Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827145
Hans-Joachim Bieg, Simon Strobel, M. Fischer, Paula Laßmann
Methods to estimate a driver’s visual attention from video images have received increased research interest. Such methods are especially important for detecting inattentive drivers in partially automated vehicles. The current study compares different driver gaze region estimation techniques, which may serve as a basis for detecting inattentive drivers. The accuracy of these techniques was evaluated on data from automated drives in a driving simulator. The examined techniques include a classical, state-of-the-art eye tracking approach, two data-driven approaches that rely on eye tracking data, a data-driven approach that only considers the driver’s facial configuration, and an end-to-end approach based on a convolutional neural network. The results showcase the advantages of data-driven approaches over a classical geometric interpretation of the eye tracking data. The results also highlight challenges regarding generalization for purely data-driven approaches and the benefits of data-driven approaches that operate on eye tracking data rather than video image data alone.
{"title":"Comparison of Video-based Driver Gaze Region Estimation Techniques","authors":"Hans-Joachim Bieg, Simon Strobel, M. Fischer, Paula Laßmann","doi":"10.1109/iv51971.2022.9827145","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827145","url":null,"abstract":"Methods to estimate a driver’s visual attention from video images have received increased research interest. Such methods are especially important for detecting inattentive drivers in partially automated vehicles. The current study compares different driver gaze region estimation techniques, which may serve as a basis for detecting inattentive drivers. The accuracy of these techniques was evaluated on data from automated drives in a driving simulator. The examined techniques include a classical, state-of-the-art eye tracking approach, two data-driven approaches that rely on eye tracking data, a data-driven approach that only considers the driver’s facial configuration, and an end-to-end approach based on a convolutional neural network. The results showcase the advantages of data-driven approaches over a classical geometric interpretation of the eye tracking data. The results also highlight challenges regarding generalization for purely data-driven approaches and the benefits of data-driven approaches that operate on eye tracking data rather than video image data alone.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130888006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827388
Hongsheng Qi, Yuyan Ying, Jiahao Zhang
The microscopic behavior of the vehicle can be decomposed into car following and lane changing, and can be described by the longitudinal and lateral movement. The longitudinal movement has long been studied, while the lateral counterpart, especially the stochastic lateral movement, has rarely been investigated. The lacking of an understanding of the lateral behavior makes current microscopic simulation results deviate from real-world observations. Besides, many behavior identification algorithms which rely on lateral displacement are not robust, if the lateral stochastic nature is not well studied. To fill in this gap, a stochastic differential equation approach is employed. Firstly, the lateral noise is modeled by a transformed Brownian motion. Then the noise is embedded into a differential lateral movement model. The parameters in the lateral noise and movement models all have clear physical meaning. The Fokker-Planck equation, which describes the distribution evolution of the lateral displacement, is derived. A parameters calibration procedure is derived using the Euler discretization scheme. The model is calibrated using real world data. The results show that the proposed model can well describe the lateral movement distribution.
{"title":"Stochastic lateral noise and movement by Brownian differential models","authors":"Hongsheng Qi, Yuyan Ying, Jiahao Zhang","doi":"10.1109/iv51971.2022.9827388","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827388","url":null,"abstract":"The microscopic behavior of the vehicle can be decomposed into car following and lane changing, and can be described by the longitudinal and lateral movement. The longitudinal movement has long been studied, while the lateral counterpart, especially the stochastic lateral movement, has rarely been investigated. The lacking of an understanding of the lateral behavior makes current microscopic simulation results deviate from real-world observations. Besides, many behavior identification algorithms which rely on lateral displacement are not robust, if the lateral stochastic nature is not well studied. To fill in this gap, a stochastic differential equation approach is employed. Firstly, the lateral noise is modeled by a transformed Brownian motion. Then the noise is embedded into a differential lateral movement model. The parameters in the lateral noise and movement models all have clear physical meaning. The Fokker-Planck equation, which describes the distribution evolution of the lateral displacement, is derived. A parameters calibration procedure is derived using the Euler discretization scheme. The model is calibrated using real world data. The results show that the proposed model can well describe the lateral movement distribution.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128696151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reasonable power distribution between battery and supercapacitor in electric vehicles is a crucial problem to improve energy consumption and economy. An online energy management strategy based on model predictive control (MPC) is proposed in this paper. Firstly, a radial basis function neural network optimized by particle swarm algorithm is presented to generate the short-term future velocity, i.e., the reference trajectory of the MPC. Then, a cost function considering the battery degradation cost and the electricity cost is constructed and optimized within each prediction horizon while maintaining the state of charge of the supercapacitor. Simulation results on the UDDS driving cycle show that the total cost of the proposed strategy is reduced by 6.3% and 3.9% compared with the near-optimal rule-based strategy and the none optimized velocity predictor-MPC, respectively, indicating that the velocity prediction accuracy has a significant impact on the performance of real-time energy management.
