Automotive Sensor Fusion for Situation Awareness

The use of radar and camera for situation awareness is gaining popularity in automotive safety applications. In this thesis situation awareness consists of accurate estimates of the ego vehicle’s motion, the position of the other vehicles and the road geometry. By fusing information from different types of sensors, such as radar, camera and inertial sensor, the accuracy and robustness of those estimates can be increased. Sensor fusion is the process of using information from several different sensors to compute an estimate of the state of a dynamic system, that in some sense is better than it would be if the sensors were used individually. Furthermore, the resulting estimate is in some cases only obtainable through the use of data from different types of sensors. A systematic approach to handle sensor fusion problems is provided by model based state estimation theory. The systems discussed in this thesis are primarily dynamic and they are modeled using state space models. A measurement model is used to describe the relation between the state variables and the measurements from the different sensors. Within the state estimation framework a process model is used to describe how the state variables propagate in time. These two models are of major importance for the resulting state estimate and are therefore given much attention in this thesis. One example of a process model is the single track vehicle model, which is used to model the ego vehicle’s motion. In this thesis it is shown how the estimate of the road geometry obtained directly from the camera information can be improved by fusing it with the estimates of the other vehicles’ positions on the road and the estimate of the radius of the ego vehicle’s currently driven path. The positions of stationary objects, such as guardrails, lampposts and delineators are measured by the radar. These measurements can be used to estimate the border of the road. Three conceptually different methods to represent and derive the road borders are presented in this thesis. Occupancy grid mapping discretizes the map surrounding the ego vehicle and the probability of occupancy is estimated for each grid cell. The second method applies a constrained quadratic program in order to estimate the road borders, which are represented by two polynomials. The third method associates the radar measurements to extended stationary objects and tracks them as extended targets. The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden.