Pub Date : 2024-12-01DOI: 10.26599/JICV.2023.9210044
Jian Chen;Yunfeng Xiang;Yugong Luo;Keqiang Li;Xiaomin Lian
The behaviors of front vehicles are important factors that can influence the driving safety of autonomous vehicles on highways. This situation poses a serious threat to the security of autonomous vehicles, especially when front vehicle sideslip occurs. To address this problem, a decision-making approach can be used to promote the emergency obstacle avoidance capability of autonomous vehicles. First, the front sideslip vehicle trajectory was predicted by the kinematic models Constant Acceleration (CA), Constant Turn Rate and Velocity (CTRV), and Constant Turn Rate and Acceleration (CTRA) based on the front vehicle sideslip identification results. The CTRA prediction approach is chosen by comparing the prediction errors of the three models. To enhance the obstacle avoidance ability of autonomous vehicles, a novel trajectory planning method based on a driving characteristic vector is proposed. Model prediction control (MPC) is used to track the planned trajectory. Finally, the cosimulation platform of Simulink and Carsim was built. The simulation results show that autonomous vehicles can avoid collisions with front sideslip vehicles through the proposed approach, and the proposed trajectory planning approach has better obstacle avoidance ability than does the traditional approach.
{"title":"Decision Making and Control of Autonomous Vehicles Under the Condition of Front Vehicle Sideslip","authors":"Jian Chen;Yunfeng Xiang;Yugong Luo;Keqiang Li;Xiaomin Lian","doi":"10.26599/JICV.2023.9210044","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210044","url":null,"abstract":"The behaviors of front vehicles are important factors that can influence the driving safety of autonomous vehicles on highways. This situation poses a serious threat to the security of autonomous vehicles, especially when front vehicle sideslip occurs. To address this problem, a decision-making approach can be used to promote the emergency obstacle avoidance capability of autonomous vehicles. First, the front sideslip vehicle trajectory was predicted by the kinematic models Constant Acceleration (CA), Constant Turn Rate and Velocity (CTRV), and Constant Turn Rate and Acceleration (CTRA) based on the front vehicle sideslip identification results. The CTRA prediction approach is chosen by comparing the prediction errors of the three models. To enhance the obstacle avoidance ability of autonomous vehicles, a novel trajectory planning method based on a driving characteristic vector is proposed. Model prediction control (MPC) is used to track the planned trajectory. Finally, the cosimulation platform of Simulink and Carsim was built. The simulation results show that autonomous vehicles can avoid collisions with front sideslip vehicles through the proposed approach, and the proposed trajectory planning approach has better obstacle avoidance ability than does the traditional approach.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 4","pages":"248-257"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10823098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918489","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 : 2024-12-01DOI: 10.26599/JICV.2023.9210042
Chen Tu;Liang Wang;Jaehyuck Lim;Inhi Kim
The advancement of technology has propelled autonomous driving into the public spotlight over the past decade, establishing it as a strategic focal point for technological competition among countries (Lin et al., 2023b). For instance, the U.S. Department of Transportation released a series of influential documents outlining top-level designs for autonomous driving, ranging from the ‘Federal Autonomous Vehicle Policy Guide’ in 2016 to the ‘Ensuring the U.S. Leadership in Automated Driving: Autonomous Vehicle 4.0’ in 2020. In 2016, Japan formulated a roadmap to promote the adoption of autonomous driving, culminating in the launch of its inaugural L4-level autonomous vehicle public road operation service in 2023. Moreover, the development of autonomous driving in Europe is primarily concentrated in countries such as Germany, France, UK, and Sweden. These countries boast robust automotive industry foundations in the field of autonomous driving, accompanied by advanced systems and frameworks in terms of regulations and standards.
{"title":"Advancements and Prospects in Multisensor Fusion for Autonomous Driving","authors":"Chen Tu;Liang Wang;Jaehyuck Lim;Inhi Kim","doi":"10.26599/JICV.2023.9210042","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210042","url":null,"abstract":"The advancement of technology has propelled autonomous driving into the public spotlight over the past decade, establishing it as a strategic focal point for technological competition among countries (Lin et al., 2023b). For instance, the U.S. Department of Transportation released a series of influential documents outlining top-level designs for autonomous driving, ranging from the ‘Federal Autonomous Vehicle Policy Guide’ in 2016 to the ‘Ensuring the U.S. Leadership in Automated Driving: Autonomous Vehicle 4.0’ in 2020. In 2016, Japan formulated a roadmap to promote the adoption of autonomous driving, culminating in the launch of its inaugural L4-level autonomous vehicle public road operation service in 2023. Moreover, the development of autonomous driving in Europe is primarily concentrated in countries such as Germany, France, UK, and Sweden. These countries boast robust automotive industry foundations in the field of autonomous driving, accompanied by advanced systems and frameworks in terms of regulations and standards.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 4","pages":"245-247"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10823101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918293","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 : 2024-12-01DOI: 10.26599/JICV.2023.9210047
Syed Muzammil Abbas Rizvi;Bernhard Friedrich
The macroscopic fundamental diagram (MFD) represents the aggregated traffic states of a road network. However, the uniqueness of an empirically estimated MFD cannot be guaranteed due to the problem of link selection. Instationarity and varying flow patterns make it difficult to select link flows that are representative of the traffic state in the whole network. This study developed a new method for selecting links equipped with loop detectors that represent a particular traffic state of a road network. The method utilizes a metric of heterogeneity characterizing the role of a network link over the time of day. The dispersion metric indicates the heterogeneity in traffic states and the dynamic role of each time interval. It ranks links based on the heterogeneity-weighted saturation level, with the highest-rank links representing the most homogeneous subset of sample links. This study compared classical and proposed dynamic weights using loop detector data from Zurich and London and a simulated network. Sample links were selected based on different saturation levels, and the saturation level was associated with the heterogeneity level to identify the links creating heterogeneity in the road network.
{"title":"Improving the Representation of Traffic States: A Novel Method for Link Selection of Urban Road Networks","authors":"Syed Muzammil Abbas Rizvi;Bernhard Friedrich","doi":"10.26599/JICV.2023.9210047","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210047","url":null,"abstract":"The macroscopic fundamental diagram (MFD) represents the aggregated traffic states of a road network. However, the uniqueness of an empirically estimated MFD cannot be guaranteed due to the problem of link selection. Instationarity and varying flow patterns make it difficult to select link flows that are representative of the traffic state in the whole network. This study developed a new method for selecting links equipped with loop detectors that represent a particular traffic state of a road network. The method utilizes a metric of heterogeneity characterizing the role of a network link over the time of day. The dispersion metric indicates the heterogeneity in traffic states and the dynamic role of each time interval. It ranks links based on the heterogeneity-weighted saturation level, with the highest-rank links representing the most homogeneous subset of sample links. This study compared classical and proposed dynamic weights using loop detector data from Zurich and London and a simulated network. Sample links were selected based on different saturation levels, and the saturation level was associated with the heterogeneity level to identify the links creating heterogeneity in the road network.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 4","pages":"266-278"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10823100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918520","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 : 2024-12-01DOI: 10.26599/JICV.2023.9210046
Adham Badran;Ahmed El-Geneidy;Luis Miranda-Moreno
The emergence of road users' global positioning system (GPS) trajectory data is attracting increasing research interest in knowledge discovery to improve transport planning-related methods and tools. In fact, the widespread use of GPS-enabled smartphones and the mobile internet has increased the availability and size of such data. With the increase in GPS data coverage and availability, some research has expanded its use to estimate state-wide vehicle-miles travelled, to classify driving maneuvers for road safety assessment, or to estimate environmental performance indicators, such as vehicular fuel consumption and pollution emissions. In computer science, research has used GPS data to infer road network maps. Although the inferred maps provide a correct topology and connectivity, they lack the essential details to be used for transport modeling. Therefore, this work proposes a method to extract network-wide road direction and turning movement rules. In addition, building a road network model under the widely used macroscopic transport modeling software serves as a proof of concept. A sensitivity analysis was carried out to determine the output quality and recommend future improvements. Road segment geometry and directionality were extracted accurately (case study accuracy of 95 % ); however, turning movement rules can be extracted more accurately using a larger GPS vehicle trajectory sample (case study accuracy of 68%).
{"title":"Extracting Networkwide Road Segment Location, Direction, and Turning Movement Rules From Global Positioning System Vehicle Trajectory Data for Macrosimulation","authors":"Adham Badran;Ahmed El-Geneidy;Luis Miranda-Moreno","doi":"10.26599/JICV.2023.9210046","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210046","url":null,"abstract":"The emergence of road users' global positioning system (GPS) trajectory data is attracting increasing research interest in knowledge discovery to improve transport planning-related methods and tools. In fact, the widespread use of GPS-enabled smartphones and the mobile internet has increased the availability and size of such data. With the increase in GPS data coverage and availability, some research has expanded its use to estimate state-wide vehicle-miles travelled, to classify driving maneuvers for road safety assessment, or to estimate environmental performance indicators, such as vehicular fuel consumption and pollution emissions. In computer science, research has used GPS data to infer road network maps. Although the inferred maps provide a correct topology and connectivity, they lack the essential details to be used for transport modeling. Therefore, this work proposes a method to extract network-wide road direction and turning movement rules. In addition, building a road network model under the widely used macroscopic transport modeling software serves as a proof of concept. A sensitivity analysis was carried out to determine the output quality and recommend future improvements. Road segment geometry and directionality were extracted accurately (case study accuracy of 95\u0000<sup>%</sup>\u0000); however, turning movement rules can be extracted more accurately using a larger GPS vehicle trajectory sample (case study accuracy of 68%).","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 4","pages":"258-265"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10823099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918488","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 : 2024-12-01DOI: 10.26599/JICV.2023.9210050
Katherine Riffle;Edward J. Smaglik;Steven Procaccio;Steven R. Gehrke;Brendan J. Russo;David Hurwitz
This study develops new methods for evaluating detector health via event-based outputs and existing traffic flow theory. In this work, event-based detector data outputs were used to develop empirical vehicle volume-density curves per Greenshields fundamental model. Through integration, these empirical lines were compared with a conceptual volume-density curve for each detector, which was generated with average headway and posted speed limit data. The detector performance and site information were also used to model a predicted volume-density relationship for each detector on the basis of empirical observations, which was then compared with the conceptual line in the same manner as the empirical lines. The outcomes of each comparison were then used to create a database for assessing detector health within the structure of an algorithm. The algorithm is presented and discussed, followed by directions for future research, applications for practice, lessons learned, and limitations of this work.
{"title":"Application of the Traffic Fundamental Diagram to Assess Detector Performance","authors":"Katherine Riffle;Edward J. Smaglik;Steven Procaccio;Steven R. Gehrke;Brendan J. Russo;David Hurwitz","doi":"10.26599/JICV.2023.9210050","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210050","url":null,"abstract":"This study develops new methods for evaluating detector health via event-based outputs and existing traffic flow theory. In this work, event-based detector data outputs were used to develop empirical vehicle volume-density curves per Greenshields fundamental model. Through integration, these empirical lines were compared with a conceptual volume-density curve for each detector, which was generated with average headway and posted speed limit data. The detector performance and site information were also used to model a predicted volume-density relationship for each detector on the basis of empirical observations, which was then compared with the conceptual line in the same manner as the empirical lines. The outcomes of each comparison were then used to create a database for assessing detector health within the structure of an algorithm. The algorithm is presented and discussed, followed by directions for future research, applications for practice, lessons learned, and limitations of this work.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 4","pages":"279-291"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10823097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918519","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 : 2024-09-26DOI: 10.26599/JICV.2023.9210038
Zhe Zheng;Jian Yuan;Kun An;Nan Zheng;Wanjing Ma
Through and right-turn shared lanes are widely designed to increase the capacity of through traffic, but they can also cause delays for right-turn vehicles. This study presents a dynamic control method for a shared lane that prioritizes right-turn vehicles at the beginning of the cycle and subsequently allows through traffic to queue in the shared lane for saturated discharge. The traffic wave model is employed to reveal the dynamics of the traffic flow under this control and to derive the relationships among major traffic parameters. Constrained by the major relationship, a linear programming approach to minimize the total queue length is developed to determine the proper values of control parameters, including the shared area length, subordinate signal time lag, and shared or exclusive duration. A sensitivity analysis of the control parameters for different arrival rates and flow ratios is performed. Comparisons are conducted among the dynamic shared lane, the fixed exclusive lane, and the fixed shared lane. The results show that the dynamic control method results in a lower delay for both through and total traffic.
{"title":"Coordinated Optimization of Signal Timing for Intersections with Dynamic Shared Through- and Right-Turn Lanes","authors":"Zhe Zheng;Jian Yuan;Kun An;Nan Zheng;Wanjing Ma","doi":"10.26599/JICV.2023.9210038","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210038","url":null,"abstract":"Through and right-turn shared lanes are widely designed to increase the capacity of through traffic, but they can also cause delays for right-turn vehicles. This study presents a dynamic control method for a shared lane that prioritizes right-turn vehicles at the beginning of the cycle and subsequently allows through traffic to queue in the shared lane for saturated discharge. The traffic wave model is employed to reveal the dynamics of the traffic flow under this control and to derive the relationships among major traffic parameters. Constrained by the major relationship, a linear programming approach to minimize the total queue length is developed to determine the proper values of control parameters, including the shared area length, subordinate signal time lag, and shared or exclusive duration. A sensitivity analysis of the control parameters for different arrival rates and flow ratios is performed. Comparisons are conducted among the dynamic shared lane, the fixed exclusive lane, and the fixed shared lane. The results show that the dynamic control method results in a lower delay for both through and total traffic.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"219-228"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324273","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}
The mobility landscape is experiencing major changes due to two emerging transportation trends, autonomous vehicles (AVs) and on-demand transportation, and the convergence of these smart mobility innovations as shared autonomous vehicles (SAVs) can considerably alter travel behavior and consequently the ecological and societal aspects of the transportation sector. On-demand autonomous mobility is a promising transportation mode, but further research is necessary to evaluate its various aspects and implications prior to widespread adoption. Thus, this study investigates the effects of integrating automation and on-demand mobility by analyzing the effects on the environment, public transportation, land use, vehicle ownership, and public acceptance. A comprehensive literature review was performed, and through a detailed review of 210 articles, the impacts of each of these categories were determined and classified according to their causes, and the number of publications with which they were cited in the literature was determined. The review showed that SAVs can either positively or negatively impact categories and have the potential to minimize mobility obstacles and transportation inequity if legislators use technology to develop a better transportation system by initiating effective policies that govern the four impacted areas. A list of 22 policy recommendations designed to avoid the negative consequences of SAVs by maximizing the benefits of the technology while limiting the associated risks was also identified. The findings of this review will be beneficial to AV manufacturers, transportation professionals, and especially policymakers, who play an integral role in shaping how society benefits from SAV technology.
由于自动驾驶汽车（AV）和按需运输这两种新兴的交通趋势，交通格局正在经历重大变化，而共享自动驾驶汽车（SAV）这种智能交通创新的融合可以大大改变人们的出行行为，进而改变交通领域的生态和社会方面。按需自主交通是一种前景广阔的交通模式，但在广泛采用之前，有必要开展进一步研究，以评估其各个方面和影响。因此，本研究通过分析对环境、公共交通、土地使用、车辆所有权和公众接受度的影响，调查了自动化与按需移动相结合的影响。本研究进行了全面的文献综述，通过对 210 篇文章的详细审查，确定了上述各类影响，并根据其成因进行了分类，还确定了这些影响在文献中的引用数量。审查结果表明，如果立法者利用技术开发出更好的交通系统，启动有效的政策来管理这四个受影响的领域，那么小型自动变速器就有可能最大限度地减少流动障碍和交通不公平。此外，还确定了一份 22 条政策建议清单，旨在通过最大限度地发挥技术的益处，同时限制相关风险，避免 SAVs 带来的负面影响。本次审查的结果将有益于自动驾驶汽车制造商、交通专业人士，特别是政策制定者，他们在塑造社会如何从自动驾驶汽车技术中受益方面发挥着不可或缺的作用。
{"title":"Convergence of Emerging Transportation Trends: A Comprehensive Review of Shared Autonomous Vehicles","authors":"Deema Almaskati;Sharareh Kermanshachi;Apurva Pamidimukkala","doi":"10.26599/JICV.2023.9210043","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210043","url":null,"abstract":"The mobility landscape is experiencing major changes due to two emerging transportation trends, autonomous vehicles (AVs) and on-demand transportation, and the convergence of these smart mobility innovations as shared autonomous vehicles (SAVs) can considerably alter travel behavior and consequently the ecological and societal aspects of the transportation sector. On-demand autonomous mobility is a promising transportation mode, but further research is necessary to evaluate its various aspects and implications prior to widespread adoption. Thus, this study investigates the effects of integrating automation and on-demand mobility by analyzing the effects on the environment, public transportation, land use, vehicle ownership, and public acceptance. A comprehensive literature review was performed, and through a detailed review of 210 articles, the impacts of each of these categories were determined and classified according to their causes, and the number of publications with which they were cited in the literature was determined. The review showed that SAVs can either positively or negatively impact categories and have the potential to minimize mobility obstacles and transportation inequity if legislators use technology to develop a better transportation system by initiating effective policies that govern the four impacted areas. A list of 22 policy recommendations designed to avoid the negative consequences of SAVs by maximizing the benefits of the technology while limiting the associated risks was also identified. The findings of this review will be beneficial to AV manufacturers, transportation professionals, and especially policymakers, who play an integral role in shaping how society benefits from SAV technology.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"177-189"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324349","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 provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.
