Pub Date : 2020-11-01DOI: 10.1109/CAVS51000.2020.9334555
Julian Schindler, R. Markowski, Daniel Wesemeyer, B. Coll-Perales, Clarissa Böker, S. Khan
When an automated vehicle (AV) of level 3 and above arrives at an area on the road which is not part of its operational design domain (ODD), it is forced to perform a transition of control (ToC) to the driver. If the driver is not responding, the ToC fails and a minimum risk maneuver (MRM) needs to be executed. When the penetration rate of such AVs on the roads is high, this will negatively impact traffic efficiency and safety. In EU H2020 TransAID, infrastructure supported traffic management measures have been investigated which reduce these negative impacts. The measures and their effects are tested intensively in simulation. To demonstrate that the measures could also be applied to the real world, feasibility assessments with real-world prototypes have been performed. This paper shows how the measures have been implemented in ITS-G5 communication, infrastructure and connected automated vehicles (CAV), and how the prototypes have been tested.
{"title":"Infrastructure Supported Automated Driving in Transition Areas – a Prototypic Implementation","authors":"Julian Schindler, R. Markowski, Daniel Wesemeyer, B. Coll-Perales, Clarissa Böker, S. Khan","doi":"10.1109/CAVS51000.2020.9334555","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334555","url":null,"abstract":"When an automated vehicle (AV) of level 3 and above arrives at an area on the road which is not part of its operational design domain (ODD), it is forced to perform a transition of control (ToC) to the driver. If the driver is not responding, the ToC fails and a minimum risk maneuver (MRM) needs to be executed. When the penetration rate of such AVs on the roads is high, this will negatively impact traffic efficiency and safety. In EU H2020 TransAID, infrastructure supported traffic management measures have been investigated which reduce these negative impacts. The measures and their effects are tested intensively in simulation. To demonstrate that the measures could also be applied to the real world, feasibility assessments with real-world prototypes have been performed. This paper shows how the measures have been implemented in ITS-G5 communication, infrastructure and connected automated vehicles (CAV), and how the prototypes have been tested.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"54 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":"124858298","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/CAVS51000.2020.9334650
Alessio Buscemi, G. Castignani, T. Engel, Ion Turcanu
Current in-vehicle communication systems lack security features, such as encryption and secure authentication. The approach most commonly used by car manufacturers is to achieve security through obscurity – keep the proprietary format used to encode the information secret. However, it is still possible to decode this information via reverse engineering. Existing reverse engineering methods typically require physical access to the vehicle and are time consuming. In this paper, we present a Machine Learning-based method that performs automated Controller Area Network (CAN) bus reverse engineering while requiring minimal time, hardware equipment, and potentially no physical access to the vehicle. Our results demonstrate high accuracy in identifying critical vehicle functions just from analysing raw traces of CAN data.
{"title":"A Data-Driven Minimal Approach for CAN Bus Reverse Engineering","authors":"Alessio Buscemi, G. Castignani, T. Engel, Ion Turcanu","doi":"10.1109/CAVS51000.2020.9334650","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334650","url":null,"abstract":"Current in-vehicle communication systems lack security features, such as encryption and secure authentication. The approach most commonly used by car manufacturers is to achieve security through obscurity – keep the proprietary format used to encode the information secret. However, it is still possible to decode this information via reverse engineering. Existing reverse engineering methods typically require physical access to the vehicle and are time consuming. In this paper, we present a Machine Learning-based method that performs automated Controller Area Network (CAN) bus reverse engineering while requiring minimal time, hardware equipment, and potentially no physical access to the vehicle. Our results demonstrate high accuracy in identifying critical vehicle functions just from analysing raw traces of CAN data.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"23 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":"121368478","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/CAVS51000.2020.9334568
Jasmina Zubaca, M. Stolz, D. Watzenig
Estimation of vehicle motion is a pivotal requirement for autonomous vehicles. This paper proposes a robust ego-vehicle motion estimation to achieve precise localization and tracking, especially in the case of highly dynamic driving. An extended H∞ filter, based on a kinematic motion model assuming constant turn-rate and acceleration is used to fuse LiDAR, IMU, and vehicle dynamic sensors’ measurements. Measurements from a real high-performance autonomous race car, the so-called DevBot 2.0, have been used to validate the fusion approach in a Roborace competition and compared to a standard Kalman-filter approach.The proposed estimation concept adapts the H∞ robustness bound based on the innovation sequence of the filter. This provides very fast tracking when it comes to highly dynamic movement, but still achieves minimal estimation uncertainty in case of stationary conditions with lower innovation. Furthermore, a pure kinematic model is used, which is robust against vehicle parameters, changes in the tire-road conditions, and changes in driving maneuvers. The resulting estimation concept shows outstanding performance for considered autonomous race scenario and can be used for a wide range of different applications, such as highway driving, urban driving, platooning, etc.
