Pub Date : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334558
Nikos Piperigkos, A. Lalos, K. Berberidis, C. Anagnostopoulos
Cooperative Localization is expected to play a crucial role in various applications in the field of Connected and Autonomous vehicles (CAVs). Future 5G wireless systems are expected to enable cost-effective Vehicle-to-Everything (V2X) systems, allowing CAVs to share with the other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance and azimuth angle to neighbouring vehicles, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighbouring vehicles. This method is based on: i) the Laplacian Processing, a Graph Signal Processing tool that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system and ii) the temporal coherence due to motion patterns of the moving vehicles.
{"title":"Cooperative Multi-Modal Localization in Connected and Autonomous Vehicles","authors":"Nikos Piperigkos, A. Lalos, K. Berberidis, C. Anagnostopoulos","doi":"10.1109/CAVS51000.2020.9334558","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334558","url":null,"abstract":"Cooperative Localization is expected to play a crucial role in various applications in the field of Connected and Autonomous vehicles (CAVs). Future 5G wireless systems are expected to enable cost-effective Vehicle-to-Everything (V2X) systems, allowing CAVs to share with the other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance and azimuth angle to neighbouring vehicles, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighbouring vehicles. This method is based on: i) the Laplacian Processing, a Graph Signal Processing tool that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system and ii) the temporal coherence due to motion patterns of the moving vehicles.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128399389","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 : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334630
Kenan Ahmic, Anel Tahirbegović, A. Tahirovic, D. Watzenig, G. Stettinger
The role of autonomous cooperative vehicles will undoubtedly be important in Intelligent Transportation Systems (ITS) to increase both the safety and the overall efficiency of a high traffic network system. An autonomous platooning provides one promising strategy for decreasing total fuel consumption of a fleet of vehicles and potential risk of accidents, especially during long-distance transportation. In this work, we provide a proof-of-concept for a simulation framework in which it is possible to simulate platoon and other multi-vehicle systems using realistic vehicle models within different traffic scenarios, which is based on ROS, Gazebo and SUMO. The framework enables an easy-to-use perception and control modules of the autonomous driving stack for a realistic vehicle models, while preserving a convenient setup of different high traffic platooning scenarios. Consequently, it provides a platooning design step for conducting reliable development analyses and a platform for comparisons of different platooning strategies. We illustrate the effectiveness of the proposed platooning framework through three typical scenarios using a distributed model predictive control scheme with a platoon consisted of Toyota Prius car models.
{"title":"Simulation Framework for Platooning based on Gazebo and SUMO","authors":"Kenan Ahmic, Anel Tahirbegović, A. Tahirovic, D. Watzenig, G. Stettinger","doi":"10.1109/CAVS51000.2020.9334630","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334630","url":null,"abstract":"The role of autonomous cooperative vehicles will undoubtedly be important in Intelligent Transportation Systems (ITS) to increase both the safety and the overall efficiency of a high traffic network system. An autonomous platooning provides one promising strategy for decreasing total fuel consumption of a fleet of vehicles and potential risk of accidents, especially during long-distance transportation. In this work, we provide a proof-of-concept for a simulation framework in which it is possible to simulate platoon and other multi-vehicle systems using realistic vehicle models within different traffic scenarios, which is based on ROS, Gazebo and SUMO. The framework enables an easy-to-use perception and control modules of the autonomous driving stack for a realistic vehicle models, while preserving a convenient setup of different high traffic platooning scenarios. Consequently, it provides a platooning design step for conducting reliable development analyses and a platform for comparisons of different platooning strategies. We illustrate the effectiveness of the proposed platooning framework through three typical scenarios using a distributed model predictive control scheme with a platoon consisted of Toyota Prius car models.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123860707","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 : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334649
C. N. Mokogwu, K. Hashtrudi-Zaad
String stability is a vital property of vehicle platoons which ensures disturbances of system states are not amplified along the string of vehicles. In this paper, we discuss string stability in the frequency domain showing the platoon as a cascade of linear time-invariant subsystems. We show that a sufficient condition for string stability is that the Nyquist plot of the transfer function of each subsystem is within the unit circle. Also, an energy-based notion of string stability based on passivity is derived by showing output strict passivity as a bridge between passivity and string stability of a vehicle platoon. By constraining the output strict passivity index of each subsystem (vehicle) in a string to one or greater than one through proper control strategy, the condition for string stability is met. This is validated through numerical simulations.
