Context-Aware Content Selection and Message Generation for Collective Perception Services

Abstract

Collective perception messages (CPMs) inform neighbors about detected objects and their current status. Although CPMs can greatly contribute to improving vehicle perception, their performance will degrade if the wireless channel is saturated and/or vehicles must process excessively high amount of data to build such a perception. Channel saturation and processing overhead can be induced by transmissions of CPMs if vehicles and roadside infrastructure transmit CPMs too often with too much redundant information. Indeed, according to the current draft standard, the CPM generation frequency at individual vehicles and infrastructure can be as high as 10 Hz and depending on the perception capability, individual CPMs can be quite heavy (around 1500 Bytes), containing redundant information. It is hence important to integrate techniques that reduce information redundancy while providing sufficient perception with optimal resource use. In this paper, we propose context-aware communication schemes that control CPM content selection and transmission. Two types of contexts, particularly radio resource use (channel busy ratio: CBR) and infrastructure availability, have been considered in the proposed schemes. Using a network simulator, we evaluated the performances of the proposed schemes in terms of CBR, packet delivery ratio and awareness ratio. The simulation results show that the scheme that takes into account both resource use and infrastructure availability offers the best performance on all the above metrics.

Analysis and Evaluation of Information Redundancy Mitigation for V2X Collective Perception

“…Second, as already discussed in Section IV, many different vehicles may send updates about the same object in a short interval of time, circumventing any additional updates in the rest of the W_Redundancy time. A better approach for this RMR and already analyzed in [18] is to use the number of V2X stations that transmitted information about the same object instead of considering the number of updates received. The advantage of the Frequency-based RMR is the direct control over the number of V2X stations generating information about the same object, which could be helpful, for example, in case of misbehavior detection.…”

“…A more recent work [18] provided a novel and promising way of controlling information redundancy. Their RMR filters perceived objects using three criteria: channel load as well as number and type of V2X stations that have already provided information about these objects.…”

“…Currently, none of the RMR has a method to address this functional requirement. Adapting the RMR parameters could be developed similar to [18] which proposed a filtering approach considering RSU.…”

Multi-Agent Advantage Actor-Critic Learning For Message Content Selection in Cooperative Perception Networks

“…A recent dynamics-based redundancy mitigation technique is proposed in [16], where each CAV analyzes the most recent CPMs received from other CAVs and excludes perceived objects that exceed predefined position or speed thresholds. In addition, authors in [17] have proposed redundancy control schemas based on channel status, number, and type of V2X stations that have also provided the same perceived information. The main objective is to adapt the number of V2X stations transmitting data about the same object to the channel load while maintaining CP awareness close to the default CPM generation rules.…”

“…However, we observe that this metric increases with the number of learning episodes and stabilizes after about 3500 update steps, indicating that CAVs maximize the usefulness of their CPMs in the V2V network after about 35000 time slots. Following that, we compare the performances achieved by MCORM to the ETSI CPM generation rules [5] summarized at the beginning of Section II, dynamics-based [16] technique, and CBR-selective [17] scheme. A CAV utilizing the dynamicbased technique verifies the most recent CPM received from all neighboring CAVs and excludes perceived objects whose positions or speeds exceed static thresholds.…”