Table 3 - uploaded by Alessandro Bazzi
Content may be subject to copyright.
Source publication
Connected vehicles will improve safety and enable new services to drivers and passengers. One of the main enabled services will be the cooperative awareness, that is the broadcast transmission of periodic messages containing updated information on status and movements. This continuos communication may help the drivers in critical situations and eve...
Contexts in source publication
Context 1
... safety applications foreseen by three of the main international institutions and standardization bodies, NHTSA, ETSI, and 3GPP, are summarized in Table 1 see Table 3 Receiver antenna gain (G r ) ...
Context 2
... dB Minimum SINR (γ) see Table 3 Transmission range (LOS) see whereas applications based on event-driven messages and vehicle-to-infrastructure (V2I) communications are not shown since out of the scope of the present work. The numbers from NHTSA report the results of studies carried out during a project, in which 34 safety and 11 non-safety scenarios have been described. ...
Context 3
... the simulations, each on board unit (OBU) periodically transmits a beacon of B bytes in broadcast, with periodicity f B set to 10 Hz in accordance to the value required by most applications (see Table 1). All transmissions are performed at constant power, adopting one of the eight combinations of modulation and coding scheme of IEEE 802.11p, hereafter called Modes and recalled in Table 3. The length of each transmission is thus also the same for all transmissions performed during one simulation, with a value that depends on the beacon size and the adopted Mode. ...
Context 4
... length of each transmission is thus also the same for all transmissions performed during one simulation, with a value that depends on the beacon size and the adopted Mode. Cooperative Awareness in the Internet of Vehicles for Safety Enhancement With the settings detailed in Tables 2 and 3, the average radio range varies from approximately 740 m if Mode 1 is assumed, to nearly 125 m with Mode 8 (see Table 3. Independently from the adopted Mode, the sensing range is always set to the sensitivity of Mode 1, with a maximum range of approximately 740 m. ...
Similar publications
We suggest secure Vehicle-to-Vehicle communications in a secure cluster. Here, the security cluster refers to a group of vehicles having a certain level or more of secrecy capacity. Usually, there are many difficulties in defining secrecy capacity, but we define vehicular secrecy capacity for the vehicle defined only by SNR values. Defined vehicula...
Citations
... Given the IEEE 802.11p modulation and coding schemes (MCSs) [25], the time is 92.62 ms, using MCS 3 and 54.28 ms using MCS 8, where t slot = 9µs, SIFS=16 µs [26], [27], cw max = 255, and p c ≤ 0.03 [24], the collision probability that is proportional to vehicle density and the distance to RSU. Each vehicle waits backoff time at maximum. ...
Speeding, slowing down, and sudden acceleration are the leading causes of fatal accidents on highways.
Anomalous driving behavior detection can improve road safety by informing drivers who are in the vicinity of dangerous vehicles.
However, detecting abnormal driving behavior at the city-scale in a centralized fashion results in considerable network and computation load, that would significantly restrict the scalability of the system.
In this paper, we propose CAD3, a distributed collaborative system for road-aware and driver-aware anomaly driving detection.
CAD3 considers a decentralized deployment of edge computation nodes on the roadside and combines collaborative and context-aware computation with low-latency communication to detect and inform nearby drivers of unsafe behaviors of other vehicles in real-time.
Adjacent edge nodes collaborate to improve the detection of abnormal driving behavior at the city-scale.
We evaluate CAD3 with a physical testbed implementation. We emulate realistic driving scenarios from a real driving data set of 3,000 vehicles, 214,000 trips, and 18 million trajectories of private cars in Shenzhen, China.
At the microscopic (road) level, CAD3 significantly improves the accuracy of detection and lowers the number of potential accidents caused by false negatives up to four times and 24 times as compared to distributed standalone and centralized models, respectively. CAD3 can scale up to 256 vehicles connected to a single node while keeping the end-to-end latency under 50 ms and a required bandwidth below 5 mbps. At the mesoscopic (driver-trip) level, CAD3 performs stable and accurate detection over time, owing to local RSU interaction. With a dense deployment of edge nodes, CAD3 can scale up to the size of Shenzhen, a megalopolis of 12 million inhabitant with over 2 million concurrent vehicles at peak hours.
