Probabilistic inference of visibility conditions by means of sensor fusion

With the help of advanced driver assistance systems (ADAS), today´s vehicles are already able to perform impressive perception tasks. Besides information about other traffic participants, the current environmental visibility condition is one key aspect to enable further development, especially in difficult scenarios and adverse weather conditions. This work presents a system to estimate the visibility range for both the driver and vision-based ADAS. On the basis of an existing probabilistic radar-camera vehicle tracking framework, individual visibility range measurements are deduced by monitoring camera measurements to vehicles already confirmed by the radar sensor. This individual track-level information is then combined with spatial and temporal memory to build a holistic system to infer the current visibility condition in a probabilistic way. Experiments on both synthetic and real-world data validate the proposed concepts. In addition, a conducted user study compares system outputs to human visibility perception on realword scenes.