Co-simulation framework for control, communication and traffic for vehicle platoons

Vehicle platooning has gained attention for its potential to achieve an increased road capacity and safety, and a higher fuel efficiency. Member vehicles of a platoon wirelessly communicate complying with industrial standards such as IEEE 802.11p. By exchanging information with other members via wireless communication, a platoon member computes its desired acceleration which is then passed on to the engine control system via in-vehicle network to physically realize the acceleration. This leads to a multi-layer control scheme. The upper-layer is influenced by the behavior of 802.11p communication and network congestion due to transmissions by other vehicles in the traffic. The lower-layer engine control loop communicates over the fast and reliable in-vehicle networks (e.g., FlexRay, Ethernet). Design of the overall system therefore depends on (i) the characteristics of 802.11p-based communication (ii) the nature of the traffic (iii) the control algorithms running at the two layers. We present a cosimulation framework consisting of Matlab (for the multi-layer control algorithms), ns-3 (for the 802.11p network) and SUMO (for the traffic behavior). The framework can be used to validate different platooning setups. As an illustrative case study, we consider a multi-layer control strategy where the upper-layer uses Model Predictive Control (MPC) at a rate in compliance with 802.11p and the lower-layer uses statefeedback control at a higher sampling rate in line with in-vehicle networking capabilities. The control strategy is evaluated considering various realistic traffic and network congestion scenarios.</p

Risk assessment for traffic safety applications with V2V communications

Vehicle-to-others (V2X) communication systems intend to increase safety and efficiency of our transportation networks. However, wireless communication imperfections such as missed messages due to collisions and fading in the wireless channel, may affect safety application reliability and lead to risky situations. Thus metrics are required to evaluate the impact of communication inadequacies on the safety applications. In this paper we perform analyses of various existing safety application reliability metrics and conclude that they do not reflect safety application risk and vulnerability of individual nodes effectively. We propose a new metric called Effective Risk Factor (ERF), which quantifies the risk at a node for each link, to identify dangers due to poor awareness of their neighbors. The ERF evaluation considers links of its neighbors, thus detecting risky situations over existing neighbor links on runtime making the ERF assessment realistic. The ERF metric is evaluated and compared with other reliability metrics for a stationary vehicle warning application in a simulated highway scenario. The results show that the ERF evaluation performed at each node on runtime is able to capture a fine time scale fluctuations in the risk experienced by an application precisely. The ERF also enables prediction of higher risk situations. The results also demonstrate that the ERF captures application risk experienced by nodes effectively compared to other reliability metric

Modelling of communication reliability for platooning applications for intelligent transport system

Vehicle platooning using reliable wireless communication between the member vehicles is a promising method to increase road capacity, lower fuel consumption, and improve safety and driver comfort. IEEE 802.11p is a key communication technology in Vehicular Ad-hoc Networks (VANETs) for Intelligent Transport System (ITS) applications. The broadcasted awareness messages in the wireless communication channel may be used for platooning control, but there are reliability concerns especially in a highly congested network in rush hours. Keeping short inter-vehicle distance requires timeliness and reliability of the underlying exchange of control data in the communication channel. In this paper, we present a novel analytical model of communication reliability between platoon members in a channel shared with other vehicles using ITS. We use a discrete time M/G/1 queue model for occurrence of messages in Poisson distribution, and hence for the estimation of the Packet Reception Probability between the platoon members. We have evaluated the model for different vehicular densities, message rate and data rate with network simulations. Our results show that the Packet Reception Rate (PRR) from the model closely matches the simulation results. Based on PRR we estimate probability of missing packet in consecutive period of transmission. This model opens new opportunities to improve and evaluate the platooning control algorithms

Data rate based congestion control in V2V communication for traffic safety applications

Vehicle-to-Vehicle (V2V) communication systems intend to increase safety and efficiency of the transportation networks. At high vehicle density, the communication channel may become congested, impairing the reliability of the safety applications. As a counter measure, the European Telecommunications Standard Institute (ETSI), proposes the Decentralized Congestion Control (DCC) framework to control the channel load, by tuning message transmission parameters, such as message rate and transmitting power. In this paper, we analyze a congestion control scheme that follows the DCC framework known as Data Rate-DCC (DR-DCC) for various traffic densities. DR-DCC adjusts the data rate based on channel load measurements thus controlling the air time of packets to avoid congestion. Although tuning the data rate has been proposed before we are not aware of any reported work, where its full potential has been investigated in detail. In this paper we intend to provide more insight on the benefits of this approach by analyzing schemes that aim at optimum data rate for various traffic density cases. The objective is not only to avoid congestion but also to provide optimal support to safety applications. We compare the performance of DR-DCC to another DCC approach based on adjusting transmit power for various traffic density cases. DRDCC outperforms at various traffic densities providing better support to safety applications

