In this chapter, we have proposed and evaluated elementary functions and building blocks of a data fusion framework for V2V CLoc in the very specific context of GNSS-aided ITS-G5. Our evaluations take account of ad hoc communication and positioning aspects, such as distributed and asynchronous position estimates or random CAM transmissions.
On the one hand, we have pointed out that the transmission intervals between CAMs are constrained by channel load conditions, leading to non-periodic transmissions and as such, asynchronous data reception from “virtual anchors”. Accordingly, we have presented a prediction-based data resynchronization mechanism to properly incorporate cooperative information incoming from asynchronous neighboring cars relying on ana priori mobility model.
On the other hand, we have stated and solved the link selection problem, as perform-ing exhaustively cooperative schemes is questionable due to heavy required communication traffic and computational processing. Both classic non-Bayesian and Bayesian CRLB
crite-11Lateral errors might yet remain high regardless of the strategy, as it will be discussed with more details in Chapter 6.
ria have been investigated and incorporated in a computationally efficient search algorithm to reach the subset of the most informative neighbors, while minimizing the performance degradation caused by information loss. We have found that: (i) it is worth employing se-lective fusion in vehicular CLoc owing to the aforementioned benefits; (ii) the uncertainties of the “virtual anchors” should be monitored to prevent from having wrong cooperative neighbors in some special but common situations.
While considering link selection on the “ego” receiving side, we have also seen that the tolerance regarding the number of packets required in the fusion could induce/inspire more advanced transmission policies (see Chapter 4). Finally, we have illustrated that the use of RSSI over V2V communication link (as direct source of range information) may bring rather limited localization gains whenever the GNSS means already performs reasonably well, thus suggesting the use of more accurate V2V ranging technologies (see Chapter 5).
Wireless Channel Impacts on V2V Cooperative Localization
4.1 Introduction and Related Works
In Chapter 3, we have shown the promising potential of V2V CLoc to enhance the GNSS solutions in various environments and in different network settings. Nevertheless, in our initial evaluation framework, several simplistic assumptions have been made regarding the V2V wireless channel, which will be relaxed in this chapter.
On the one hand, it has been assumed that the GNSS and the RSSI readings inte-grated as observations are affected by white error processes (see Section 3.2.2). In practice however, they are strongly correlated over both space and time [26, 30, 79, 101–103], as a result from the combination of locally continuous physical propagation phenomena, highly specific vehicular mobility patterns and constrained refreshment rates. Such spatial corre-lations are viewed as a drastic limitation of current state of the art CLoc approaches (e.g., degrading fusion filters optimality). Thus, this chapter first concerns the observation noise correlations that may be specifically found under vehicular mobility. Practically speaking, the spatial correlations of observed measurement processes (and thus, their temporal cor-relations under vehicles mobility) result indeed from the conjunction of different factors triggered by constrained vehicular mobility. First of all, GNSS conditions (good or bad) may not change much over multiple samples and between neighboring vehicles (given a common class of equipment). Similarly, the channel fading conditions (obstructed or not) may not change much between two consecutive CAM transmissions (e.g., every 100 ms) by
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neighboring vehicles. Jointly or independently, these effects lead to correlated GNSS/RSSI measurements. A major issue when integrating such correlated measures into fusion fil-ters is that they are no longer affected by white Gaussian noise terms (but hence, by dependent contributions) and as such, they break a core assumption of most CLoc fu-sion approaches [86, 87, 89, 104] leading to inconsistent estimates with large fluctuations.
Thus, solutions need to be figured out or adapted to mitigate -or even benefit from- these correlation phenomena in our CLoc context.
On the other hand, CLoc based on PF induces not only high computational complexity but also extra communication cost (e.g., while exchanging particle clouds through message passing [105]) to achieve optimal performance levels. This limitation can be alleviated by adopting parametric message representations (e.g., well-known Gaussian mixture mod-els (GMMs)) instead of propagating explicit particle clouds. In the literature, this has been considered mostly in iterative message passing localization algorithms for generic, static wireless networks so far (typically within WSNs), thus enjoying more stable net-work connectivity and topology than in VANET scenarios [90, 106, 107]. Alternatively, localization based on variational message passing (VMP) can propagate and multiply circular symmetric Gaussian distributions to produce estimated locations instead of re-drawing samples out of explicit distributions received from neighboring nodes, and thus features significantly lower communication overhead [108, 109]. However, the latter solu-tions also rely on intermediary message approximation steps. All in all, to the best of our knowledge in the vehicular context, no in-depth investigation has been yet carried out in the literature to compare the various parameterization approaches and their per-formance trade-offs in terms of localization accuracy, communication traffic, channel load, computational complexity, latency, etc., whereas these metrics are expected to strongly impact the practicability and the implementability of PF-based CLoc. Moreover, in case of channel congestion, DCC mechanisms specified by the ETSI recommend to scale the CAM transmission rate from 10 Hz down to 2 Hz (in order not to exceed 60–70% channel load), what is expected to degrade CLoc accuracy accordingly.
This chapter is structured as follows. Section 4.2 formulates the aforementioned prob-lems, namely the space-time correlation of input observation noises and the limited com-munication channel (in terms of both rate and capacity). In Section 4.3, new methods are proposed at both signal processing and protocol/fusion rate levels so as to mitigate the
harmful impact of observations correlations. On this occasion, the achieved performance is compared with that of initial/nominal CLoc approaches by means of simulations (under both correlated and uncorrelated observation assumptions). Next, Section 4.4 presents and combines message approximation techniques with a new transmission control strategy so as to limit dramatically the channel load. Finally, Section 4.5 provides a summary for the chapter.