Detection of moving and stationary objects at high velocities using cost-efficient sensors, curve-fitting and neural networks

In recent years, driver-assistance systems have emerged as one major possibility to increase comfort and - even more important - safety in road traffic. Still, cost is one major hindrance to the widespread use of safety systems such as lane change or blind spot warning. To facilitate the widespread adoption of such assistance systems, thus increasing safety for all traffic participants, the use of cost-efficient components is of crucial importance. This paper investigates the usage of cost-efficient, widely used ultrasonic sensors for blind spot warning at high velocities. After discussing the requirements and setup of such a system a model-based approach for the detection of moving and stationary objects is outlined. The sensor-signal is compared with a precalculated curve data base and the correlation-coefficients are feeded into a neural network. To revise its performance the concept at hand is qualitatively and quantitatively evaluated in real road traffic situations under different driving conditions.