{"title":"Extended H∞ Filter Adaptation Based on Innovation Sequence for Advanced Ego-Vehicle Motion Estimation","authors":"Jasmina Zubaca, M. Stolz, D. Watzenig","doi":"10.1109/CAVS51000.2020.9334568","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334568","url":null,"abstract":"Estimation of vehicle motion is a pivotal requirement for autonomous vehicles. This paper proposes a robust ego-vehicle motion estimation to achieve precise localization and tracking, especially in the case of highly dynamic driving. An extended H∞ filter, based on a kinematic motion model assuming constant turn-rate and acceleration is used to fuse LiDAR, IMU, and vehicle dynamic sensors’ measurements. Measurements from a real high-performance autonomous race car, the so-called DevBot 2.0, have been used to validate the fusion approach in a Roborace competition and compared to a standard Kalman-filter approach.The proposed estimation concept adapts the H∞ robustness bound based on the innovation sequence of the filter. This provides very fast tracking when it comes to highly dynamic movement, but still achieves minimal estimation uncertainty in case of stationary conditions with lower innovation. Furthermore, a pure kinematic model is used, which is robust against vehicle parameters, changes in the tire-road conditions, and changes in driving maneuvers. The resulting estimation concept shows outstanding performance for considered autonomous race scenario and can be used for a wide range of different applications, such as highway driving, urban driving, platooning, etc.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"23 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":"115601989","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/CAVS51000.2020.9334661
D. Patel, Rym Zalila-Wenkstern
Modern connected and automated vehicles (CAV) are capable of making informed decisions in unexpected situations. CAVs can achieve this by collaborating with other CAVs using communication and sensing capabilities. This work discusses a partially-decentralized collaborative decision making approach for a coalition of CAVs in the presence of a misbehaving vehicle. A novel algorithm based on Monte Carlo Tree Search (MCTS) is presented for the CAV’s planning problem of deriving mitigation action plans. This algorithm reduces the size of the search tree exponentially to overcome the computational limitations of MCTS for large action-agent sets. V2V communication is used to ensure that mitigation action plans chosen by coalition members are conflict-free when possible. The proposed method is evaluated for several conflict scenarios showing that the system can effectively avoid collisions in diverse situations.
{"title":"Collaborative Collision Avoidance for CAVs in Unpredictable Scenarios","authors":"D. Patel, Rym Zalila-Wenkstern","doi":"10.1109/CAVS51000.2020.9334661","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334661","url":null,"abstract":"Modern connected and automated vehicles (CAV) are capable of making informed decisions in unexpected situations. CAVs can achieve this by collaborating with other CAVs using communication and sensing capabilities. This work discusses a partially-decentralized collaborative decision making approach for a coalition of CAVs in the presence of a misbehaving vehicle. A novel algorithm based on Monte Carlo Tree Search (MCTS) is presented for the CAV’s planning problem of deriving mitigation action plans. This algorithm reduces the size of the search tree exponentially to overcome the computational limitations of MCTS for large action-agent sets. V2V communication is used to ensure that mitigation action plans chosen by coalition members are conflict-free when possible. The proposed method is evaluated for several conflict scenarios showing that the system can effectively avoid collisions in diverse situations.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"18 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":"129306820","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/CAVS51000.2020.9334658
Salvatore Dabbene, Christopher Lehmann, C. Campolo, A. Molinaro, F. Fitzek
The Multi-access Edge Computing (MEC) paradigm allows several automotive applications to be offloaded from the vehicles to the edge. Besides a higher computation capability, compared to the on-board vehicle, and the shorter latency, compared to the remote cloud, the edge offers additional (context) information that is not directly available at the vehicle, e.g., via data fusion from multiple sources. In this paper we propose a high-level architecture for MEC-assisted platooning control. Within the architecture, the longitudinal controller is conceived as a virtualized application running on an edge server, and aligned with the European Telecommunications Standard Institute (ETSI) MEC reference framework. Performance assessment conducted through a realistic simulation framework, coupling a vehicular mobility simulator and Docker containers, showcases the feasibility and effectiveness of our proposal.
{"title":"A MEC-assisted Vehicle Platooning Control through Docker Containers","authors":"Salvatore Dabbene, Christopher Lehmann, C. Campolo, A. Molinaro, F. Fitzek","doi":"10.1109/CAVS51000.2020.9334658","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334658","url":null,"abstract":"The Multi-access Edge Computing (MEC) paradigm allows several automotive applications to be offloaded from the vehicles to the edge. Besides a higher computation capability, compared to the on-board vehicle, and the shorter latency, compared to the remote cloud, the edge offers additional (context) information that is not directly available at the vehicle, e.g., via data fusion from multiple sources. In this paper we propose a high-level architecture for MEC-assisted platooning control. Within the architecture, the longitudinal controller is conceived as a virtualized application running on an edge server, and aligned with the European Telecommunications Standard Institute (ETSI) MEC reference framework. Performance assessment conducted through a realistic simulation framework, coupling a vehicular mobility simulator and Docker containers, showcases the feasibility and effectiveness of our proposal.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"1 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":"129756649","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/CAVS51000.2020.9334669
Trevor Crain, P. Jaworski, Ioannis Kyriakopoulos, Richard Blachford, B. Fabien
The EcoCAR Mobility Challenge is the latest iteration of the Department of Energy (DOE) Advanced Vehicle Technology Competitions organized by Argonne National Laboratory (ANL). EcoCAR’s new focus on Connected and Automated Vehicle (CAV) technology will require the development of new methods and tools for fairly assessing energy usage for each of the university prototype vehicles. This paper serves to introduce potential methods for assessing CAV technology energy impacts in controlled urban and highway proving ground environments. In addition, it describes the development process of a target vehicle test system in collaboration with HORIBA MIRA. The system, based on HORIBA MIRA’s DigiCAV platform, will accelerate test system development for EcoCAR and produce a test environment with both real and simulated target vehicles for accurately assessing EcoCAR prototype vehicle implementations of hybrid powertrains and CAV features. The authors developed and validated a Hardware-in-the-Loop (HIL) test setup to perform initial calibrations of vehicle-specific DigiCAV controller implementations and will be testing those implementations in the next phase of development.
{"title":"Prototyping EcoCAR Connected Vehicle Testing System Using DigiCAV Development Platform","authors":"Trevor Crain, P. Jaworski, Ioannis Kyriakopoulos, Richard Blachford, B. Fabien","doi":"10.1109/CAVS51000.2020.9334669","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334669","url":null,"abstract":"The EcoCAR Mobility Challenge is the latest iteration of the Department of Energy (DOE) Advanced Vehicle Technology Competitions organized by Argonne National Laboratory (ANL). EcoCAR’s new focus on Connected and Automated Vehicle (CAV) technology will require the development of new methods and tools for fairly assessing energy usage for each of the university prototype vehicles. This paper serves to introduce potential methods for assessing CAV technology energy impacts in controlled urban and highway proving ground environments. In addition, it describes the development process of a target vehicle test system in collaboration with HORIBA MIRA. The system, based on HORIBA MIRA’s DigiCAV platform, will accelerate test system development for EcoCAR and produce a test environment with both real and simulated target vehicles for accurately assessing EcoCAR prototype vehicle implementations of hybrid powertrains and CAV features. The authors developed and validated a Hardware-in-the-Loop (HIL) test setup to perform initial calibrations of vehicle-specific DigiCAV controller implementations and will be testing those implementations in the next phase of development.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"13 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":"115523612","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/CAVS51000.2020.9334670
Kilian Schneider, Maximilian Inderst, T. Brandmeier
In recent years emergency braking systems became a standard in modern vehicles. However, these systems can not prevent every collision. Integrated safety systems allow bringing vehicle safety to the next level. This paper introduces a crash severity estimation algorithm based only on information received from environmental sensors like radar, camera, and LiDAR. Using a quadruple Kelvin model, the physical behavior of the ego vehicle during the crash is approximated, and thus, the crash severity parameters are derived. This paper focuses on the headon collisions with different relative velocities and approach angles. More than 50 finite element method simulations (FEM) with the same crash scenarios were performed to compare and validate the model’s results. The results prove that the presented methodology can reproduce the crash behavior and reliably approximates the crash severity parameters with-in the desired range.