{"title":"Platoon String Stability: A Passivity Perspective","authors":"C. N. Mokogwu, K. Hashtrudi-Zaad","doi":"10.1109/CAVS51000.2020.9334649","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334649","url":null,"abstract":"String stability is a vital property of vehicle platoons which ensures disturbances of system states are not amplified along the string of vehicles. In this paper, we discuss string stability in the frequency domain showing the platoon as a cascade of linear time-invariant subsystems. We show that a sufficient condition for string stability is that the Nyquist plot of the transfer function of each subsystem is within the unit circle. Also, an energy-based notion of string stability based on passivity is derived by showing output strict passivity as a bridge between passivity and string stability of a vehicle platoon. By constraining the output strict passivity index of each subsystem (vehicle) in a string to one or greater than one through proper control strategy, the condition for string stability is met. This is validated through numerical simulations.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"92 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128864505","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 : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334597
J. Villagrá, Antonio Artuñedo, Vinicius Trentin, Jorge Godoy
To make the massive deployment of automated vehicles possible in complex urban environments, it is essential to provide them with the ability of making safe and useful decisions. To that end, it is necessary to improve their capability to infer the intentions of the surrounding vehicles and their associated collision risk for the ego-vehicle in complex driving scenes. This work shows the implementation and validation in simulation of a probabilistic approach to estimate the risk of driving under uncertain conditions, combining (i) intention estimations and (ii) the expected behaviour of vehicles according to the topology and the subsequent traffic rules of the considered driving scenario. Promising results in terms of success rate and prediction horizon have been obtained testing the proposed approach in driving situations where lateral intention estimation is relevant, namely in multi-lane roundabouts and highways.
{"title":"Interaction-aware risk assessment: focus on the lateral intention","authors":"J. Villagrá, Antonio Artuñedo, Vinicius Trentin, Jorge Godoy","doi":"10.1109/CAVS51000.2020.9334597","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334597","url":null,"abstract":"To make the massive deployment of automated vehicles possible in complex urban environments, it is essential to provide them with the ability of making safe and useful decisions. To that end, it is necessary to improve their capability to infer the intentions of the surrounding vehicles and their associated collision risk for the ego-vehicle in complex driving scenes. This work shows the implementation and validation in simulation of a probabilistic approach to estimate the risk of driving under uncertain conditions, combining (i) intention estimations and (ii) the expected behaviour of vehicles according to the topology and the subsequent traffic rules of the considered driving scenario. Promising results in terms of success rate and prediction horizon have been obtained testing the proposed approach in driving situations where lateral intention estimation is relevant, namely in multi-lane roundabouts and highways.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129994146","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 : 2020-11-01DOI: 10.1109/cavs51000.2020.9334655
{"title":"Welcome from the General and Technical Program Co-chairs","authors":"","doi":"10.1109/cavs51000.2020.9334655","DOIUrl":"https://doi.org/10.1109/cavs51000.2020.9334655","url":null,"abstract":"","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116724863","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 : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334621
Fuguo Xu, T. Shen
This paper developed a look-ahead horizon based optimal control scheme to jointly improve the efficiencies of powertrain and vehicle for hybrid electric vehicles (HEVs) with connectivity and automated driving. Both a speed planning strategy and energy management strategy is provided by the proposed approach. A constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-distance between ego vehicle and preceding vehicle. The optimal solution is derived through the Pontryagins maximum principle and verified in a traffic-in-the-loop powertrain simulation platform to show the effectiveness of the proposed approach.
