Cooperative Localization

The general principle of vehicular CLoc works in two main phases.

In the first phase, each vehicle piggybacks its position-dependent data (e.g., at least its absolute GNSS position) in a “Beacon” sent over V2X communication links⁶.

In the second phase, through the reception of these “Beacons”, a given “ego” vehicle be-comes aware of theabsoluteposition estimates of its neighbors. The optional task consists of using the “Beacon” signal statistics to sample relativeposition-dependent information from these “virtual anchors”. So as to perform localization or localization enhancement, data fusion thus combines the multiple sources of information including:

• Data from other entities representing their local observations through V2X commu-nications (e.g., GNSS data, sensor data, etc.),

• Data from communication signals (e.g., received signal strength indicator (RSSI), TOA, time difference of arrival (TDOA), AOA, etc.),

• Data from on-board sensors (e.g., GNSS data, sensor data, digital map, etc.).

CLoc with V2X Measurements

Extensive research has been devoted to incorporate V2V measurements with diverse sources of information such as GNSS/GPS data, car’s kinematics (speed, acceleration, heading, etc.), and even prior knowledge of the road map using different CLoc architec-tures. With the advent of ITS-G5/DSRC standards, V2V RSSI (or V2V distance estimates based on RSSI) or Doppler shift is primarily utilized in this vehicular CLoc context.

In [58], the authors present a distributed cooperative solution based on a dissimilarity matrix composed of RSSI-based distance estimates. Position estimation is performed by a least squares (LS) estimator yielding accuracy improvement over stand-alone GPS, while using GPS estimates for initialization purposes only. The authors extend this work by additionally incorporating vehicle’s kinematics and road constraints based on a extended

6To remain technology neutral, a “Beacon” is a message periodically broadcast by each node, while V2X refers to any technology capable of D2D communication in a vehicular context.

Kalman filter (EKF) in [59]. However, collecting V2V distance measurements in mesh topology, which is challenging in a fast-moving VANETs, is not discussed the works.

Additionally, the RSSI-based ranging error less than 10 m is not realistic according to [60]

(see also Appendix C.1). A similar work is proposed in [61] together with a study on communication overhead, its impacts on CLoc accuracy, and protocol improvements. The authors claim to also improve neighboring vehicles’ positions at the same time. A very similar CLoc problem is solved by a fast multidimensional scaling (MDS) in [62] and the MDS coupled with a PF prefiltering V2V distances in [63]. A simplified CLoc architecture fusing V2V distance measurements in star topology (to avoid range vector exchanges) and GPS measurements in an EKF is found in [64]. However, since the trilateration is performed with the GPS-based positioned neighbors, the sub-meter localization accuracy cannot be achieved. Particularly, the best scenario with 14 neighboring vehicles only yields about 3.5 m error.

In [65], the target vehicle’s position is trilaterated using the neighboring vehicles’ po-sitions and range information (assumed to be perfect) in a LS technique. The algorithm is initialized with Kalman filter (KF)-based GPS with kinematics. This also implies that the neighbors communicate their enhanced positions and associated uncertainties rather than the GPS ones.

Different from the majority of CLoc techniques employing distances between the par-ticipating nodes, it is proposed in [66] to fuse on-board GPS position and velocity with those of the neighbors and Dopller shifts of the received signals over V2V ITS-G5 in an EKF. An accuracy improvement of up to 48% over the GPS accuracy is reported but still without achieving the sub-meter level. Very recently, a GNSS/ITS-G5 integrated archi-tecture considering both Doppler and range from DSRC is developed in [67] to improve the CLoc performance. For the data fusion, a modified cubature KF is applied to account for the probable anomalies in state estimation.

With an increased number of sensors on vehicles, [68] fuses information from on-board GPS, on-board ranging sensors, and DSRC messages using an EKF. The association for the data coming from independent DSRC and ranging sensors is based on the minimum Mahalanobis distance and Chi-square test. If a neighboring vehicle is discovered by both DSRC and on-board sensors, the corresponding relative distance is cross-checked and corrected. Another strong point lies in the possibility to synchronize all the position

information in DSRC messages using an open-loop KF.

