Route guidance is an essential feature in current land navigation systems. In this navigation feature, the driver feeds the navigation system with the desired trip destination. The route guidance algorithm calculates the route for the driver to follow. The driver may make mistakes in following the intended (calculated route), and the route guidance system will have to adjust its instruction to correct this mistake. This corrective instruction will result in the driver having to spend extra time and the vehicle to consume more fuel to perform this correction. In addition this instruction may confuse the driver and cause hazard situation. A predictability feature can be added to the route guidance algorithm if the instantaneous MLP (IMLP) is calculated using the MLP algorithm. If the IMLP doest not match the pre-calculated route, the route guidance system may advise the driver of potential mistake in following the pre-determined route.

Map matching can also use the MLP information in an ambiguous road geometry scenario where the combined GPS/Map accuracy is not adequate to place the vehicle on the right road with a high confi dence. The MLP information provides the map matching algorithm with the expected position after the branching. This information can either increase the history weight (MLP matches the expectation that the vehicle will continue on the same road), or reduce the history weight (MLP indicates that the driver will take the branch).

The Service drive/Highway road scenario shown in Fig. 8 presents a difficult challenge for map matching due to the lack of map matching excitation. Both roads are parallel and close to each other. The heading angle/yaw rate information is not helpful, and the combined GPS/Map accuracy is not adequate to place the vehicle on the right road with a high confidence. A combination of the vehicle speed and the posted/advisory speed map database attribute can help in resolving such ambiguity.

Current commercial map databases are designed for navigation purposes. The accuracy of a commercial map is investigated in [2]. The accuracy of these maps is suffi cient for the navigation application in a large variety of road scenarios. However, they sometime fail in road scenarios like service drive/highway, highway/ exit ramp, fork, complex overpasses, and mountain area/single road. All of these scenarios could lead to placing the vehicle on the wrong road or off the road. The absolute and relative accuracies have been improved by the continuing replacement of the old map database shape points with ADAS quality shape points. However, the accuracy of the ADAS map is still inadequate in many of the branching scenarios and scenarios where the 3D information is required.

For the path prediction algorithm, placing the vehicle on the wrong road segment results in incorrect set of the road candidates, which leads to an incorrect MLP. In cases with correct vehicle position, the relative accuracy is the determining factor in path prediction. An accurate relative
placement of the shape points along the MLP means an accurate curvature distribution along this path. The rules and method of creating the map database (ADAS or older) can lead to very low relative accuracy in some road scenarios. An example of this is the connectivity rule, which requires of adding extra shape points solely for connectivity purposes. These added shape points are not part of the road geometry and can lead to wrong curvature values along the path. Other rules like the merging rule in connecting divided roads to undivided roads or vice versa, or connecting an on ramp with a main road can also lead to wrong
representations of the path geometry.

Considering the map as a sensor requires, as with any other sensor, having its error sources defi ned and modeled. It is also required to have corrective/updating capability. Furthermore, information such as height and super elevation are required to extend the usage of the map for other automotive applications.

The sensing capability of the map provides detailed information of the instantaneous road segment and the road segments ahead. An example of that is shown in Fig. 9. In this fi gure a vehicle (red arrow inside the circle) is approaching an exit ramp branching. The blue road is path 1 which consists of two segments: the segment that the vehicle is currently on (segment before branching), and the straight (highway) segment after
branching. The blue road segment followed by a magenta segment is path 2, which consists also of two segments: the segment that the vehicle is currently on (segment before branching), and the curved (ramp) segment after branching.

The path set in Fig. 9 is an example of how the navigation unit may output the map sensor data. The path data could be described by a number of curvature points along a look-ahead travel distance of the corresponding path. Each curvature point can be described by global latitude and longitude coordinates, vehicle centered true north/east coordinates, curvature value, confi dence value, number of lanes, and travel distance from the vehicle location.

Figure 10 shows application specific path data. In this scenario, the path prediction algorithm senses that the driver is most probably taking the exit ramp to the right. The curvature data and other data of the MLP are sent to the CSW threat assessment algorithm, which may initiate a CSW warning at some distance before branching.

The Map database can provide detailed information about the road segment at the vehicle position and the road segments ahead of the vehicle. This information, when processed, can be used for driver assistance and awareness applications such as ACC, FCW, CSW, PAFS, and SSW. In order to optimally use the map database, its error sources should be defi ned and modeled. In addition, it requires map corrective/updating capability due to the changing nature of the roads. Furthermore, information such as height or elevation and super elevation
are required to extend the usage of the map for other automotive applications. From a commercialization perspective, a standardized navigation system interface is recommended.

[1] NHTSA, “Status Report on USDOT Project “An Intelligent Vehicle Initiative Road Departure Crash Warning Field Operational Test”. www-nrd.nhtsa.dot.gov.

[2] T. Pilutti, F. Ibrahim, M. Palmer, and A. Waldis, “Vehicle Position Analysis of a Commercial Navigation System”, SRR-1999-0169, November 17, 1999.