... Whereas the loss increases when K-value becomes low such as zero, it represents the urban environment where NLOS components are available. The receiver sensitivity column shows the minimum values of the Signal-to-Noise Ratio (SNR) at the receiver to guarantee successful data reception [53]. The data delivery probability between the sender vehicle and receiver vehicle has been estimated and summarised in Table II. ...
A novel approach is proposed for multi-modal collective awareness (CA) of multiple networked intelligent agents. Each agent is here considered as an Internet of Things (IoT) node equipped with machine learning capabilities; collective awareness aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Data-driven dynamic Bayesian models learned from multi-sensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. A set of switching Dynamic Bayesian Network (DBN) models collectively learned in a training phase, each related to particular sensorial modality, is used to allow each agent in the network to perform synchronous estimation of possible abnormalities occurring when a new task of the same type is jointly performed. Collective DBN (CDBN) learning is performed by unsupervised clustering of generalized errors (GEs) obtained from a starting generalized model. A Growing Neural Gas (GNG) algorithm is used as a basis to learn the discrete switching variables at the semantic level. Conditional probabilities linking nodes in the CDBN models are estimated using obtained clusters. CDBN models are associated with a Bayesian inference method, namely Distributed Markov Jump Particle Filter (D-MJPF), employed for joint state estimation and abnormality detection. The effects of networking protocols and of communications in the estimation of state and abnormalities are analyzed. Performance is evaluated by using a small network of two autonomous vehicles performing joint navigation tasks in a controlled environment. In the proposed method, firstly the sharing of observations is considered in ideal condition, and then the effects of a wireless communication channel have been analysed for the collective abnormality estimation of the agents. Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well.
... • CAM range (r CAM ) is the maximum distance at which the achieved PRR exceeds the minimum acceptable PRR, PRR TH . This metric has already been considered in the literature, and the threshold is typically set to 0.9 [23], [47]- [49]. For this reason, we also set PRR TH to 0.9 in our evaluation. ...
To support the emerging vehicle-to-everything (V2X) communication for autonomous vehicles and smart transportation services, 3rd Generation Partnership Project (3GPP) has recently introduced cellular V2X (C-V2X) standards. In C-V2X, radio resources can be managed not only centrally by the cellular network base station, but also in a completely distributed manner (Mode 4) without any cellular support. However, Mode 4 may suffer significant collisions and interference in a dense environment since each vehicle selects its own resources to transmit V2X messages autonomously without adapting appropriately to vehicle density. To address this challenge, we propose ATOMIC, an Adaptive Transmission pOwer and Message Interval Control scheme for C-V2X Mode 4, in which each vehicle utilizes real-time channel sensing and neighbor information to reduce channel contention for improved reliability and latency. Through analysis and extensive simulations, we show that ATOMIC outperforms the standard Mode 4 in both urban and highway scenarios especially in highly dense environments.
... Whereas the loss increases when K-value becomes low such as zero, it represents the urban environment where NLOS components are available. The receiver sensitivity column shows the minimum values of the Signal-to-Noise Ratio (SNR) at the receiver to guarantee successful data reception [53]. The data delivery probability between the sender vehicle and receiver vehicle has been estimated and summarised in Table II. ...
