In order to navigate safely, it is important to detect and to react to a potentially dangerous situation. Such a situation can be underlined by a judicious use of the locations and the uncertainties of both the navigating vehicle and the obstacles. We propose to build an estimation of the collision probability from the environment perception with its probabilistic modelling. The probability of collision is computed from integrals of Gaussians and takes into account the uncertain configurations and the volume of both the vehicle and the obstacles.

In order to navigate safely, it is important to detect and to react to a potentially dangerous situation. Such a situation can be underlined by a judicious use of the locations and the uncertainties of both the navigating vehicle and the obstacles. We propose to build an estimation of the collision probability from the environment perception with its probabilistic modelling. Then this probability is used for updating a braking order applied to our vehicle either to avoid or to mitigate a collision. The probability of collision is computed from a product of integrals of a product of Gaussians. The integrals take into account the uncertain configurations and the volume of both the vehicle and the obstacles.

Object detection is recognized as one of the most critical research areas for the perception of self-driving cars. Current vision systems combine visible imaging, LIDAR, and/or RADAR technology, allowing perception of the vehicle’s surroundings. However, harsh weather conditions mitigate the performances of these systems. Under these circumstances, thermal imaging becomes the complementary solution to current systems not only because it makes it possible to detect and recognize the environment in the most extreme conditions, but also because thermal images are compatible with detection and recognition algorithms, such as those based on artificial neural networks. In this paper, an analysis of the resilience of thermal sensors in very unfavorable fog conditions is presented. The goal was to study the operational limits, i.e., the very degraded fog situation beyond which a thermal camera becomes unreliable. For the analysis, the mean pixel intensity and the contrast were used as indicators. Results showed that the angle of view (AOV) of a thermal camera is a determining parameter for object detection in foggy conditions. Additionally, results show that cameras with AOVs 18° and 30° are suitable for object detection, even under thick fog conditions (from 13 m meteorological optical range). These results were extended using object detection software, with which it is shown that, for the pedestrian, a detection rate ≥90% was achieved using the images from the 18° and 30° cameras.

Using molecular psychophysics, temporal loudness weights were measured for 2-s, 1-kHz tones with flat, increasing and decreasing time-intensity profiles. While primacy and recency effects were observed for flat profile stimuli, the so-called “level dominance” effect was observed for both increasing and decreasing profile stimuli, fully determining their temporal weights. The weighs obtained for these profiles were basically zero for all but the most intense parts of these sounds. This supports the view that the “level dominance” effect is prominent with intensity-varying sounds and that it persists over time since temporal weights are not affected by the direction of intensity change.