Furthermore, an integrated localization algorithm that exploits all possible data sources, relying on a WLS estimator and exploiting various data sources (e.g., GPS, RFID, V2X and DR), has been proposed in [69]. Another recent work [70] improves GPS vehicle posi-tioning by the fusion with V2X RSSIs through IEEE 802.11p interfaces and inertial sensors on driver’s smartphone, and map information if available. A two-state Bayesian framework is proposed including an UKF for prefilering the heading estimation using smartphone in-ertial sensors, and a core PF to combine all the aforementioned information. The authors perform a comparative evaluation with different combinations of the location information sources using real-word data in an urban scenario i.e., the city of Porto. However, the localization accuracy gain of the fusion approaches against the stand-alone GPS is limited (location errors of 9.47 m and 9.8 m for GPS+V2V+map and GPS, respectively for full trajectory).

CLoc without V2X Measurements

CLoc can also be performed with the information contained in the messages only, without requiring explicit V2V measurements, contrarily to the methods discussed in the previous section. At least a couple CLoc methods have been extensively investigated including CLoc based on GNSS pseudorange information or map exchanges.

CLoc techniques with GNSS pseudoranges are commonly implemented in one of the two following schemes. On the one hand, in [71], a tightly coupled GPS/INS integration is adopted for relative CLoc. Based on the exchange of GPS pseudoranges and vehicles’

motion through V2V communications, the relative distances between vehicles are tracked using a PF. Though simulated data of two vehicles is demonstrated, this work can be extended to estimate the relative position of multiple neighboring vehicles, which is com-pleted later in [72]. The latter even obtains experimental results. In [73], V2V distances are estimated based on the sharing of GPS pseudorange measurements through DSRC and a WLS method. Then, a distributed location estimation algorithm uses these distances and the shared GPS fixes to compute the target vehicle’s absolute position.

Instead of sharing the GNSS pseudoranges, other studies such as [74, 75] propose to broadcast GNSS pseudorange correction through V2V communications, thus, the receivers can improve their GNSS positions by compensating the common errors. In other words,

the principal of DGNSS is extended from fixed base stations to dynamic base stations such as vehicles. Specifically, each vehicle estimates its position using all their on-board sensors and then the geometric ranges to satellites. It eventually subtracts the latter from the measured pseudoranges and broadcast this information to other vehicles. The receiving vehicles can include this correction in their measured pseudoranges from the same satellites to compensate the common errors. In [74], through simulation, the authors prove that a large amount of the error on the measured pseudoranges can be compensated (5x) and therefore, a more accurate positioning (3.5–6.5x) can be achieved whereas an experiment with 2 vehicles is presented in [75].

By sharing both GPS absolute positions and pseudoranges only via the V2V com-munications, in [76], Mattern el al. present a cooperative map matching method. Each receiving vehicle can then calculate relative positions between itself and transiting vehi-cles. Assuming that participating vehicles are within drivable areas, matching of groups of vehicles to a lane-level map is performed. This matching is based on the fact that match-ing of polygons to a map is less ambiguous than point map matchmatch-ing. A very similar approach appears later on in [77]. However, this approach requires that the geometry of the involved vehicles is good enough to remove ambiguity in 2-D space.

In [25], the authors discuss the possibility to exchange raw sensor data (i.e., radar, lidar, camera) between vehicles using 5G mmWave V2X to enlarge sensing range and improve automated driving functions as the current IEEE 802.11p and 4G LTE D2D do not support the Gbps data rates required for raw sensor data exchange. A simplified prototype so-called implicit cooperative positioning (ICP) is presented in [78] to jointly estimate the positions of sensing vehicles and sensed features. Specifically, vehicles jointly localize features (e.g., pedestrians, traffic lights, parked cars, etc.) in the surrounding areas using radars or lidars, and consider them as common noisy reference marks to refine their position estimates. Information on sensed features are simplified by Gaussian distributions fully described by their means and covariances, and further exchanged between cooperating nodes till convergence. However, the distributed data association issue, which may be very challenging (e.g., considering clouds of lidar-based detected points), still needs to be further investigated in depth in this very context and latency issues may be critical at high speed (i.e., the iterative solution requiring the exchange of a few packets between a pair of sensing nodes before achieving convergence).

Table 2.6: Vehicular communication capabilities by today and prospective technologies [7].

Maturity Technologies Throughput Delay Range

Today ITS-G5/IEEE 802.11p 3–12 Mbps ≈10 ms 300–1000 m

Prospective 4D LTE V2X ≈70 Mbps ≈50 ms (Mode 1)/≈10 ms (Mode 2) 300–900m

Prospective 5G mmWave V2X >10 Gbps 1 ms <200 m