Measurement data selection and association in a collision mitigation system

Abstract: Today many car manufactures are developing systems that help the driver to avoid collisions. Examples of this kind of systems are: adaptive cruise control, collision warning and collision mitigation / avoidance. All these systems need to track and predict future positions of surrounding objects (vehicles ahead of the system host vehicle), to calculate the risk of a future collision. To validate that a prediction is correct the predictions must be correlated to observations. This is called the data association problem. If a prediction can be correlated to an observation, this observation is used for updating the tracking filter. This process maintains the low uncertainty level for the track. From the work behind this thesis, it has been found that a sequential nearest- neighbour approach for the solution of the problem to correlate an observation to a prediction can be used to find the solution to the data association problem. Since the computational power for the collision mitigation system is limited, only the most dangerous surrounding objects can be tracked and predicted. Therefore, an algorithm that classifies and selects the most critical measurements is developed. The classification into order of potential risk can be done using the measurements that come from an observed object.