging on ethical decision-making in simulated critical driving scenarios. 204 participants from North America, grouped into two age groups (18–30 years and 65 years and above), were asked to decide whether their simulated automated vehicle should stay in or change from the current lane in scenarios mimicking the Trolley Problem. Each participant viewed a video clip rendered by the driving simulator at Old Dominion University and pressed the space-bar if they decided to intervene in the control of the simulated automated vehicle in an online experiment. Bayesian hierarchical models were used to analyze participants' responses, response time, and acceptability of utilitarian ethical decision-making. The results showed significant pedestrian placement, age, and time-to-collision (TTC) effects on participants' ethical decisions. When pedestrians were in the right lane, participants were more likely to switch lanes, indicating a utilitarian approach prioritizing pedestrian safety. Younger participants were more likely to switch lanes in general compared to older participants. The results imply that older drivers can maintain their ability to respond to ethically fraught scenarios with their tendency to switch lanes more frequently than younger counterparts, even when the tasks interacting with an automated driving system. The current findings may inform the development of decision algorithms for intelligent and connected vehicles by considering potential ethical dilemmas faced by human drivers across different age groups.
{"title":"Ethical Decision-Making in Older Drivers During Critical Driving Situations: An Online Experiment","authors":"Amandeep Singh;Sarah Yahoodik;Yovela Murzello;Samuel Petkac;Yusuke Yamani;Siby Samuel","doi":"10.26599/JICV.2023.9210031","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210031","url":null,"abstract":"The present study examined the impact of aging on ethical decision-making in simulated critical driving scenarios. 204 participants from North America, grouped into two age groups (18–30 years and 65 years and above), were asked to decide whether their simulated automated vehicle should stay in or change from the current lane in scenarios mimicking the Trolley Problem. Each participant viewed a video clip rendered by the driving simulator at Old Dominion University and pressed the space-bar if they decided to intervene in the control of the simulated automated vehicle in an online experiment. Bayesian hierarchical models were used to analyze participants' responses, response time, and acceptability of utilitarian ethical decision-making. The results showed significant pedestrian placement, age, and time-to-collision (TTC) effects on participants' ethical decisions. When pedestrians were in the right lane, participants were more likely to switch lanes, indicating a utilitarian approach prioritizing pedestrian safety. Younger participants were more likely to switch lanes in general compared to older participants. The results imply that older drivers can maintain their ability to respond to ethically fraught scenarios with their tendency to switch lanes more frequently than younger counterparts, even when the tasks interacting with an automated driving system. The current findings may inform the development of decision algorithms for intelligent and connected vehicles by considering potential ethical dilemmas faced by human drivers across different age groups.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 1","pages":"30-37"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10506787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826049","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.2023.9210019
Changxi Ma;Fansong Xue
With the increasing number of vehicles, there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure. In order to achieve faster and more accurate identification of traffic vehicles, computer vision and deep learning technology play a vital role and have made significant advancements. This study summarizes the current research status, latest findings, and future development trends of traditional detection algorithms and deep learning-based detection algorithms. Among the detection algorithms based on deep learning, this study focuses on the representative convolutional neural network models. Specifically, it examines the two-stage and one-stage detection algorithms, which have been extensively utilized in the field of intelligent transportation systems. Compared to traditional detection algorithms, deep learning-based detection algorithms can achieve higher accuracy and efficiency. The single-stage detection algorithm is more efficient for real-time detection, while the two-stage detection algorithm is more accurate than the single-stage detection algorithm. In the follow-up research, it is important to consider the balance between detection efficiency and detection accuracy. Additionally, vehicle missed detection and false detection in complex scenes, such as bad weather and vehicle overlap, should be taken into account. This will ensure better application of the research findings in engineering practice.
{"title":"A Review of Vehicle Detection Methods Based on Computer Vision","authors":"Changxi Ma;Fansong Xue","doi":"10.26599/JICV.2023.9210019","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210019","url":null,"abstract":"With the increasing number of vehicles, there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure. In order to achieve faster and more accurate identification of traffic vehicles, computer vision and deep learning technology play a vital role and have made significant advancements. This study summarizes the current research status, latest findings, and future development trends of traditional detection algorithms and deep learning-based detection algorithms. Among the detection algorithms based on deep learning, this study focuses on the representative convolutional neural network models. Specifically, it examines the two-stage and one-stage detection algorithms, which have been extensively utilized in the field of intelligent transportation systems. Compared to traditional detection algorithms, deep learning-based detection algorithms can achieve higher accuracy and efficiency. The single-stage detection algorithm is more efficient for real-time detection, while the two-stage detection algorithm is more accurate than the single-stage detection algorithm. In the follow-up research, it is important to consider the balance between detection efficiency and detection accuracy. Additionally, vehicle missed detection and false detection in complex scenes, such as bad weather and vehicle overlap, should be taken into account. This will ensure better application of the research findings in engineering practice.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 1","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10520219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826050","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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