Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520393
Flávio Tonioli Mariotto;Néstor Becerra Yoma;Madson Cortes de Almeida
To make the proper planning of bus public transportation systems, especially with the introduction of electric buses to the fleets, it is essential to characterize the routes, patterns of traffic, speed, constraints, and presence of high slopes. Currently, GPS (Global Position System) is available worldwide in the fleet. However, they often produce datasets of poor quality, with low data rates, loss of information, noisy samples, and eventual paths not belonging to regular bus routes. Therefore, extracting useful information from these poor data is a challenging task. The current paper proposes a novel method based on an unsupervised competitive density clustering algorithm to obtain hot spot clusters of any density. The clusters are a result of their competition for the GPS samples. Each cluster attracts GPS samples until a maximum radius from its centroid and thereafter moves toward the most density areas. The winning clusters are sorted using a novel distance metric with the support of a visual interface, forming a sequence of points that outline the bus trajectory. Finally, indicators are correlated to the clusters making a trajectory characterization and allowing extensive assessments. According to the actual case studies, the method performs well with noisy GPS samples and the loss of information. The proposed method presents quite a fixed parameter, allowing fair performance for most GPS datasets without needing custom adjustments. It also proposes a framework for preparing the input GPS dataset, clustering, sorting the clusters to outline the trajectory, and making the trajectory characterization.
{"title":"Unsupervised Competitive Learning Clustering and Visual Method to Obtain Accurate Trajectories From Noisy Repetitive GPS Data","authors":"Flávio Tonioli Mariotto;Néstor Becerra Yoma;Madson Cortes de Almeida","doi":"10.1109/TITS.2024.3520393","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520393","url":null,"abstract":"To make the proper planning of bus public transportation systems, especially with the introduction of electric buses to the fleets, it is essential to characterize the routes, patterns of traffic, speed, constraints, and presence of high slopes. Currently, GPS (Global Position System) is available worldwide in the fleet. However, they often produce datasets of poor quality, with low data rates, loss of information, noisy samples, and eventual paths not belonging to regular bus routes. Therefore, extracting useful information from these poor data is a challenging task. The current paper proposes a novel method based on an unsupervised competitive density clustering algorithm to obtain hot spot clusters of any density. The clusters are a result of their competition for the GPS samples. Each cluster attracts GPS samples until a maximum radius from its centroid and thereafter moves toward the most density areas. The winning clusters are sorted using a novel distance metric with the support of a visual interface, forming a sequence of points that outline the bus trajectory. Finally, indicators are correlated to the clusters making a trajectory characterization and allowing extensive assessments. According to the actual case studies, the method performs well with noisy GPS samples and the loss of information. The proposed method presents quite a fixed parameter, allowing fair performance for most GPS datasets without needing custom adjustments. It also proposes a framework for preparing the input GPS dataset, clustering, sorting the clusters to outline the trajectory, and making the trajectory characterization.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1562-1572"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520317
Shanglian Zhou;Igor Lashkov;Hao Xu;Guohui Zhang;Yin Yang
In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on user expertise and prior knowledge, while newer deep learning approaches, such as Long Short-Term Memory (LSTM) networks, also face challenges related to human intervention and subjective hyperparameter selection. This study proposes a systematic approach for Light Detection and Ranging (LiDAR)-based vehicle trajectory prediction, leveraging LSTM networks to predict vehicle trajectories and employing Bayesian optimization to automatically search for optimal hyperparameter values related to both the training scheme and LSTM architectures. In the experimental study, a custom vehicle trajectory dataset extracted from roadside LiDAR data, along with the V2X-Seq-TFD dataset, was utilized for network training and testing. The optimal LSTM network obtained through Bayesian optimization was compared against two benchmark models: a handcrafted LSTM network and a Kalman filter with a 2D constant velocity motion model. The results demonstrate that the proposed deep learning-based framework, with robust hyperparameter selection through Bayesian optimization, yields more accurate and consistent prediction performance than the benchmark models.