{"title":"Look-ahead Horizon based Energy Optimization for Connected Hybrid Electric Vehicles","authors":"Fuguo Xu, T. Shen","doi":"10.1109/CAVS51000.2020.9334621","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334621","url":null,"abstract":"This paper developed a look-ahead horizon based optimal control scheme to jointly improve the efficiencies of powertrain and vehicle for hybrid electric vehicles (HEVs) with connectivity and automated driving. Both a speed planning strategy and energy management strategy is provided by the proposed approach. A constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-distance between ego vehicle and preceding vehicle. The optimal solution is derived through the Pontryagins maximum principle and verified in a traffic-in-the-loop powertrain simulation platform to show the effectiveness of the proposed approach.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121125493","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 : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334646
Lino Antoni Giefer, Razieh Khamsehashari, K. Schill
The perception ability of automated systems such as autonomous cars plays an outstanding role for safe and reliable functionality. With the continuously growing accuracy of deep neural networks for object detection on one side and the investigation of appropriate space representations for object tracking on the other side both essential perception parts received special research attention within the last years. However, early fusion of multiple sensors turns the determination of suitable measurement spaces into a complex and not trivial task. In this paper, we propose the use of a deep multi-modal object detection network for the early fusion of LiDAR and camera data to serve as a measurement source for an extended object tracking algorithm on Lie groups. We develop an extended Kalman filter and model the state space as the direct product Aff(2) × ℝ6 incorporating second- and third-order dynamics. We compare the tracking performance of different measurement space representations-SO(2) × ℝ4, SO(2)2 × ℝ3 and Aff(2)-to evaluate, how our object detection network encapsulates the measurement parameters and the associated uncertainties. With our results, we show that the lowest tracking errors in the case of single object tracking are obtained by representing the measurement space by the affine group. Thus, we assume that our proposed object detection network captures the intrinsic relationships between the measurement parameters, especially between position and orientation.
{"title":"Evaluation of Measurement Space Representations of Deep Multi-Modal Object Detection for Extended Object Tracking in Autonomous Driving","authors":"Lino Antoni Giefer, Razieh Khamsehashari, K. Schill","doi":"10.1109/CAVS51000.2020.9334646","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334646","url":null,"abstract":"The perception ability of automated systems such as autonomous cars plays an outstanding role for safe and reliable functionality. With the continuously growing accuracy of deep neural networks for object detection on one side and the investigation of appropriate space representations for object tracking on the other side both essential perception parts received special research attention within the last years. However, early fusion of multiple sensors turns the determination of suitable measurement spaces into a complex and not trivial task. In this paper, we propose the use of a deep multi-modal object detection network for the early fusion of LiDAR and camera data to serve as a measurement source for an extended object tracking algorithm on Lie groups. We develop an extended Kalman filter and model the state space as the direct product Aff(2) × ℝ6 incorporating second- and third-order dynamics. We compare the tracking performance of different measurement space representations-SO(2) × ℝ4, SO(2)2 × ℝ3 and Aff(2)-to evaluate, how our object detection network encapsulates the measurement parameters and the associated uncertainties. With our results, we show that the lowest tracking errors in the case of single object tracking are obtained by representing the measurement space by the affine group. Thus, we assume that our proposed object detection network captures the intrinsic relationships between the measurement parameters, especially between position and orientation.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127036182","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 : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334683
Qiao Wang, D. Grace
Proactive caching is a promising approach to achieve efficient content delivery, reduce content retrieval latency, and improve user experience in vehicular content networks. This paper proposes a mobility prediction based proactive caching scheme utilizing a sequence prediction algorithm, namely Sequence Prediction-based Proactive Caching, to predict the next possible RSU along a vehicle’s path and pre-locate relevant content. Four systems’ performance is evaluated in two areas of Las Vegas and Manchester. The obtained results in Las Vegas have shown that the proposed system outperforms the other three systems i.e., Baseline Proactive Caching system, non-proactive caching system and no-caching system. It is shown to be up to over three times and twice better than the non-proactive caching system and Baseline Proactive Caching system respectively in terms of cache performance and on average, network delay of SPPC is reduced by 18% and 24% compared with non-proactive caching system and no-caching system respectively. Performance benchmark in Manchester generalized the application of SPPC system and asserted its superiority. The paper also gives insight into solving prediction issues with data mining techniques.
