Towards image-free object detection for autonomous vehicles under harsh environmental conditions

Abstract

There exist two key aspects to enable autonomous vehicles: Robust sensors to provide all relevant data concerning the vehicle's surroundings as well as algorithms to evaluate this data in real time. Apart from radar and ultrasonic sensors, optical sensors such as lidar and cameras are state-of-the-art in prototype autonomous vehicles. In adverse weather conditions, such as e.g. fog, snow, dust, heavy rain or poorly illuminated scenes, though, those sensors do not perform reliably. Recently, we proposed to use a time-gated-single-pixel-camera to not only significantly reduce the amount of recorded data but additionally filter ballistic object photons, i.e. suppress the effect of noise from the obscuring medium. Apart from generating 3D object information, such a system can operate fast enough to deal with the highly dynamic environment as well as respect eye-safety norms. Moreover, a time-gated-single-pixel-camera offers the ability of image-free detection of all relevant objects within the scene which speeds up data evaluation as well. Here, we want to report on our progress towards realizing such a system. We will demonstrate image-free object detection on simulated data. We realize multi-object detection by generating object heat-maps for the different classes. Additionally, we discuss the difficulties we have to overcome to robustly detect objects in real measured data and shortly present our prototype setup, which we have implemented on a car together with our partners from Fraunhofer