{"title":"Optimized Long Short-Term Memory Network for LiDAR-Based Vehicle Trajectory Prediction Through Bayesian Optimization","authors":"Shanglian Zhou;Igor Lashkov;Hao Xu;Guohui Zhang;Yin Yang","doi":"10.1109/TITS.2024.3520317","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520317","url":null,"abstract":"In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on user expertise and prior knowledge, while newer deep learning approaches, such as Long Short-Term Memory (LSTM) networks, also face challenges related to human intervention and subjective hyperparameter selection. This study proposes a systematic approach for Light Detection and Ranging (LiDAR)-based vehicle trajectory prediction, leveraging LSTM networks to predict vehicle trajectories and employing Bayesian optimization to automatically search for optimal hyperparameter values related to both the training scheme and LSTM architectures. In the experimental study, a custom vehicle trajectory dataset extracted from roadside LiDAR data, along with the V2X-Seq-TFD dataset, was utilized for network training and testing. The optimal LSTM network obtained through Bayesian optimization was compared against two benchmark models: a handcrafted LSTM network and a Kalman filter with a 2D constant velocity motion model. The results demonstrate that the proposed deep learning-based framework, with robust hyperparameter selection through Bayesian optimization, yields more accurate and consistent prediction performance than the benchmark models.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"2988-3003"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces the Metacognitive-Attribution Inspired Reinforcement Learning (MAIRL) approach, designed to address unprotected interactive left turns at intersections—one of the most challenging tasks in autonomous driving. By integrating the Metacognitive Theory and Attribution Theory from the psychology field with reinforcement learning, this study enriches the learning mechanisms of autonomous vehicles with human cognitive processes. Specifically, it applies Metacognitive Theory’s three core elements—Metacognitive Knowledge, Metacognitive Monitoring, and Metacognitive Reflection—to enhance the control framework’s capabilities in skill differentiation, real-time assessment, and adaptive learning for interactive motion planning. Furthermore, inspired by Attribution Theory, it decomposes the reward system in RL algorithms into three components: 1) skill improvement, 2) existing ability, and 3) environmental stochasticity. This framework emulates human learning and behavior adjustment, incorporating a deeper cognitive emulation into reinforcement algorithms to foster a unified cognitive structure and control strategy. Contrastive tests conducted in various intersection scenarios with differing traffic densities demonstrated the superior performance of the proposed controller, which outperformed baseline algorithms in success rates and had lower collision and timeout incidents. This interdisciplinary approach not only enhances the understanding and applicability of RL algorithms but also represents a meaningful step towards modeling advanced human cognitive processes in the field of autonomous driving.
{"title":"Equipping With Cognition: Interactive Motion Planning Using Metacognitive-Attribution Inspired Reinforcement Learning for Autonomous Vehicles","authors":"Xiaohui Hou;Minggang Gan;Wei Wu;Yuan Ji;Shiyue Zhao;Jie Chen","doi":"10.1109/TITS.2024.3520514","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520514","url":null,"abstract":"This study introduces the Metacognitive-Attribution Inspired Reinforcement Learning (MAIRL) approach, designed to address unprotected interactive left turns at intersections—one of the most challenging tasks in autonomous driving. By integrating the Metacognitive Theory and Attribution Theory from the psychology field with reinforcement learning, this study enriches the learning mechanisms of autonomous vehicles with human cognitive processes. Specifically, it applies Metacognitive Theory’s three core elements—Metacognitive Knowledge, Metacognitive Monitoring, and Metacognitive Reflection—to enhance the control framework’s capabilities in skill differentiation, real-time assessment, and adaptive learning for interactive motion planning. Furthermore, inspired by Attribution Theory, it decomposes the reward system in RL algorithms into three components: 1) skill improvement, 2) existing ability, and 3) environmental stochasticity. This framework emulates human learning and behavior adjustment, incorporating a deeper cognitive emulation into reinforcement algorithms to foster a unified cognitive structure and control strategy. Contrastive tests conducted in various intersection scenarios with differing traffic densities demonstrated the superior performance of the proposed controller, which outperformed baseline algorithms in success rates and had lower collision and timeout incidents. This interdisciplinary approach not only enhances the understanding and applicability of RL algorithms but also represents a meaningful step towards modeling advanced human cognitive processes in the field of autonomous driving.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"4178-4191"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3521961
