From a communication perspective, the backbone of CLoc is the V2X communication technology. Table 2.6 summaries the core V2X technologies that are or will be on-board of future connected vehicles including ITS-G5/IEEE 802.11p, LTE V2X, and 5G mmWave V2X. From this table, the technology of choice for our CLoc investigations is ITS-G5 since it is by far the most mature, while already fulfilling basic CLoc needs in terms of range (thus, cooperation potential), rate (sufficient for basic location awareness) and latency (compatible with current nominal GNSS refresh rates). Moreover, it is fully tested and available on the market today, what is particularly appealing for short-term algorithms implementation and validations. Besides, it already provides adequate location awareness mechanisms. On the contrary, LTE V2X is still under specification (at a quite early stage) and needs several years to be validated while the promising 5G mmWave V2X has an even longer time horizon ahead. Even if the cooperative fusion algorithms described in this thesis are primarily adapted to ITS-G5 communications (and to some extent jointly optimized, as it will be seen in particular in Chapters 2 and 3), note that the overall optimization methodology is however agnostic to the underlying technology and could be applied to other underlying V2X technologies in the near future.
From a location estimation perspective, according to the detailed taxonomy available in Appendix B, we are interested in CLoc algorithms which fall into the following categories:
• Two-step localization due to its low complexity and modularity;
• Distributed architecture to cope with high mobility patterns, frequent fragmentation and rapid evolution of the network topology, short link life time, etc.;
• Absolute localization to fulfill the requirements of the C-ITS applications;
• Probabilistic approach to exploit available statistical models;
• Multisensor fusion to exploit multiple available information sources from a number
of sensors in vehicles;
• Range-based localization as approaches that do not require explicit V2X measure-ments but just communicate raw GNSS information operate only under satellite coverage, while those exchanging maps or raw sensor data are still quite challenging for current ITS-G5 and even 4G LTE V2X specifications, besides other limitations such as distributed data association and synchronization.
Table 2.7 summaries the relevant technologies that could provide explicit V2X range-dependent measurements. Some technologies can support the exchange location-range-dependent data and/or the acquisition of radiolocation metrics over V2V or V2I links. For exam-ple, though ITS-G5 has been mostly adopted for communication purposes, it can support limited ranging capability through RSSI measurements. On the contrary, IR-UWB is a technology primarily intended for accurate ranging but it can hardly communicate data at high rates (say above a few tens of Mbps) while achieving simultaneously sufficient transmission ranges (say, beyond about 100 m). Throughout this thesis, we thus build our CLoc framework in a gradually complex way. As a starting point, we fuse on-board GNSS positions with opportunistic RSSI readings based uniquely on ITS-G5 under simplified working assumptions first in Chapter 3, before considering more realistic V2V wireless channel and protocol constraints in Chapter 4. This first combination of technologies is intended as a nominal baseline (making opportunistic use of ITS-G5 only) and as such, it is expected to offer only quite moderate accuracy. As RSSI is neither accurate enough, nor reliable enough (as discussed in details in Appendix C), Chapter 5 presents a hybrid V2V CLoc scheme combining on-board GNSS and IR-UWB V2V TOF measurements while still using the ITS-G5 to communicate position estimates to neighboring vehicles. Our CLoc framework is completed in Chapter 6 to include inertial/DR sensors (and even possibly, camera-based lance detectors) under full V2X cooperation (i.e., including both V2V and V2I links, considering systematically ITS-G5 for data communication, along with IR-UWB TOF or ITS-G5 RSSI for range-dependent measurements).
To combine multiple information sources, we use a hybrid data fusion architecture mainly due to its flexibility for proof of concept, besides the following reasons. On the one hand, low level architectures are highly complex with more parameters to control, difficult to extend with new modalities, and they also require deep access to the devices (e.g.,
Table 2.7: V2X range-dependent measurement capabilities by today and prospective tech-nologies [8].
Maturity Technologies Frequency Metric Links
Today ITS-G5/IEEE 802.11p 5.9 GHz RSSI V2V/V2I
Today ZigBee/IEEE 802.15.4 2.4 GHz RSSI/PDOA V2V/V2I
Today IR-UWB/IEEE 802.15.4a or proprietary 4 GHz TOA (TOF)/TDOA V2V/V2I
Prospective 4G LTE V2X 2 GHz Not defined V2V/V2I
Prospective 5G mmWave V2X 30–100 GHz AOA, AOD, TOA V2V/V2I
Prospective WiFi extension 2GHz Not defined V2V/V2I
GNSS pseudoranges). On the other hand, high level architectures requires that all the involved sensors can independently estimate the state vector before fusing their results, which can not always be realized.
To implement the hybrid fusion architecture above, PF is chosen as core filter fusion engine due to its suitability to nonlinear and non-Gaussian dynamics. By using PF, we can make our study generic enough to possibly integrate other location metrics/technologies (considering the increasing number of sensors in today vehicles) which may be character-ized by complex models. Besides, the complexity of PF is not an issue in the vehicular context since the relative extra-cost to supply adequate powerful hardware and software capabilities looks still relatively reasonable (i.e., in comparison with the cost of the whole car).
Even if CLoc yet remains a very promising approach to enhance geo-localization, in particular in GNSS (partially) denied environments. The combination of V2V and GNSS information raises unprecedented and specific challenges that require in-depth understand-ing and careful assessment as follows:
• Asynchronism of CAM transmissions and local estimations among the involved ve-hicles (thus requiring advanced prediction mechanisms before fusing the received data);
• High computational complexity and high data traffic under exhaustive/systematic cooperation with all the available neighbors (thus requiring low-complexity and context-aware links selection mechanisms);
• Measurements space-time correlation under constrained vehicle mobility and refresh-ment rates (thus requiring correlation mitigation at both signal processing and pro-tocol levels);
• Limited CAM payloads and V2V channel congestion (thus requiring V2V message simplifications and transmission rate/power adaptation);
• Whenever both GNSS and accurate V2V ranging based on IR-UWB are available, propagation of location errors among vehicles and/or fusion filters overconfidence, depending on local GNSS quality and dispersion (thus requiring mitigation mecha-nisms at both signal processing and protocol levels);
• Poor GDOP along the dimension orthogonal to the road, due to highly constrained VANET mobility and topology;
• In challenging but common tunnel environments, prolonged GNSS outages and un-sustainable error accumulation of inertial sensors over time (e.g., gyroscopes), leading to the fast divergence of position estimates.
The previous key points will be addressed in the following chapters.