{"title":"Energy Management Strategy for Hybrid Energy Storage System using Optimized Velocity Predictor and Model Predictive Control","authors":"Zhiwu Huang, Pei Huang, Yue Wu, Heng Li, Hui Peng, Jun Peng","doi":"10.1109/iv51971.2022.9827322","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827322","url":null,"abstract":"Reasonable power distribution between battery and supercapacitor in electric vehicles is a crucial problem to improve energy consumption and economy. An online energy management strategy based on model predictive control (MPC) is proposed in this paper. Firstly, a radial basis function neural network optimized by particle swarm algorithm is presented to generate the short-term future velocity, i.e., the reference trajectory of the MPC. Then, a cost function considering the battery degradation cost and the electricity cost is constructed and optimized within each prediction horizon while maintaining the state of charge of the supercapacitor. Simulation results on the UDDS driving cycle show that the total cost of the proposed strategy is reduced by 6.3% and 3.9% compared with the near-optimal rule-based strategy and the none optimized velocity predictor-MPC, respectively, indicating that the velocity prediction accuracy has a significant impact on the performance of real-time energy management.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115934452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827385
Zhuojian Cao, Jiang Liu, Wei Jiang, B. Cai, J. Wang
GNSS (Global Navigation Satellite System) is virtually becoming an autonomous train localization technology for the next-generation train control system. However, potential threats from the intentional interference may severely degrade the availability of GNSS due to its vulnerability. It is of great significance to detect and isolate the negative effects from GNSS interference for the Train Control System (TCS) in the railway field. For the protection against GNSS jamming, extra information from the Inertial Navigation System (INS) and odometer are involved, and an INS/odometer/trackmap-aided GNSS localization method for railway trains is raised in this paper. While the GNSS receiver cannot identify the real signals under a high-power jamming attack condition, a prediction deduced train position generation approach is proposed. In this strategy, velocity from the odometer and the geospatial constraint from the trackmap are involved to calibrate INS, with which continuous positioning is realized under a GNSS-denied situation. Furthermore, while the measurements degradation occurs caused by a relatively low power jamming, a residual-test-based detection solution based on the deviation between the predicted reference pseudo-ranges and the real ones is proposed to isolate degraded measurements. Results from an experiment under a GPS jamming condition demonstrate that the proposed solution outperforms the GPS Single Point Positioning (SPP) and the conventional GPS/INS method. The jamming protection and continuous positioning performance under specific jamming conditions enhance the capability of resilient train positioning.
{"title":"INS/Odometer/Trackmap-aided Railway Train Localization under GNSS Jamming Conditions","authors":"Zhuojian Cao, Jiang Liu, Wei Jiang, B. Cai, J. Wang","doi":"10.1109/iv51971.2022.9827385","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827385","url":null,"abstract":"GNSS (Global Navigation Satellite System) is virtually becoming an autonomous train localization technology for the next-generation train control system. However, potential threats from the intentional interference may severely degrade the availability of GNSS due to its vulnerability. It is of great significance to detect and isolate the negative effects from GNSS interference for the Train Control System (TCS) in the railway field. For the protection against GNSS jamming, extra information from the Inertial Navigation System (INS) and odometer are involved, and an INS/odometer/trackmap-aided GNSS localization method for railway trains is raised in this paper. While the GNSS receiver cannot identify the real signals under a high-power jamming attack condition, a prediction deduced train position generation approach is proposed. In this strategy, velocity from the odometer and the geospatial constraint from the trackmap are involved to calibrate INS, with which continuous positioning is realized under a GNSS-denied situation. Furthermore, while the measurements degradation occurs caused by a relatively low power jamming, a residual-test-based detection solution based on the deviation between the predicted reference pseudo-ranges and the real ones is proposed to isolate degraded measurements. Results from an experiment under a GPS jamming condition demonstrate that the proposed solution outperforms the GPS Single Point Positioning (SPP) and the conventional GPS/INS method. The jamming protection and continuous positioning performance under specific jamming conditions enhance the capability of resilient train positioning.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122594337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827351
Thomas Genevois, Jean-Baptiste Horel, A. Renzaglia, C. Laugier
Testing and validating advanced automotive software is of paramount importance to guarantee safety and quality. While real-world testing is highly demanding and simulation testing is not reliable, we propose a new augmented reality framework that takes advantage of both environments. This new testing methodology is intended to be a bridge between Vehicle-in-the-Loop and real-world testing. It enables to easily and safely place the whole vehicle and all its software, from perception to control, in realistic test conditions. This framework provides a flexible way to introduce any virtual element in the outputs of the sensors of the vehicle under test. For each modality of sensing, the framework requires a real time augmentation function that preserves real sensor data and enhances them with virtual data. The LiDAR data augmentation function is presented together with its implementation details. Relying on both qualitative and quantitative analysis of experimental results, the representability of tests scenes generated by the augmented reality framework is finally proven.
