Self-driving car technology requires detectors capable of sensing a car’s environment, also in situations of limited visibility like bad weather conditions. Radar-based sensors have emerged as an essential component of driver assistance systems and self-driving vehicles, as they can robustly distinguish nearby pedestrians and other traffic-relevant objects. Apart from being applicable in bad weather, artificial recognition systems also need to be capable of dealing with so-called non-line-of-sight (NLOS) situations, when the line of sight between detector and object is obstructed. In traffic, NLOS situations occur when pedestrians are blocked from sight; for example, a child behind a parked car, about to run suddenly into the street. Now, Shouhei Kidera from the University of Electro-Communications and colleagues have developed a radar-based detection method for recognizing humans in NLOS situations. The scheme is based on reflection and diffraction signal analysis and machine-learning techniques.

Quantitative information about a physical system comes in the form of numbers following from the system’s mathematical description — equations capturing the physical processes involved. In classical physics, it is in principle possible to retrieve, by means of measurements, the complete information of a system. According to quantum mechanics, however, one can never obtain all information of a system with infinite precision because of the quantum-mechanical uncertainty principle stating that certain pairs of quantities (e.g. momentum and position) cannot be measured simultaneously with absolute certainty.