A combined fair decentralized message-rate and data-rate congestion control for V2V communication

Channel congestion Is one of the most critical Issues In IEEE 802.11p-based vehicular ad hoc networks as it leads to unreliability of safety applications. As a counter measure, the European Telecommunications Standard Institute (ETSI), proposes a mandatory Decentralized Congestion Control (DCC) framework to control the channel load, by tuning transmission parameters, such as message-rate or data-rate. This paper defines a novel decentralized combined message-rate and data-rate congestion control (MD-DCC) scheme, which provides a fair and effective way of message-rate and data-rate allocation among vehicles to avoid congestion and satisfy application requirements. We discuss several implementation aspects such as the selection of parameters of MD-DCC and their relation with the application requirements. Simulations studies are presented to show the performance of MD-DCC in terms of application reliability and fairness. Our results show that, for various application requirements in a synthetic highway scenario and for various vehicular densities, MD-DCC outperforms other approaches that adapt only message-rate or data-rate. We conclude that MD-DCC takes the best of both message-rate and data-rate algorithms, resulting in superior application reliability as well as a dramatic increase in the maximum supported vehicular density

Co-simulation framework for control, communication and traffic for vehicle platoons

Vehicle platooning has gained attention for its potential to achieve an increased road capacity and safety, and a higher fuel efficiency. Member vehicles of a platoon wirelessly communicate complying with industrial standards such as IEEE 802.11p. By exchanging information with other members via wireless communication, a platoon member computes its desired acceleration which is then passed on to the engine control system via in-vehicle network to physically realize the acceleration. This leads to a multi-layer control scheme. The upper-layer is influenced by the behavior of 802.11p communication and network congestion due to transmissions by other vehicles in the traffic. The lower-layer engine control loop communicates over the fast and reliable in-vehicle networks (e.g., FlexRay, Ethernet). Design of the overall system therefore depends on (i) the characteristics of 802.11p-based communication (ii) the nature of the traffic (iii) the control algorithms running at the two layers. We present a cosimulation framework consisting of Matlab (for the multi-layer control algorithms), ns-3 (for the 802.11p network) and SUMO (for the traffic behavior). The framework can be used to validate different platooning setups. As an illustrative case study, we consider a multi-layer control strategy where the upper-layer uses Model Predictive Control (MPC) at a rate in compliance with 802.11p and the lower-layer uses statefeedback control at a higher sampling rate in line with in-vehicle networking capabilities. The control strategy is evaluated considering various realistic traffic and network congestion scenarios.</p

Co-simulation framework for control, communication and traffic for vehicle platoons

Vehicle platooning has gained attention for its potential to achieve an increased road capacity and safety, and a higher fuel efficiency. Member vehicles of a platoon wirelessly communicate complying with industrial standards such as IEEE 802.11p. By exchanging information with other members via wireless communication, a platoon member computes its desired acceleration which is then passed on to the engine control system via in-vehicle network to physically realize the acceleration. This leads to a multi-layer control scheme. The upper-layer is influenced by the behavior of 802.11p communication and network congestion due to transmissions by other vehicles in the traffic. The lower-layer engine control loop communicates over the fast and reliable in-vehicle networks (e.g., FlexRay, Ethernet). Design of the overall system therefore depends on (i) the characteristics of 802.11p-based communication (ii) the nature of the traffic (iii) the control algorithms running at the two layers. We present a cosimulation framework consisting of Matlab (for the multi-layer control algorithms), ns-3 (for the 802.11p network) and SUMO (for the traffic behavior). The framework can be used to validate different platooning setups. As an illustrative case study, we consider a multi-layer control strategy where the upper-layer uses Model Predictive Control (MPC) at a rate in compliance with 802.11p and the lower-layer uses statefeedback control at a higher sampling rate in line with in-vehicle networking capabilities. The control strategy is evaluated considering various realistic traffic and network congestion scenarios.</p