{"title":"Augmented Reality on LiDAR data: Going beyond Vehicle-in-the-Loop for Automotive Software Validation","authors":"Thomas Genevois, Jean-Baptiste Horel, A. Renzaglia, C. Laugier","doi":"10.1109/iv51971.2022.9827351","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827351","url":null,"abstract":"Testing and validating advanced automotive software is of paramount importance to guarantee safety and quality. While real-world testing is highly demanding and simulation testing is not reliable, we propose a new augmented reality framework that takes advantage of both environments. This new testing methodology is intended to be a bridge between Vehicle-in-the-Loop and real-world testing. It enables to easily and safely place the whole vehicle and all its software, from perception to control, in realistic test conditions. This framework provides a flexible way to introduce any virtual element in the outputs of the sensors of the vehicle under test. For each modality of sensing, the framework requires a real time augmentation function that preserves real sensor data and enhances them with virtual data. The LiDAR data augmentation function is presented together with its implementation details. Relying on both qualitative and quantitative analysis of experimental results, the representability of tests scenes generated by the augmented reality framework is finally proven.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126517777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827210
T. Fleischer, M. Puhe, J. Schippl, Yukari Yamasaki
Social acceptance is seen as an important prerequisite for a successful adoption and diffusion of automated driving technologies and services. The paper presents a proposal about how social acceptance could be conceptualized and argues that expectations and promises regarding the role of autonomous driving in future mobility systems should play an important role here. It then provides first results of a representative survey among German citizens, focusing on their individual expectations on the longer-term effects of the widespread use of autonomous road vehicles as well as on their opinions on changes of framework conditions and regulations associated with their diffusion and deployment.
{"title":"Public Expectations Regarding the Longer-term Implications of and Regulatory Changes for Autonomous Driving: A Contribution to the Debate on its Social Acceptance","authors":"T. Fleischer, M. Puhe, J. Schippl, Yukari Yamasaki","doi":"10.1109/iv51971.2022.9827210","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827210","url":null,"abstract":"Social acceptance is seen as an important prerequisite for a successful adoption and diffusion of automated driving technologies and services. The paper presents a proposal about how social acceptance could be conceptualized and argues that expectations and promises regarding the role of autonomous driving in future mobility systems should play an important role here. It then provides first results of a representative survey among German citizens, focusing on their individual expectations on the longer-term effects of the widespread use of autonomous road vehicles as well as on their opinions on changes of framework conditions and regulations associated with their diffusion and deployment.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129109535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827105
Zeyu Mu, Zheng Chen, Seunghan Ryu, S. Avedisov, Rui Guo, B. Park
In this paper, we showcase a framework for cooperative mixed traffic platooning that allows the platooning vehicles to realize multiple benefits from using vehicle-to-everything (V2X) communications and advanced controls on urban arterial roads. A mixed traffic platoon, in general, can be formulated by a lead and ego connected automated vehicles (CAVs) with one or more unconnected human-driven vehicles (UHVs) in between. As this platoon approaches an intersection, the lead vehicle uses signal phase and timing (SPaT) messages from the connected intersection to optimize its trajectory for travel time and energy efficiency as it passes through the intersection. These benefits carry over to the UHVs and the ego vehicle as they follow the lead vehicle. The ego vehicle then uses information from the lead vehicle received through basic safety messages (BSMs) to further optimize its safety, driving comfort, and energy consumption. This is accomplished by the recently designed cooperative adaptive cruise control with unconnected vehicles (CACCu). The performance benefits of our framework are proven and demonstrated by simulations using real-world platooning data from the CACC Field Operation Test (FOT) Dataset from the Netherlands.
{"title":"Cooperative Platooning with Mixed Traffic on Urban Arterial Roads","authors":"Zeyu Mu, Zheng Chen, Seunghan Ryu, S. Avedisov, Rui Guo, B. Park","doi":"10.1109/iv51971.2022.9827105","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827105","url":null,"abstract":"In this paper, we showcase a framework for cooperative mixed traffic platooning that allows the platooning vehicles to realize multiple benefits from using vehicle-to-everything (V2X) communications and advanced controls on urban arterial roads. A mixed traffic platoon, in general, can be formulated by a lead and ego connected automated vehicles (CAVs) with one or more unconnected human-driven vehicles (UHVs) in between. As this platoon approaches an intersection, the lead vehicle uses signal phase and timing (SPaT) messages from the connected intersection to optimize its trajectory for travel time and energy efficiency as it passes through the intersection. These benefits carry over to the UHVs and the ego vehicle as they follow the lead vehicle. The ego vehicle then uses information from the lead vehicle received through basic safety messages (BSMs) to further optimize its safety, driving comfort, and energy consumption. This is accomplished by the recently designed cooperative adaptive cruise control with unconnected vehicles (CACCu). The performance benefits of our framework are proven and demonstrated by simulations using real-world platooning data from the CACC Field Operation Test (FOT) Dataset from the Netherlands.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133611706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827225
Jingo Adachi, Hiroshi Tsukahara, N. Mizuno, Akira Yoshizawa
In order to meet the demand for safety, usability, comfortability, and entertainment for rear seat passenger service, we introduce Skeleton motion dataset of Vehicle Rear seat Passenger (SVRP) which is a world first skeleton motion dataset for rear seat passenger with 22 different actions publicly available†. The dataset was trained and tested by a neural network with CTR-GCN [10] for action inference. The result shows the accuracy is 78.3 percent for 25 joint 2D skeleton and 80.2 percent for 32 joint 3D skeleton by sliding 4 second observation window. We also found that a longer observation window is crucial for a stable inference while time frame resolution can be reduced to 5 frames per second for lightweight computation without much accuracy drop. The number of skeleton joints can be also reduced with same accuracy from 25 points to 10 points, which is a mostly upper body part, by a proposed heatmap correlation method.†SVRP dataset available at conference on web https://github.com/DensoITLab/pvi
{"title":"Action Inference of Rear Seat Passenger for In-Vehicle Service","authors":"Jingo Adachi, Hiroshi Tsukahara, N. Mizuno, Akira Yoshizawa","doi":"10.1109/iv51971.2022.9827225","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827225","url":null,"abstract":"In order to meet the demand for safety, usability, comfortability, and entertainment for rear seat passenger service, we introduce Skeleton motion dataset of Vehicle Rear seat Passenger (SVRP) which is a world first skeleton motion dataset for rear seat passenger with 22 different actions publicly available†. The dataset was trained and tested by a neural network with CTR-GCN [10] for action inference. The result shows the accuracy is 78.3 percent for 25 joint 2D skeleton and 80.2 percent for 32 joint 3D skeleton by sliding 4 second observation window. We also found that a longer observation window is crucial for a stable inference while time frame resolution can be reduced to 5 frames per second for lightweight computation without much accuracy drop. The number of skeleton joints can be also reduced with same accuracy from 25 points to 10 points, which is a mostly upper body part, by a proposed heatmap correlation method.†SVRP dataset available at conference on web https://github.com/DensoITLab/pvi","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130605379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827379
Mohamed Radjeb Oudainia, C. Sentouh, Anh‐Tu Nguyen, J. Popieul
The work described in this paper proposes a new dynamic conflict attenuation strategy in driving shared control for intelligent vehicles lane keeping systems (LKS). This strategy takes into account the activity and availability of the driver as well as the external risk and conflict between the driver and the control system in order to manage and adapt the level of assistance in real time. The design of an adaptive shared controller is based on a dynamic multi-objective cost function that changes according to the level of assistance. Based on Lyapunov stability arguments, the global asymptotical stability of the closed-loop control system with the adaptive cost function and the variation in vehicle speed is proven and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, for different scenarios show the importance of adapting the controller in real time in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver.
