Cooperative awareness using roadside unit networks in mixed traffic

Question

1. What are the contributions mentioned in the paper "Cooperative awareness using roadside unit networks in mixed traffic" ?

2. What future works have the authors mentioned in the paper "Cooperative awareness using roadside unit networks in mixed traffic" ?

3. How many CAMs can be transmitted in a single packet?

4. What is the effect of adding more messages to the queue?

Accepted Answer

To enhance cooperative awareness in this mixed traffic, the authors assessed the special roadside unit that they developed in their previous work that generates required V2V messages on behalf of sensed target vehicles.. In this paper, the authors extend their earlier work to propose a system called Grid Proxy Cooperative Awareness Message to broaden the cooperative awareness message dissemination area by connecting infrastructure using high-speed roadside networks.. To minimize delay in message delivery, the authors designed the proposed system to use edge computing.

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The future work includes adaptive maximum distance.

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A proxy CAM (payload length Ppcam = 680 bits) is transmitted over 0.2885 ms on average, resulting in an effective proxy CAM throughput over IEEE802.11p of about 2.73 Mbps.

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When a transmitter receives more proxy CAMs than the sending rate, additional messages are added to the queue, increasing the wait time and end-to-end delay, which in turn makes real-time information outdated.

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The objective of the present study was to broaden the cooperative awareness message dissemination range by connecting Proxy CAM agents using a high-speed roadside network.

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To evaluate the communication performance in terms of delay and PDR under various scenarios, the authors implemented their proposed method on the Artery1 framework, an extension of the open source vehicular network simulation framework Veins2.

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Applying the distance priority algorithm, however, enabled the delivery of 100% of the messages within a range of 450 m, with the PDR gradually dropping thereafter up to 800 m.

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Under Proxy CAM, roadside sensors detect target vehicles and estimate vehicle dynamic data such as positions, velocities, and accelerations.

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Each building edge blocking a path attenuates the signal by 9 db; a signal passing through a building is attenuated by 0.4 db per meter.

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In the case of a 1,000-packet queue length, which is applied in the simulation scenario in Section V), the queuing delay is 0.2885ms ⇥ 1000 = 288.5 ms when the queue is full.

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Their simulation results demonstrate that the proposed scheme successfully delivers messages with low latency and at a high delivery ratio, particularly under heavy road traffic conditions.

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DSSS experimented three systems: rear-end collision prevention system at entering to traffic jam which hides behind obstacles, collision prevention system at turning right at the intersection, and collision prevention system at the intersection in a bad view.

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To achieve an adaptive maximum distance, the queue monitoring results must be shared among neighboring agents to ensure that the proper number of messages is sent to each agent.

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The average time interval needed to successfully transmit a CAM packet via the wireless channel, tcam, is given bytcam = tDIFS + b+ tsend, (2)where tDIFS is the Distributed Inter Frame Space (DIFS) interval, b is the backoff time, and tsend is the duration of transmission.

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The simulated vehicles traveled at 50 km/h through intersections along two routes, (A) and (B), which are marked in Figs 3 and 4.

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Under the current V2V messaging paradigm, nonV2V aware nodes are excluded and therefore the cooperative ITS network does not relay their presence to connected autonomous vehicles.

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At a vehicle density of 50 vehicles/km, the PDR of the Grid Proxy CAM scenario was maintained at around 70% with large standard deviations, which arose for the following reasons.

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The solution must frequently deliver messages over a broad area through a high-speed roadside network connection of Proxy CAM agents.

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The transmitter has a maximum distance of dmax, which is pre-configured by the road infrastructure operator, and the queue occupancy ratio rqo is continuously monitored.

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The authors conducted all simulations 100 times each with random seeds and calculated the averages and standard deviations for the results.

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Each vehicle passed through intersections without stopping, as the authors assumed that the roads had no traffic signals; two vehicles per second passed through each intersection.

Cooperative awareness using roadside unit networks in mixed traffic

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Most frequently asked questions

1. What are the contributions mentioned in the paper "Cooperative awareness using roadside unit networks in mixed traffic" ?

2. What future works have the authors mentioned in the paper "Cooperative awareness using roadside unit networks in mixed traffic" ?

3. How many CAMs can be transmitted in a single packet?

4. What is the effect of adding more messages to the queue?

5. What was the objective of the present study?

6. What is the proposed method for evaluating the communication performance under various scenarios?

7. How many messages were delivered at a distance of 450 m?

8. What is the role of proxy CAM in cooperative ITS?

9. How many db per meter is a signal passing through a building?

10. How long does a queue take to broadcast a CAM?

11. What is the effect of the proposed scheme on traffic?

12. How many vehicles/km are involved in the collision prevention system?

13. How to achieve an adaptive maximum distance?

14. What is the average time interval needed to successfully transmit a CAM packet via the wireless channel?

15. How many vehicles were simulated at the intersections?

16. What is the problem with the current V2V messaging paradigm?

17. What was the PDR of the Grid Proxy CAM scenario?

18. What is the requirement for a network to deliver messages?

19. What is the rqo of the queue?

20. How many times did the authors run the simulations?

21. How many vehicles per second passed through the intersections?

Figures

Citations

Pedestrian support in intelligent transportation systems: challenges, solutions and open issues

Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles

Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles

Networked Roadside Perception Units for Autonomous Driving

AutoC2X: Open-source software to realize V2X cooperative perception among autonomous vehicles

References

The potential of collective perception in vehicular ad-hoc networks

Priority-based coordination of autonomous and legacy vehicles at intersection

Cooperative perception for autonomous vehicle control on the road: Motivation and experimental results

Analysis of V2X communication parameters for the development of a fusion architecture for cooperative perception systems

Cellular-based vehicle to pedestrian (V2P) adaptive communication for collision avoidance

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