Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827381
Lie Guo, Liang Huang, Yibing Zhao
In LiDAR-based point cloud, objects are always represented as 3D bounding boxes with direction. LiDAR-based object detection task is similar to image-based task but comes with additional challenges. In LiDAR-based detection for autonomous vehicles, the size of 3D object is significant smaller compared with size of input scene represented by point cloud, thus conventional 3D backbones cannot effectively preserve detail geometric information of object with only a few points. To resolve this problem, this paper presents a MBConv Submanifold module, which is simple and effective for voxel-based detector from point cloud. The novel convolution architecture introduces inverted bottleneck and residual connection into 3D sparse backbone, which enable detector to learn high dimension feature from point cloud. Experiments shows that MBConv Submanifold module bring consistent improvement over the baseline method: MBConv Submanifold achieves the AP of 68.03% and 54.74% in the moderate cyclist and pedestrian category on the KITTI validation benchmark, surpass the baseline method significantly. Our code and pretrained models are available at: https://github.com/s1mpleee/ResMBSubmanifold.
{"title":"Residual MBConv Submanifold Module for 3D LiDAR-based Object Detection","authors":"Lie Guo, Liang Huang, Yibing Zhao","doi":"10.1109/iv51971.2022.9827381","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827381","url":null,"abstract":"In LiDAR-based point cloud, objects are always represented as 3D bounding boxes with direction. LiDAR-based object detection task is similar to image-based task but comes with additional challenges. In LiDAR-based detection for autonomous vehicles, the size of 3D object is significant smaller compared with size of input scene represented by point cloud, thus conventional 3D backbones cannot effectively preserve detail geometric information of object with only a few points. To resolve this problem, this paper presents a MBConv Submanifold module, which is simple and effective for voxel-based detector from point cloud. The novel convolution architecture introduces inverted bottleneck and residual connection into 3D sparse backbone, which enable detector to learn high dimension feature from point cloud. Experiments shows that MBConv Submanifold module bring consistent improvement over the baseline method: MBConv Submanifold achieves the AP of 68.03% and 54.74% in the moderate cyclist and pedestrian category on the KITTI validation benchmark, surpass the baseline method significantly. Our code and pretrained models are available at: https://github.com/s1mpleee/ResMBSubmanifold.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"9 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":"130238802","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/iv51971.2022.9827297
Alejandro Diaz-Diaz, M. Ocaña, A. Llamazares, Carlos Gómez Huélamo, P. Revenga, L. Bergasa
Autonomous vehicle (AV) is one of the most challenging engineering tasks of our era. High-Definition (HD) maps are a fundamental tool in the development of AVs, being considered as pseudo sensors that provide a trusted baseline that other sensors cannot. Our approach is focused on the use of OpenDRIVE standard based HD maps in order to conduct the different mapping and planning tasks involved in Autonomous Driving (AD). In this paper we present a method for exploiting the HD map potential for two specific purposes: i) Global Path Planning and ii) Monitoring the relevant lanes and regulatory elements around the ego-vehicle to support the perception module. Mapping and planning modules are connected to the other modules of the AV stack by using ROS (Robot Operating System). Our AD architecture has been validated both in local and CARLA Autonomous Driving Leaderboard cloud, where we can appreciate a considerable improvement in the metrics by incorporating information from the HD map, not only used to conduct the Global Path Planning task but also providing prior information to the Perception module. Code is available in https://github.com/AlejandroDiazD/opendrive-mapping-planning.
{"title":"HD maps: Exploiting OpenDRIVE potential for Path Planning and Map Monitoring","authors":"Alejandro Diaz-Diaz, M. Ocaña, A. Llamazares, Carlos Gómez Huélamo, P. Revenga, L. Bergasa","doi":"10.1109/iv51971.2022.9827297","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827297","url":null,"abstract":"Autonomous vehicle (AV) is one of the most challenging engineering tasks of our era. High-Definition (HD) maps are a fundamental tool in the development of AVs, being considered as pseudo sensors that provide a trusted baseline that other sensors cannot. Our approach is focused on the use of OpenDRIVE standard based HD maps in order to conduct the different mapping and planning tasks involved in Autonomous Driving (AD). In this paper we present a method for exploiting the HD map potential for two specific purposes: i) Global Path Planning and ii) Monitoring the relevant lanes and regulatory elements around the ego-vehicle to support the perception module. Mapping and planning modules are connected to the other modules of the AV stack by using ROS (Robot Operating System). Our AD architecture has been validated both in local and CARLA Autonomous Driving Leaderboard cloud, where we can appreciate a considerable improvement in the metrics by incorporating information from the HD map, not only used to conduct the Global Path Planning task but also providing prior information to the Perception module. Code is available in https://github.com/AlejandroDiazD/opendrive-mapping-planning.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"7 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":"129299029","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/iv51971.2022.9827191
A. Abdo, Guoyuan Wu, Qi Zhu, Nael B. Abu-Ghazaleh
Connected vehicle (CV) applications promise to revolutionize our transportation systems, improving safety and traffic capacity while reducing environmental footprint. Many CV applications have been proposed towards these goals, with the US Department of Transportation (USDOT) recently initiating some designated deployment sites to enable experimentation and validation. While the focus of this initial development effort is on demonstrating the functionality of a range of proposed applications, recent attacks have demonstrated their vulnerability to application level attacks. In these attacks, a malicious actor operates within the application’s parameters but providing falsified information. This paper explores a framework that protects against such application-level attacks. Then, we analyze the impact of the attacks, showing that an individual attacker can have substantial effects on the safety and efficiency of traffic flow even in the presence of message security standards developed by USDOT, motivating the need for our defense. Our defense relies on physically modeling the vehicles and their interaction using dynamic models and state estimation filters as well as reinforcement learning. It combines these observations with knowledge of application rules and guidelines to capture logic deviations. We demonstrate that the resultant defense, called CVGuard, can accurately and promptly detect attacks, with low false positive rates over a range of attack scenarios for different CV applications.