{"title":"Dynamic Conflict Mitigation for Cooperative Driving Control of Intelligent Vehicles","authors":"Mohamed Radjeb Oudainia, C. Sentouh, Anh‐Tu Nguyen, J. Popieul","doi":"10.1109/iv51971.2022.9827379","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827379","url":null,"abstract":"The work described in this paper proposes a new dynamic conflict attenuation strategy in driving shared control for intelligent vehicles lane keeping systems (LKS). This strategy takes into account the activity and availability of the driver as well as the external risk and conflict between the driver and the control system in order to manage and adapt the level of assistance in real time. The design of an adaptive shared controller is based on a dynamic multi-objective cost function that changes according to the level of assistance. Based on Lyapunov stability arguments, the global asymptotical stability of the closed-loop control system with the adaptive cost function and the variation in vehicle speed is proven and an LMI optimization is used to formulate the control design. The simulation results, conducted with the SHERPA dynamic car simulator under real-world driving situations, for different scenarios show the importance of adapting the controller in real time in order to decrease the conflict between the driver and the lane keeping system and to ensure the safety of the vehicle as well as to increase the confidence and acceptability of the driver.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"108 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113969412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827441
Yafu Tian, Alexander Carballo, Rui Li, K. Takeda
Reproducing real-world traffic scenes in the simulator is fundamental to training self-driving systems. Creating a simulation scenario is a complex task, generally done manually: the ego-vehicle and other entities are placed and their trajectories defined, trying to recreate some situation found in real traffic. To reduce the manual burden, here we propose the Real-to-Synthetic toolset. This toolset provides synthetic traffic scene in openDrive format, which can be directly simulated in many simulators such as SUMO or CARLA. Also, we provide a scene generator which generates near-realistic scene from minimum user effort. To maintain the similarity between real-world scene and generated one, here we introduce the concept “Road Scene Graph”(RSG). In this graph, nodes represent entities while edges stand for pairwise relationships. These relationships could be maintained in the scene generation process while the actor is generated according to the distribution sampled from real-world data. Experiments proved that by using “Road Scene Graph”, our scene generator proposes a much more convenient way to conFigure traffic scenes rather than manually defining every actor’s initial status and trajectories.
在模拟器中再现真实的交通场景是训练自动驾驶系统的基础。创建模拟场景是一项复杂的任务，通常是手动完成的:放置自我车辆和其他实体并定义其轨迹，试图重现真实交通中的一些情况。为了减少手工负担，我们在这里提出了Real-to-Synthetic工具集。该工具集提供了openDrive格式的合成交通场景，可以在SUMO或CARLA等许多模拟器中直接模拟。此外，我们还提供了一个场景生成器，可以从最小的用户工作量中生成接近真实的场景。为了保持真实场景与生成场景之间的相似性，我们在这里引入了“道路场景图”(Road scene Graph, RSG)的概念。在这个图中，节点代表实体，而边代表成对关系。这些关系可以在场景生成过程中保持，而演员是根据从现实世界数据中采样的分布来生成的。实验证明，通过使用“道路场景图”，我们的场景生成器提供了一种更方便的方式来配置交通场景，而不是手动定义每个参与者的初始状态和轨迹。
{"title":"Real-to-Synthetic: Generating Simulator Friendly Traffic Scenes from Graph Representation","authors":"Yafu Tian, Alexander Carballo, Rui Li, K. Takeda","doi":"10.1109/iv51971.2022.9827441","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827441","url":null,"abstract":"Reproducing real-world traffic scenes in the simulator is fundamental to training self-driving systems. Creating a simulation scenario is a complex task, generally done manually: the ego-vehicle and other entities are placed and their trajectories defined, trying to recreate some situation found in real traffic. To reduce the manual burden, here we propose the Real-to-Synthetic toolset. This toolset provides synthetic traffic scene in openDrive format, which can be directly simulated in many simulators such as SUMO or CARLA. Also, we provide a scene generator which generates near-realistic scene from minimum user effort. To maintain the similarity between real-world scene and generated one, here we introduce the concept “Road Scene Graph”(RSG). In this graph, nodes represent entities while edges stand for pairwise relationships. These relationships could be maintained in the scene generation process while the actor is generated according to the distribution sampled from real-world data. Experiments proved that by using “Road Scene Graph”, our scene generator proposes a much more convenient way to conFigure traffic scenes rather than manually defining every actor’s initial status and trajectories.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121478529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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