An integrated algorithm for ego-vehicle and obstacles state estimation for autonomous driving

Understanding of the driving scenario represents a necessary condition for autonomous driving. Within control routine an vehicle, it preliminary step motion planning system. Estimation algorithms hence need to handle considerable number information coming from multiple sensors, provide estimates regarding ego-vehicle and surrounding obstacles. Furthermore, tracking is crucial in obstacles state estimation, because ensures recognition during time. This paper presents integrated algorithm estimation obstacles’ positioning along given road, modeled curvilinear coordinates. Sensor fusion deals with two Radars Lidar identify track The has been validated through experimental tests carried on prototype vehicle.