Ning Wang;Wei Jia;Haojun Wu;Yueying Wang
Aiming at autonomous massive transportation by sea, economically condition-based cooperative control solution remains unrevealed and is highly desirable for collective swarming of distributed unmanned surface vehicles (USVs) suffering from narrow-band communication and unstructured unknowns. In this paper, an event-triggered self-organizing swarm control (ESSC) scheme is innovated to flexibly helm a herd of USVs, and features main contributions as follows: 1) A suite of self-organizing swarm mechanism consisting of aggregation, collision avoidance and heading alignment is holistically established, such that emerging behaviors of swarm kinetics can be self-evolved for flexible morphology; 2) Within adaptive dynamic programming framework, an event-triggered optimal solution to USV swarm control is worked out by deriving optimization-oriented event-triggering mechanism from swarm kinetics tracking errors, thereby making a rational balance between channel occupation and tracking accuracy; and 3) Approximately optimal control actions are acquired by employing actor-critic reinforcement learning networks to solve Hamilton-Jacobi-Bellman equation, thereby assuring communication parsimony and control optimality, simultaneously. Performance validations with intensive comparisons to time-triggered methods demonstrate the effectiveness and superiority in terms of tracking accuracy, channel occupancy and control optimality, in addition that extensive application to roundup scenario showcases the proposed ESSC scheme performs feasible extension to wide-range tasks.
{"title":"Event-Triggered Self-Organizing Swarm Control of Distributed Unmanned Surface Vehicles","authors":"Ning Wang;Wei Jia;Haojun Wu;Yueying Wang","doi":"10.1109/TITS.2024.3521961","DOIUrl":"https://doi.org/10.1109/TITS.2024.3521961","url":null,"abstract":"Aiming at autonomous massive transportation by sea, economically condition-based cooperative control solution remains unrevealed and is highly desirable for collective swarming of distributed unmanned surface vehicles (USVs) suffering from narrow-band communication and unstructured unknowns. In this paper, an event-triggered self-organizing swarm control (ESSC) scheme is innovated to flexibly helm a herd of USVs, and features main contributions as follows: 1) A suite of self-organizing swarm mechanism consisting of aggregation, collision avoidance and heading alignment is holistically established, such that emerging behaviors of swarm kinetics can be self-evolved for flexible morphology; 2) Within adaptive dynamic programming framework, an event-triggered optimal solution to USV swarm control is worked out by deriving optimization-oriented event-triggering mechanism from swarm kinetics tracking errors, thereby making a rational balance between channel occupation and tracking accuracy; and 3) Approximately optimal control actions are acquired by employing actor-critic reinforcement learning networks to solve Hamilton-Jacobi-Bellman equation, thereby assuring communication parsimony and control optimality, simultaneously. Performance validations with intensive comparisons to time-triggered methods demonstrate the effectiveness and superiority in terms of tracking accuracy, channel occupancy and control optimality, in addition that extensive application to roundup scenario showcases the proposed ESSC scheme performs feasible extension to wide-range tasks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3431-3445"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520511
Yatao Zhang;Yi Wang;Song Gao;Martin Raubal
Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to insufficient integration frameworks and the complexity of urban contexts. This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Employing a relation-dependent integration strategy, the framework generates context-aware representations from the spatial and temporal units of CKG to capture spatio-temporal dependencies of urban contexts. A CKG-GNN model, combining the CKG, dual-view multi-head self-attention (MHSA), and graph neural network (GNN), is then designed to predict traffic speed utilizing these context-aware representations. Our experiments demonstrate that CKG’s configuration significantly influences embedding performance, with ComplEx and KG2E emerging as optimal for embedding spatial and temporal units, respectively. The CKG-GNN model establishes a benchmark for 10-120 min predictions, achieving average MAE, MAPE, and RMSE of 3.46±0.01, 14.76±0.09%, and 5.08±0.01, respectively. Compared to the baseline DCRNN model, integrating the spatial unit improves the MAE by 0.04 and the temporal unit by 0.13, while integrating both units further reduces it by 0.18. The dual-view MHSA analysis reveals the crucial role of relation-dependent features from the context-based view and the model’s ability to prioritize recent time slots in prediction from the sequence-based view. Overall, this study underscores the importance of merging context-aware knowledge graphs with graph neural networks to improve traffic forecasting.
{"title":"Context-Aware Knowledge Graph Framework for Traffic Speed Forecasting Using Graph Neural Network","authors":"Yatao Zhang;Yi Wang;Song Gao;Martin Raubal","doi":"10.1109/TITS.2024.3520511","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520511","url":null,"abstract":"Human mobility is intricately influenced by urban contexts spatially and temporally, constituting essential domain knowledge in understanding traffic systems. While existing traffic forecasting models primarily rely on raw traffic data and advanced deep learning techniques, incorporating contextual information remains underexplored due to insufficient integration frameworks and the complexity of urban contexts. This study proposes a novel context-aware knowledge graph (CKG) framework to enhance traffic speed forecasting by effectively modeling spatial and temporal contexts. Employing a relation-dependent integration strategy, the framework generates context-aware representations from the spatial and temporal units of CKG to capture spatio-temporal dependencies of urban contexts. A CKG-GNN model, combining the CKG, dual-view multi-head self-attention (MHSA), and graph neural network (GNN), is then designed to predict traffic speed utilizing these context-aware representations. Our experiments demonstrate that CKG’s configuration significantly influences embedding performance, with ComplEx and KG2E emerging as optimal for embedding spatial and temporal units, respectively. The CKG-GNN model establishes a benchmark for 10-120 min predictions, achieving average MAE, MAPE, and RMSE of 3.46±0.01, 14.76±0.09%, and 5.08±0.01, respectively. Compared to the baseline DCRNN model, integrating the spatial unit improves the MAE by 0.04 and the temporal unit by 0.13, while integrating both units further reduces it by 0.18. The dual-view MHSA analysis reveals the crucial role of relation-dependent features from the context-based view and the model’s ability to prioritize recent time slots in prediction from the sequence-based view. Overall, this study underscores the importance of merging context-aware knowledge graphs with graph neural networks to improve traffic forecasting.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3885-3902"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to advancements in intelligent transportation and the emergence of automated vehicles, interest in analyzing driver behavior to improve commuters’ driving experiences has surged. Past studies have utilized driver gaze data to analyze behavior under various driving conditions using machine learning techniques. However, exploring driver behavior through multiple modalities can provide deeper insights. To this end, we conducted a naturalistic driver behavior study with ten participants, collecting vehicular data and driver gaze measurements using standard sensors. This dataset allows for an accurate assessment of driver behavior across different road types, traffic conditions, and congestion levels. Additionally, we investigated the influence of driving experience and time of day on behavior. Experienced drivers showed greater consistency across scenarios, while novices’ performance varied based on traffic intensity and route type.
{"title":"Repeated Route Naturalistic Driver Behavior Analysis Using Motion and Gaze Measurements","authors":"Bikram Adhikari;Zoran Durić;Duminda Wijesekera;Bo Yu","doi":"10.1109/TITS.2024.3520893","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520893","url":null,"abstract":"Due to advancements in intelligent transportation and the emergence of automated vehicles, interest in analyzing driver behavior to improve commuters’ driving experiences has surged. Past studies have utilized driver gaze data to analyze behavior under various driving conditions using machine learning techniques. However, exploring driver behavior through multiple modalities can provide deeper insights. To this end, we conducted a naturalistic driver behavior study with ten participants, collecting vehicular data and driver gaze measurements using standard sensors. This dataset allows for an accurate assessment of driver behavior across different road types, traffic conditions, and congestion levels. Additionally, we investigated the influence of driving experience and time of day on behavior. Experienced drivers showed greater consistency across scenarios, while novices’ performance varied based on traffic intensity and route type.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3273-3283"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520613
Kehua Chen;Yuhao Luo;Meixin Zhu;Hai Yang
Lane changing presents a dynamic scenario characterized by intricate interactions among vehicles. Within mixed-autonomy traffic environment, modeling a human-like lane-change trajectory enables human drivers to better understand and predict autonomous vehicles’ behaviors, thereby enhancing road safety and travel efficiency. In this study, we achieve human-like interactive lane-change modeling based on a novel framework named Diff-LC. The human-like modeling of LCV behaviors relies on an advanced diffusive planner, and the implemented trajectory is selected based on the recovered LCV reward function learned through Multi-Agent Adversarial Inverse Reinforcement Learning (MA-AIRL). To account for interactions between FVs and LCVs, we further employ a diffusive predictor to forecast future behaviors of FVs conditioned on both historical and planned trajectories. Additionally, we leverage the recovered reward function of FVs to enable controllable prediction of trajectories. In the experimental part, we begin by analyzing the significance of features in the recovered reward functions and then proceed to compare the distinctions between the LCV and the FV. To validate the effectiveness of the proposed framework, we compare the diffusive predictor and planner with several state-of-the-art methods. The results demonstrate that motions planned by Diff-LC closely reach the intended positions with small displacement errors and exhibit highly similar speed and jerk distributions to those of human drivers. We also conduct a dynamic simulation to evaluate Diff-LC’s performance across different traffic conditions. Finally, we explore customized generation using the Diffusion Posterior Sampling method. The codes can be found at https://github.com/zeonchen/Diff-LC/.
{"title":"Human-Like Interactive Lane-Change Modeling Based on Reward-Guided Diffusive Predictor and Planner","authors":"Kehua Chen;Yuhao Luo;Meixin Zhu;Hai Yang","doi":"10.1109/TITS.2024.3520613","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520613","url":null,"abstract":"Lane changing presents a dynamic scenario characterized by intricate interactions among vehicles. Within mixed-autonomy traffic environment, modeling a human-like lane-change trajectory enables human drivers to better understand and predict autonomous vehicles’ behaviors, thereby enhancing road safety and travel efficiency. In this study, we achieve human-like interactive lane-change modeling based on a novel framework named Diff-LC. The human-like modeling of LCV behaviors relies on an advanced diffusive planner, and the implemented trajectory is selected based on the recovered LCV reward function learned through Multi-Agent Adversarial Inverse Reinforcement Learning (MA-AIRL). To account for interactions between FVs and LCVs, we further employ a diffusive predictor to forecast future behaviors of FVs conditioned on both historical and planned trajectories. Additionally, we leverage the recovered reward function of FVs to enable controllable prediction of trajectories. In the experimental part, we begin by analyzing the significance of features in the recovered reward functions and then proceed to compare the distinctions between the LCV and the FV. To validate the effectiveness of the proposed framework, we compare the diffusive predictor and planner with several state-of-the-art methods. The results demonstrate that motions planned by Diff-LC closely reach the intended positions with small displacement errors and exhibit highly similar speed and jerk distributions to those of human drivers. We also conduct a dynamic simulation to evaluate Diff-LC’s performance across different traffic conditions. Finally, we explore customized generation using the Diffusion Posterior Sampling method. The codes can be found at <uri>https://github.com/zeonchen/Diff-LC/</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3903-3916"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3504605
Kunyoung Lee;Hyunsoo Seo;Seunghyun Kim;Byeong Seon An;Shinwi Park;Yonggwon Jeon;Eui Chul Lee
Remote photoplethysmography (rPPG) is a method for monitoring pulse signal by utilizing a camera sensor to capture a facial video including variations in blood flow beneath the skin. Recently, rPPG advancements have enabled the measurement of an individual’s heart rate with a Root Mean Square Error (RMSE) of approximately 1.0 in controlled indoor environments. However, when applied in car dataset including driving environments, the RMSE of rPPG measurements significantly increases to over 9.07. This limitation, caused by motion-related artifacts and fluctuations in ambient illumination, becomes particularly noticeable while driving, resulting in a Percentage of Time that Error is less than 6 beats per minute (PTE6) of up to 65.1%. To address these limitations, we focus on the assessment of rPPG noise, with an emphasis on evaluating noise components within facial video and quantifying quality of the rPPG measurement. In this paper, we propose a deep learning framework that infers rPPG signal and quality based on video vision transformer. the proposed method demonstrates that the top 10% quality measurements yield PTE6 of 91.98% and 99.59% in driving and garage environments, respectively. Additionally, we introduce a quality-based rPPG compensation method that improves accuracy in driving environments by predicting rPPG quality based on noise assessment. This compensation method demonstrates superior accuracy compared to the current state-of-the-art, achieving a PTE6 of 68.24% in driving scenarios.
{"title":"Quality-Based rPPG Compensation With Temporal Difference Transformer for Camera-Based Driver Monitoring","authors":"Kunyoung Lee;Hyunsoo Seo;Seunghyun Kim;Byeong Seon An;Shinwi Park;Yonggwon Jeon;Eui Chul Lee","doi":"10.1109/TITS.2024.3504605","DOIUrl":"https://doi.org/10.1109/TITS.2024.3504605","url":null,"abstract":"Remote photoplethysmography (rPPG) is a method for monitoring pulse signal by utilizing a camera sensor to capture a facial video including variations in blood flow beneath the skin. Recently, rPPG advancements have enabled the measurement of an individual’s heart rate with a Root Mean Square Error (RMSE) of approximately 1.0 in controlled indoor environments. However, when applied in car dataset including driving environments, the RMSE of rPPG measurements significantly increases to over 9.07. This limitation, caused by motion-related artifacts and fluctuations in ambient illumination, becomes particularly noticeable while driving, resulting in a Percentage of Time that Error is less than 6 beats per minute (PTE6) of up to 65.1%. To address these limitations, we focus on the assessment of rPPG noise, with an emphasis on evaluating noise components within facial video and quantifying quality of the rPPG measurement. In this paper, we propose a deep learning framework that infers rPPG signal and quality based on video vision transformer. the proposed method demonstrates that the top 10% quality measurements yield PTE6 of 91.98% and 99.59% in driving and garage environments, respectively. Additionally, we introduce a quality-based rPPG compensation method that improves accuracy in driving environments by predicting rPPG quality based on noise assessment. This compensation method demonstrates superior accuracy compared to the current state-of-the-art, achieving a PTE6 of 68.24% in driving scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1951-1963"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520520
Siyuan Yu;Congkai Shen;James Dallas;Bogdan I. Epureanu;Paramsothy Jayakumar;Tulga Ersal
This paper presents a novel terrain-adaptive local trajectory planner designed for the autonomous operation of off-road vehicles on deformable terrains. State-of-the-art solutions either do not account for deformable terrains, or do not offer sufficient robustness or computational speed. To bridge this research gap, the paper introduces a novel model predictive control (MPC) formulation. In contrast to the prevailing state-of-the-art approaches that rely exclusively on hard or soft constraints for obstacle avoidance, the present formulation enhances robustness by incorporating both types of constraints. The effectiveness and robustness of the formulation are evaluated through extensive simulations, encompassing a wide range of randomized scenarios, and compared against state-of-the-art methods. Subsequently, the formulation is augmented with an optimal-control-oriented terramechanics model from the literature, explicitly addressing terrain deformation. Additionally, a terrain estimator employing the unscented Kalman filter is utilized to dynamically adjust the sinkage exponent online, resulting in a terrain-adaptive formulation. This formulation is tested on a physical vehicle in real world experiments against a rigid-terrain formulation as the benchmark. The results showcase the superior safety and performance achieved by the proposed formulation, underscoring the critical significance of integrating terramechanics knowledge into the planning process. Specifically, the proposed terrain-adaptive formulation achieves reduced mean absolute sideslip angle, decreased mean absolute yaw rate, shorter time to goal, and a higher success rate, primarily attributed to its enhanced understanding of terramechanics within the planner.