{"title":"Development of Deep-Learning-Based Autonomous Agents for Low-Speed Maneuvering in Unity","authors":"Riccardo Berta;Luca Lazzaroni;Alessio Capello;Marianna Cossu;Luca Forneris;Alessandro Pighetti;Francesco Bellotti","doi":"10.26599/JICV.2023.9210039","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210039","url":null,"abstract":"This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"229-244"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324272","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}
Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence. While the UN R157 Regulation evaluates automated lane-keeping system (ALKS) performance baselines through safe collision plots (SCPs) in various scenario clusters, quantifying the specific ALKS safety efficiency remains challenging. We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios. First, we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process. Second, by utilizing Fourier analysis, a spectral analysis model was built to quantify and analyze the vehicle motion characteristics, providing insights into scenario safety. Finally, we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model. The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2. When the relative longitudinal distance and speed of the vehicle are the same, if the cut-in speed of the cut-in vehicle is larger, the normalized disturbance frequency is higher, indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.
{"title":"Spectrum Quantification-Based Safety Efficiency Evaluation of Autonomous Vehicle Under Random Cut-in Scenarios","authors":"Jiang Chen;Weiwei Zhang;Miao Liu;Xiaolan Wang;Jun Gong;Jun Li;Boqi Li;Jiejie Xu","doi":"10.26599/JICV.2023.9210035","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210035","url":null,"abstract":"Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence. While the UN R157 Regulation evaluates automated lane-keeping system (ALKS) performance baselines through safe collision plots (SCPs) in various scenario clusters, quantifying the specific ALKS safety efficiency remains challenging. We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios. First, we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process. Second, by utilizing Fourier analysis, a spectral analysis model was built to quantify and analyze the vehicle motion characteristics, providing insights into scenario safety. Finally, we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model. The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2. When the relative longitudinal distance and speed of the vehicle are the same, if the cut-in speed of the cut-in vehicle is larger, the normalized disturbance frequency is higher, indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"205-218"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323054","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}
With the rapid growth of urbanization and the increasing demand for transportation, urban traffic congestion has become a hindrance to individuals' travel experience. Urban intersections are one of the primary sources of traffic congestion, and these bottlenecks have a negative impact not only on traffic efficacy but also on the surrounding road traffic in the region. To alleviate urban traffic congestion, cyber-physical systems have been widely implemented in the transportation industry, allowing for the perception, analysis, calculation, and dispatching of urban traffic flow, as well as making urban transportation safe, efficient, and quick. As the system scale and functions increase, system design has become increasingly complex, necessitating a deeper comprehension of the system's structure and interaction relationships to construct a stable and reliable system. Therefore, this study proposes a method for designing cyber-physical systems for urban traffic intersections based on Model-Based Systems Engineering (MBSE). This method models and analyses exhaustively the system's requirements, functions, and logical architecture using System Modeling Language (SysML). After the architecture design has been completed, an architecture verification and optimization method based on Failure Mode and Effect Analysis (FMEA) for urban road intersection cyber-physical systems is utilized to analyze the architecture's reliability by analyzing the failure modes of activities and to optimize the system architecture to improve the design's efficiency and reliability.
{"title":"CPS Architecture Design for Urban Roadway Intersections Based on MBSE","authors":"Chen Wang;Xiaoping Ma;Limin Jia;Zheng Lai;Zhexuan Yang;Han Yan;Jing Zhao","doi":"10.26599/JICV.2023.9210030","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210030","url":null,"abstract":"With the rapid growth of urbanization and the increasing demand for transportation, urban traffic congestion has become a hindrance to individuals' travel experience. Urban intersections are one of the primary sources of traffic congestion, and these bottlenecks have a negative impact not only on traffic efficacy but also on the surrounding road traffic in the region. To alleviate urban traffic congestion, cyber-physical systems have been widely implemented in the transportation industry, allowing for the perception, analysis, calculation, and dispatching of urban traffic flow, as well as making urban transportation safe, efficient, and quick. As the system scale and functions increase, system design has become increasingly complex, necessitating a deeper comprehension of the system's structure and interaction relationships to construct a stable and reliable system. Therefore, this study proposes a method for designing cyber-physical systems for urban traffic intersections based on Model-Based Systems Engineering (MBSE). This method models and analyses exhaustively the system's requirements, functions, and logical architecture using System Modeling Language (SysML). After the architecture design has been completed, an architecture verification and optimization method based on Failure Mode and Effect Analysis (FMEA) for urban road intersection cyber-physical systems is utilized to analyze the architecture's reliability by analyzing the failure modes of activities and to optimize the system architecture to improve the design's efficiency and reliability.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"190-204"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323055","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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