{"title":"CVGuard: Mitigating Application Attacks on Connected Vehicles","authors":"A. Abdo, Guoyuan Wu, Qi Zhu, Nael B. Abu-Ghazaleh","doi":"10.1109/iv51971.2022.9827191","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827191","url":null,"abstract":"Connected vehicle (CV) applications promise to revolutionize our transportation systems, improving safety and traffic capacity while reducing environmental footprint. Many CV applications have been proposed towards these goals, with the US Department of Transportation (USDOT) recently initiating some designated deployment sites to enable experimentation and validation. While the focus of this initial development effort is on demonstrating the functionality of a range of proposed applications, recent attacks have demonstrated their vulnerability to application level attacks. In these attacks, a malicious actor operates within the application’s parameters but providing falsified information. This paper explores a framework that protects against such application-level attacks. Then, we analyze the impact of the attacks, showing that an individual attacker can have substantial effects on the safety and efficiency of traffic flow even in the presence of message security standards developed by USDOT, motivating the need for our defense. Our defense relies on physically modeling the vehicles and their interaction using dynamic models and state estimation filters as well as reinforcement learning. It combines these observations with knowledge of application rules and guidelines to capture logic deviations. We demonstrate that the resultant defense, called CVGuard, can accurately and promptly detect attacks, with low false positive rates over a range of attack scenarios for different CV applications.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"11 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":"130035055","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}
Pub Date : 2022-06-05DOI: 10.1109/iv51971.2022.9827022
Hao Yang, Y. Farid, K. Oguchi
Vehicle incidents or anomalous slow/stopping vehicles will generate non-recurrent queues and partially block roads. The queues will result in unbalanced lane-level traffic, and the large speed differences among lanes increase the difficulty for the queued vehicles to make lane changes to avoid downstream congestion. In this paper, a centralized non-recurrent queue management (C-NRQM) system is implemented to assist connected vehicles around non-recurrent queues with advisory speed and lane changing instructions to mitigate road congestion as well as to minimize the travel time delay and risk of collisions of all vehicles. A systematic evaluation of the system is conducted with microscopic traffic simulations to assess its mobility and safety benefits under different market penetration rates (MPRs) of connected vehicles. The socially responsibility of the system on the fairness of all road users and its performance under a competing environment with different connected vehicle applications are also evaluated to illustrate its real-world implementations in the future transportation systems. The system can reduces travel time delay by more than 80% for road with medium congestion, and more than 50% for more congested roads. Also, the system evaluation demonstrates that the centralized management has a distinct advantage on improving network performance at high MPRs of connected vehicles and eliminating the negative impact of the competition of different mobility services
{"title":"Systematic Evaluation of A Centralized Non-Recurrent Queue Management System","authors":"Hao Yang, Y. Farid, K. Oguchi","doi":"10.1109/iv51971.2022.9827022","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827022","url":null,"abstract":"Vehicle incidents or anomalous slow/stopping vehicles will generate non-recurrent queues and partially block roads. The queues will result in unbalanced lane-level traffic, and the large speed differences among lanes increase the difficulty for the queued vehicles to make lane changes to avoid downstream congestion. In this paper, a centralized non-recurrent queue management (C-NRQM) system is implemented to assist connected vehicles around non-recurrent queues with advisory speed and lane changing instructions to mitigate road congestion as well as to minimize the travel time delay and risk of collisions of all vehicles. A systematic evaluation of the system is conducted with microscopic traffic simulations to assess its mobility and safety benefits under different market penetration rates (MPRs) of connected vehicles. The socially responsibility of the system on the fairness of all road users and its performance under a competing environment with different connected vehicle applications are also evaluated to illustrate its real-world implementations in the future transportation systems. The system can reduces travel time delay by more than 80% for road with medium congestion, and more than 50% for more congested roads. Also, the system evaluation demonstrates that the centralized management has a distinct advantage on improving network performance at high MPRs of connected vehicles and eliminating the negative impact of the competition of different mobility services","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"234 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":"114260728","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/iv51971.2022.9827379
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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：