Collective perception and decentralized congestion control in vehicular ad-hoc networks

Günther, Hendrik-Jörn; Riebl, Raphael; Wolf, Lars; Facchi, Christian

doi:10.1109/vnc.2016.7835931

Cited by 47 publications

(25 citation statements)

References 12 publications

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“…The environmental perception unit utilizes onboard sensor data combined with the data provided by other vehicles in the vicinity, e.g. via collective perception [25], [26]. At the next step, the scene understanding and object classification is conducted by the available algorithm used by the maneuver planning tasks.…”

Section: Sensor-based Channel Prediction For Hdplmentioning

confidence: 99%

Improving the Performance of High-Density Platooning Using Vehicle Sensor-Based Doppler-Compensation Algorithms

Alieiev¹,

Jornod

Hehn

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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This paper investigates the advantages of using communication supported by information from vehicle sensors to improve the performance of the High-Density Platooning (HDPL) application. HDPL is one of the most promising applications in the field of cooperative driving. The goal of this application is to improve fuel and road efficiency without impairing safety. The level of efficiency improvement depends on the instantaneous channel quality. Strong multipath components, which arise from other vehicles, traffic signs, and surrounding buildings in such a highly dynamic vehicular environment, significantly influence the instantaneous link quality between communicating vehicles in a platoon. An emerging concept of vehicle sensor-aided predictive communications has shown its potential to improve direct-link communication. In this paper, the applicability of this approach is evaluated in terms of inter-vehicle distance error, which reflects the difficulties that the control algorithm has to cope with in the communications conditions. The simulation results show that the proposed sensor-based approach significantly improves the platoon performance even if affected by multiple dynamic scattering reflections at the same time.

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Section: Sensor-based Channel Prediction For Hdplmentioning

confidence: 99%

Improving the Performance of High-Density Platooning Using Vehicle Sensor-Based Doppler-Compensation Algorithms

Alieiev¹,

Jornod

Hehn

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Gunther et al proposed a method for enabling connected vehicles to send EPM (Environmental Perception Messages) to inform other connected vehicles of the existence of surrounding vehicles detected by their radar sensors in addition to CAM (Cooperative Awareness Messages) [3] to notify each other of their existence in a mixed situation. It was shown that a subject connected vehicle can perceive the positions of many vehicles in a radius of 300 meters (communicable range) around even if the percentage of connected vehicles in all vehicles (penetration rate) was less than 100% [4].…”

Section: Related Work a Collective Perceptionmentioning

confidence: 99%

Safety and Efficiency of Intersections With Mix of Connected and Non-Connected Vehicles

Higashiyama¹,

Kimura²,

Babakarkhail³

et al. 2020

IEEE Open J. Intell. Transp. Syst.

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Connected and autonomous vehicles have been significantly studied. They are connected to a network and communicate by exchanging information with each other, so they can detect blind spots that cannot be recognized by non-connected (conventional) vehicles. Therefore, they are expected to contribute to traffic efficiency and safety. However, even if connected vehicles are put to practical use in the future, it will take time to spread to the market, so it is considered that connected vehicles and non-connected vehicles will be mixed on the road. We proposed a method of enabling connected vehicles to share the information gathered from their sensors on surrounding vehicles near intersection in the mixed situation. We then examined the safety and efficiency of passing through an intersection through simulation. We found that efficiency improved and safety could be ensured compared to using conventional methods such as stopping before the intersection without using communication and using traffic lights. INDEX TERMS Connected vehicle, cooperative autonomous driving, V2V communication, mixed traffic.

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“…Some works have analyzed multiple packet types considering strict 802.11 EDCA priority [13]. However other works have found the limitations of MAC layer EDCA prioritization, in the ETSI ITS stack during scarce channel resource [14], [15].…”

Section: Related Work a Medium Access Control For V2x Communicationmentioning

confidence: 99%

Deep Learning-aided Resource Orchestration for Vehicular Safety Communication

Khan

Aubet

Pahl

et al. 2019

2019 Wireless Days (WD)

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802.11p based V2X communication uses stochastic medium access control, which cannot prevent broadcast packet collision, in particular during high channel load. Wireless congestion control has been designed to keep the channel load at an optimal point. However, vehicles' lack of precise and granular knowledge about true channel activity, in time and space, makes it impossible to fully avoid packet collisions. In this paper, we propose a machine learning approach using deep neural network for learning the vehicles' transmit patterns, and as such predicting future channel activity in space and time. We evaluate the performance of our proposal via simulation considering multiple safety-related V2X services involving heterogeneous transmit patterns. Our results show that predicting channel activity, and transmitting accordingly, reduces collisions and significantly improves communication performance.

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Collective perception and decentralized congestion control in vehicular ad-hoc networks

Cited by 47 publications

References 12 publications

Improving the Performance of High-Density Platooning Using Vehicle Sensor-Based Doppler-Compensation Algorithms

Improving the Performance of High-Density Platooning Using Vehicle Sensor-Based Doppler-Compensation Algorithms

Safety and Efficiency of Intersections With Mix of Connected and Non-Connected Vehicles

Deep Learning-aided Resource Orchestration for Vehicular Safety Communication

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