{"title":"Hybrid Model Based Pre-Crash Severity Estimation for Automated Driving","authors":"Kilian Schneider, Maximilian Inderst, T. Brandmeier","doi":"10.1109/CAVS51000.2020.9334670","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334670","url":null,"abstract":"In recent years emergency braking systems became a standard in modern vehicles. However, these systems can not prevent every collision. Integrated safety systems allow bringing vehicle safety to the next level. This paper introduces a crash severity estimation algorithm based only on information received from environmental sensors like radar, camera, and LiDAR. Using a quadruple Kelvin model, the physical behavior of the ego vehicle during the crash is approximated, and thus, the crash severity parameters are derived. This paper focuses on the headon collisions with different relative velocities and approach angles. More than 50 finite element method simulations (FEM) with the same crash scenarios were performed to compare and validate the model’s results. The results prove that the presented methodology can reproduce the crash behavior and reliably approximates the crash severity parameters with-in the desired range.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"116 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":"116444451","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 : 2020-10-22DOI: 10.1109/CAVS51000.2020.9334618
Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti, Y. P. Fallah
Situational awareness as a necessity in connected and autonomous vehicles (CAV) domain is the subject of significant number of researches in recent years. The driver’s safety is directly dependent on robustness, reliability and, scalability of such systems. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. These mechanisms mitigate problems such as occlusion and sensor range limitation. However, the network capacity is a factor determining the maximum amount of information being shared among cooperative entities. The notion of feature sharing, proposed in our previous work, aims to address these challenges by maintaining a balance between computation and communication load. In this work, we propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method to further improve cooperative object detection performance. The performance of the proposed framework is verified through experiments on Volony dataset. The results confirm that our proposed framework outperforms our previous cooperative object detection method (FS-COD) in terms of average precision.
{"title":"Bandwidth-Adaptive Feature Sharing for Cooperative LIDAR Object Detection","authors":"Ehsan Emad Marvasti, Arash Raftari, Amir Emad Marvasti, Y. P. Fallah","doi":"10.1109/CAVS51000.2020.9334618","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334618","url":null,"abstract":"Situational awareness as a necessity in connected and autonomous vehicles (CAV) domain is the subject of significant number of researches in recent years. The driver’s safety is directly dependent on robustness, reliability and, scalability of such systems. Cooperative mechanisms have provided a solution to improve situational awareness by utilizing high speed wireless vehicular networks. These mechanisms mitigate problems such as occlusion and sensor range limitation. However, the network capacity is a factor determining the maximum amount of information being shared among cooperative entities. The notion of feature sharing, proposed in our previous work, aims to address these challenges by maintaining a balance between computation and communication load. In this work, we propose a mechanism to add flexibility in adapting to communication channel capacity and a novel decentralized shared data alignment method to further improve cooperative object detection performance. The performance of the proposed framework is verified through experiments on Volony dataset. The results confirm that our proposed framework outperforms our previous cooperative object detection method (FS-COD) in terms of average precision.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125367578","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 : 2020-08-08DOI: 10.1109/CAVS51000.2020.9334605
M. Saifuddin, Mahdi Zaman, Behrad Toghi, Y. P. Fallah, J. Rao
LTE based Cellular Vehicle-To-Everything (C-V2X) allows vehicles to communicate with each other directly without the need for infrastructure and is expected to be a critical enabler for connected and autonomous vehicles. V2X communication based safety applications are built on periodic broadcast of basic safety messages with vehicle state information. Vehicles use this information to identify collision threats and take appropriate countermeasures. As the vehicle density increases, these broadcasts can congest the communication channel resulting in increased packet loss; fundamentally impacting the ability to identify threats in a timely manner. To address this issue, it is important to incorporate a congestion control mechanism. Congestion management scheme based on rate and power control has proved to be effective for DSRC. In this paper, we investigate the suitability of similar congestion control to C-V2X with particular focus on transmit power control. In our evaluation, we include periodic basic safety messages and high priority event messages that are generated when an event such as hard braking occurs. Our study reveals that while power control does not improve packet delivery performance of basic safety messages, it is beneficial to high priority event message delivery. In this paper, we investigate the reasons for this behavior using simulations and analysis.
{"title":"Performance Analysis of Cellular-V2X with Adaptive & Selective Power Control","authors":"M. Saifuddin, Mahdi Zaman, Behrad Toghi, Y. P. Fallah, J. Rao","doi":"10.1109/CAVS51000.2020.9334605","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334605","url":null,"abstract":"LTE based Cellular Vehicle-To-Everything (C-V2X) allows vehicles to communicate with each other directly without the need for infrastructure and is expected to be a critical enabler for connected and autonomous vehicles. V2X communication based safety applications are built on periodic broadcast of basic safety messages with vehicle state information. Vehicles use this information to identify collision threats and take appropriate countermeasures. As the vehicle density increases, these broadcasts can congest the communication channel resulting in increased packet loss; fundamentally impacting the ability to identify threats in a timely manner. To address this issue, it is important to incorporate a congestion control mechanism. Congestion management scheme based on rate and power control has proved to be effective for DSRC. In this paper, we investigate the suitability of similar congestion control to C-V2X with particular focus on transmit power control. In our evaluation, we include periodic basic safety messages and high priority event messages that are generated when an event such as hard braking occurs. Our study reveals that while power control does not improve packet delivery performance of basic safety messages, it is beneficial to high priority event message delivery. In this paper, we investigate the reasons for this behavior using simulations and analysis.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121665644","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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