Driver Assistance and awareness applications such as Adaptive Cruise Control (ACC) and Forward Collision warning need to identify the primary target in the host vehicle lane, which requires accurate estimation of the geometry of the road between the host and the target vehicle. Curve Speed Warning (CSW) also requires determining the geometry of the intended driving path to warn the driver of going too fast for an upcoming curve. Predictive adaptive front lighting can use the predicted road geometry to swivel the headlamps in the road curvature direction. Route guidance can use the MLP data to warn the driver of a potential mistake in following the calculated route. Map matching can use this data to improve its performance at ambiguous branching areas where the map matching position confi dence is low. The MLP is primarily determined by fusing vehicle signal data, lane marking information, and map database attributes. Real road results show an impressive benefit and performance from this approach.

The most likely path determination is achieved by designing a Look Ahead Module (LAM) that looks forward from the vehicle position to the lookahead distance. The LAM determines the most probable path of the vehicle using information from vehicle positioning, lane information, lateral velocity, and vehicle signals and state. The most probable path and other possible alternate paths can be predicted using the vehicle’s travel direction, the direction of the road, the vehicle lane, and the predicted directional change. This information is evaluated using a Cost Function to weight each parameter with respect to the influence that the parameter will have toward predicting the vehicle’s most probable path.

The LAM also uses the lookahead distance to assemble a candidate path subset that is projected out to a selected distance from the vehicle’s current position. If only one possible candidate path exists, it will be returned with 100% confi dence. Otherwise, a list of all possible candidate paths (and their associated confi dence levels) within the look-ahead distance will be calculated. The most probable path, i.e. the candidate path with the highest confidence level, is passed to the application (for example: CSW algorithm).

The MLP can be calculated using the map database information such as the shape point coordinates and the advisory speed or speed limit Map attributes, the lane boundary types from a vision system if available, and the yaw rate, vehicle speed, throttle, brake, turn signal from vehicle sensors.

In the road scenario shown in Fig.1, if the driver initiates a right turn signal before branching then this represents an indication that the driver intends to branch right, or to perform a lane change. If the boundary type of the driving lane indicates that the vehicle is not in the middle or left lane, then it is more probable that the driver will take the upcoming right branch. The probability of taking the branch is a function of the vehicle location from the branching point.

Visteon has used the GPS and map database as sensors. In the USDOTfunded Road Departure Crash Warning – Field Operational Test [1], Visteon developed Curve Speed Warning (CSW) functionality using
a commercial navigation system and map database. The CSW system warns the driver when the vehicle is traveling too fast for an upcoming curve by processing the map database geometry and attribute information. CSW uses the navigation system to place the vehicle position on the map, and then, the CSW algorithm looks ahead on the map, extracts all possible driving path candidates, determines the intended driving path, performs a curvature calculation on the geometric data of this path, and finally performs a threat assessment based on the vehicle speed and road curvature ahead. Figure 2 shows both single road and branching road geometries.


Adaptive cruise control and forward collision warning systems can use the MLP calculation to determine the in lane primary target. The functionality of ACC and FCW depends solely on determining the primary target in the host vehicle lane (Fig. 3). This requires accurate estimation of the road geometry between the host and the target vehicle. The host vehicle controls its speed based on the range and range rate measurements of this target. If the target becomes out of the host vehicle path, the ACC system resumes to regular speed control (cruise control). An undesired “resume” could happen in an exit ramp scenario (Fig. 4) where the host vehicle starts to accelerate to the set speed toward a low speed ramp. Such undesired ACC performance could be prevented by provided the ramp information from the map database ramp attribute.

Visteon’s Predictive Adaptive Front Lighting System (PAFS) uses the MLP calculation and processing to swivel the headlamps based on the upcoming MLP calculated curvature as shown in Fig 5. Swiveling the headlamps beam toward the upcoming road increases the visibility in that road.

Another application that can use the map database information is stop sign warning (SSW). The SSW informs the driver for an upcoming stop sign at a designed “time to reach”. The stop sign information is an attribute in the current commercial map database. Illustration of the system functionality is shown in Fig. 6.

Figure 7 illustrates the architecture of map database processing for the DAA applications. There are three main pieces in this architecture: first, a commercial navigation system module that provides the vehicle position on the map, second, the path prediction module that selects the MLP and performs a curvature calculation, and third the application module which uses the MLP information and other inputs to perform its function.