A novel approach is proposed for multi-modal collective awareness (CA) of multiple networked intelligent agents. Each agent is here considered as an Internet of Things (IoT) node equipped with machine learning capabilities; collective awareness aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Data-driven dynamic Bayesian models learned from multi-sensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. A set of switching Dynamic Bayesian Network (DBN) models collectively learned in a training phase, each related to particular sensorial modality, is used to allow each agent in the network to perform synchronous estimation of possible abnormalities occurring when a new task of the same type is jointly performed. Collective DBN (CDBN) learning is performed by unsupervised clustering of generalized errors (GEs) obtained from a starting generalized model. A Growing Neural Gas (GNG) algorithm is used as a basis to learn the discrete switching variables at the semantic level. Conditional probabilities linking nodes in the CDBN models are estimated using obtained clusters. CDBN models are associated with a Bayesian inference method, namely Distributed Markov Jump Particle Filter (D-MJPF), employed for joint state estimation and abnormality detection.
The effects of networking protocols and of communications in the estimation of state and abnormalities are analyzed. Performance is evaluated by using a small network of two autonomous vehicles performing joint navigation tasks in a controlled environment. In the proposed method, firstly the sharing of observations is considered in ideal condition, and then the effects of a wireless communication channel have been analysed for the collective abnormality estimation of the agents.
Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well.
... We performed different tests changing the values of data rate, modulation, and K-factor as shown in Table I. High K-factor values refer to rural scenarios where the presence of obstacles, buildings, etc. has a lower impact on the achieved performance. The sensitivity column shows the minimum values of the Signal-to-Noise Ratio (SNR) at the receiver to guarantee successful data reception [52]. The DBN model performance in terms of ROC curve has been plotted for the leader vehicle for data rates 18 Mbps in Fig. 9 and 27 Mbps in Fig. 10, respectively. ...
... Simulation parameters[52] Data rate (Mbits/sec) Modulation Sensitivity (dBm) ...
The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This article presents a novel approach to develop an initial level of collective awareness in a network of intelligent agents. A specific collective self awareness functionality is considered, namely, agent centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent. Data driven dynamic Bayesian network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) are here used for abnormality detection and prediction. A set of DBNs, each related to an agent, is used to allow the agents in the network to each synchronously aware possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithms reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Real data sets are also used acquired by autonomous vehicles performing different tasks in a controlled environment.
... We performed different tests changing the values of data rate, modulation, and K-factor as shown in Table I. High K-factor values refer to rural scenarios where the presence of obstacles, buildings, etc. has a lower impact on the achieved performance. The sensitivity column shows the minimum values of the Signal-to-Noise Ratio (SNR) at the receiver to guarantee successful data reception [52]. The DBN model performance in terms of ROC curve has been plotted for the leader vehicle for data rates 18 Mbps in Fig. 9 and 27 Mbps in Fig. 10, respectively. ...
... Simulation parameters[52] ...
The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This paper presents a novel approach to develop an initial level of collective awareness (CA) in a network of intelligent agents. A specific collective self-awareness functionality is considered, namely agent-centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent. Data-driven Dynamic Bayesian Network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) is here used for abnormality detection and prediction. A set of DBNs, each related to an agent is used to allow the agents in the network to reach synchronously aware about possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A Growing Neural Gas (GNG) algorithm is used to learn the nodes variables and conditional probabilities linking nodes in the DBN models; a Markov Jump Particle Filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithm’s reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Performances of the DBN based abnormality detection models under different channel and source conditions are discussed. The effects of distances among agents and of the delays and packet losses are analyzed in different scenario categories (urban, suburban, rural) Real data sets are also used acquired by autonomous vehicles performing different tasks in a controlled environment.
The connected automated vehicle has been often touted as a technology that will become pervasive in society in the near future. One can view an automated vehicle as having Artificial Intelligence (AI) capabilities, being able to self-drive, sense its surroundings, recognise objects in its vicinity, and perform reasoning and decision-making. Rather than being stand alone, we examine the need for automated vehicles to cooperate and interact within their socio-cyber-physical environments, including the problems cooperation will solve, but also the issues and challenges. We review current work in cooperation for automated vehicles, based on selected examples from the literature. We conclude noting the need for the ability to behave cooperatively as a form of
social-AI
capability for automated vehicles, beyond sensing the immediate environment and beyond the underlying networking technology.