{"title":"A Real-Time Terrain-Adaptive Local Trajectory Planner for High-Speed Autonomous Off-Road Navigation on Deformable Terrains","authors":"Siyuan Yu;Congkai Shen;James Dallas;Bogdan I. Epureanu;Paramsothy Jayakumar;Tulga Ersal","doi":"10.1109/TITS.2024.3520520","DOIUrl":"https://doi.org/10.1109/TITS.2024.3520520","url":null,"abstract":"This paper presents a novel terrain-adaptive local trajectory planner designed for the autonomous operation of off-road vehicles on deformable terrains. State-of-the-art solutions either do not account for deformable terrains, or do not offer sufficient robustness or computational speed. To bridge this research gap, the paper introduces a novel model predictive control (MPC) formulation. In contrast to the prevailing state-of-the-art approaches that rely exclusively on hard or soft constraints for obstacle avoidance, the present formulation enhances robustness by incorporating both types of constraints. The effectiveness and robustness of the formulation are evaluated through extensive simulations, encompassing a wide range of randomized scenarios, and compared against state-of-the-art methods. Subsequently, the formulation is augmented with an optimal-control-oriented terramechanics model from the literature, explicitly addressing terrain deformation. Additionally, a terrain estimator employing the unscented Kalman filter is utilized to dynamically adjust the sinkage exponent online, resulting in a terrain-adaptive formulation. This formulation is tested on a physical vehicle in real world experiments against a rigid-terrain formulation as the benchmark. The results showcase the superior safety and performance achieved by the proposed formulation, underscoring the critical significance of integrating terramechanics knowledge into the planning process. Specifically, the proposed terrain-adaptive formulation achieves reduced mean absolute sideslip angle, decreased mean absolute yaw rate, shorter time to goal, and a higher success rate, primarily attributed to its enhanced understanding of terramechanics within the planner.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3324-3340"},"PeriodicalIF":7.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/TITS.2024.3520328
Liang Cao;Yan Qin;Yingnan Pan;Hongjing Liang
This paper considers the prescribed performance-based optimal formation control problem for unmanned surface vehicles with position constraints and yaw angle time-varying partial constraints while avoiding collisions and maintaining connectivity. To be more specific, prescribed-time performance constraints are imposed on the position tracking errors between each vehicle and its leader. Then, the prescribed performance-based optimal formation control strategy is developed to guarantee that each vehicle achieves collision-free formation control while maintaining connectivity, as well as the prescribed transient and steady performance on the position tracking errors. Inspired by the prescribed performance control, an improved asymmetric barrier function with prescribed performance is provided to ensure that the yaw angle errors satisfy the prescribed performance constraints. Eventually, theoretical analysis demonstrates that the optimal formation control scheme can produce position tracking errors that converge to a prescribed arbitrarily small region within a prescribed time interval, along with the yaw angle that adheres to the time-varying partial constraints, subject to optimal cost with limited communication ranges and collision avoidance constraints. Simulation results and comprehensive comparisons show extraordinary effectiveness and superiority.
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