{"title":"Sequence Prediction-based Proactive Caching in Vehicular Content Networks","authors":"Qiao Wang, D. Grace","doi":"10.1109/CAVS51000.2020.9334683","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334683","url":null,"abstract":"Proactive caching is a promising approach to achieve efficient content delivery, reduce content retrieval latency, and improve user experience in vehicular content networks. This paper proposes a mobility prediction based proactive caching scheme utilizing a sequence prediction algorithm, namely Sequence Prediction-based Proactive Caching, to predict the next possible RSU along a vehicle’s path and pre-locate relevant content. Four systems’ performance is evaluated in two areas of Las Vegas and Manchester. The obtained results in Las Vegas have shown that the proposed system outperforms the other three systems i.e., Baseline Proactive Caching system, non-proactive caching system and no-caching system. It is shown to be up to over three times and twice better than the non-proactive caching system and Baseline Proactive Caching system respectively in terms of cache performance and on average, network delay of SPPC is reduced by 18% and 24% compared with non-proactive caching system and no-caching system respectively. Performance benchmark in Manchester generalized the application of SPPC system and asserted its superiority. The paper also gives insight into solving prediction issues with data mining techniques.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127852966","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 : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334559
Ikjot Saini, Sherif Saad, A. Jaekel
Preserving privacy in VANETs is a significant challenge for users and public acceptance of VANETs. The use of a pseudonym is a common technique for enhancing the user’s privacy in VANETs. Several Pseudonym Changing Schemes (PCS) for user’s privacy in VANETs have been proposed. The highly dynamic topology of the vehicular network can impact the way the pseudonymous identifiers are changed. To make these changes inconspicuous, we introduce the Context-Aware and Traffic Adaptive privacy scheme, which takes into account the rapidly changing traffic condition. In this paper, we propose a new PCS that aims to benefit the most from the context of the vehicle and traffic patterns to leverage a suitable situation for changing pseudonyms that increases anonymity. The vehicles change the pseudonym simultaneously in a region to increase privacy by maximizing the anonymity set. The proposed approach is evaluated in the presence of an adversary actor who could engineer privacy attacks against any given PCS.
{"title":"A Context Aware and Traffic Adaptive Privacy Scheme in VANETs","authors":"Ikjot Saini, Sherif Saad, A. Jaekel","doi":"10.1109/CAVS51000.2020.9334559","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334559","url":null,"abstract":"Preserving privacy in VANETs is a significant challenge for users and public acceptance of VANETs. The use of a pseudonym is a common technique for enhancing the user’s privacy in VANETs. Several Pseudonym Changing Schemes (PCS) for user’s privacy in VANETs have been proposed. The highly dynamic topology of the vehicular network can impact the way the pseudonymous identifiers are changed. To make these changes inconspicuous, we introduce the Context-Aware and Traffic Adaptive privacy scheme, which takes into account the rapidly changing traffic condition. In this paper, we propose a new PCS that aims to benefit the most from the context of the vehicle and traffic patterns to leverage a suitable situation for changing pseudonyms that increases anonymity. The vehicles change the pseudonym simultaneously in a region to increase privacy by maximizing the anonymity set. The proposed approach is evaluated in the presence of an adversary actor who could engineer privacy attacks against any given PCS.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126867082","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 : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334579
A. Sakr
Traffic in the near future is expected to be a mix of legacy vehicles with limited number of on-board sensors and sensor-rich vehicles with advanced sensing capabilities and different levels of automation. In this work, we propose a novel framework to leverage the existence of sensor-rich vehicles to assist legacy vehicles in estimating the road geometry which is an essential task for advanced driver assistance systems (ADAS). In the proposed method, the legacy vehicle, which is not necessarily equipped with any cameras or ranging sensors, receives processed camera data related to the road geometry from nearby sensor-rich vehicles. Then, the legacy vehicle fuses this data to build a local map of the road ahead for up to 200 m. Using experimental data, we show that the proposed method reduces the root mean square estimation error by 209% and the mean absolute estimation error by 857% compared to camera-based systems. The results also show that sensor-rich vehicles benefit from sharing the processed camera data and can significantly improve the accuracy of the road geometry estimate at much higher distances.
{"title":"Cooperative Road Geometry Estimation via Sharing Processed Camera Data","authors":"A. Sakr","doi":"10.1109/CAVS51000.2020.9334579","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334579","url":null,"abstract":"Traffic in the near future is expected to be a mix of legacy vehicles with limited number of on-board sensors and sensor-rich vehicles with advanced sensing capabilities and different levels of automation. In this work, we propose a novel framework to leverage the existence of sensor-rich vehicles to assist legacy vehicles in estimating the road geometry which is an essential task for advanced driver assistance systems (ADAS). In the proposed method, the legacy vehicle, which is not necessarily equipped with any cameras or ranging sensors, receives processed camera data related to the road geometry from nearby sensor-rich vehicles. Then, the legacy vehicle fuses this data to build a local map of the road ahead for up to 200 m. Using experimental data, we show that the proposed method reduces the root mean square estimation error by 209% and the mean absolute estimation error by 857% compared to camera-based systems. The results also show that sensor-rich vehicles benefit from sharing the processed camera data and can significantly improve the accuracy of the road geometry estimate at much higher distances.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121430012","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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