The images captured in fog conditions have degraded contrast, which makes current image processing applications sensitive and error prone. We propose in this paper an efficient single-image enhancement algorithm suitable for daytime fog conditions and, based on an original mathematical model, for computing the atmospheric veil, taking into account the variation in fog density to the distance. This model is inspired by the functions that appear in partition of unity in the differential geometry field. When observing images captured in fog conditions, usually the fog has a very low density in front of the camera and this density has a nonlinear increase with the distance, such that objects are no longer visible at greater distances. By using our mathematical model, we are able to obtain superior reconstructions of the original fog-free image when compared with traditional methods. Another advantage of our method is the ability to adapt the model in accordance to the density of the fog. A quantitative and qualitative evaluation is performed on both synthetic and real camera images. This evaluation proves that our mathematical model is more suitable for contrast restoration in both homogeneous and heterogeneous fog conditions. Our algorithm is able to perform contrast restoration in real time for both color and grayscale images.

An advanced driving assistance system (ADAS) must also take into account the weather conditions. One of the most dangerous weather conditions for driving scenarios is the presence of fog. So an important task for a driving assistance system is to detect the presence of fog, estimate the fog's density and determine the visibility distance of the driver. Our method is based on a single in-vehicle camera and is actually an improvement over existing fog detection solutions, in terms of speed and accuracy. We are able to detect day time fog in a wide range of scenarios, including urban scenarios. After detecting the presence of fog in the image and based on the fog's density we are able to compute the visibility distance and inform the driver about the environment's weather conditions.

The early recognition and understanding of the actions performed by pedestrians in traffic scenes leads to an anticipation of pedestrian intentions in advance and helps in the process of collision warning and avoidance in the context of autonomous vehicles. An environment with low visibility conditions such as night-time, fog, heavy rain or smoke increases the number of difficult situations in traffic. A complete and original model for assessing if a pedestrian is engaged in a street cross action using only infrared monocular scene perception is proposed in this paper. The assessment of a street cross action is done by the time series analysis of features like: pedestrian motion, position of pedestrians with respect to the drivable area and their distance with respect to the ego-vehicle. The extraction of these features emerges from the combination of a deep learning based pedestrian detector with an original tracking algorithm, a semantic segmentation of the road surface and a time series long-short term memory network based action recognition. In order to validate the proposed method we introduce a new dataset named CROSSIR. It is formed of pedestrian annotations, action annotations and semantic labels for the road. The CROSSIR dataset is suitable for several common computer vision algorithms: (1) pedestrian detection and tracking algorithms because each pedestrian has a unique identifier over the frames in which it appears; (2) pedestrian action recognition; (3) semantic segmentation of the road pixels in the infrared image.

This paper presents a method for the detection and localization of stop-lines and other horizontal road markings as bicycle or pedestrian crossings encountered in road environments. The few existing stop-line detection approaches found in literature are based on monocular vision, and therefore cannot infer high accuracy information from the visual data. The proposed solution uses a hybrid approach that combines 2D detections in grayscale images with stereo-vision based 3D validations in order to increase the robustness and accuracy of the results. Model based reasoning is also used to eliminate false positive situations and to detect exactly the objects of interest (stop-lines, pedestrian or bicycle crossings). The detected horizontal road markings can be used as landmarks for a very accurate longitudinal localization of the ego vehicle within intersection scenarios compensating missing GPS data or its lack of accuracy.

Miniature self-driving cars are intended to facilitate the research and development in the domain of autonomous vehicles. Algorithms developed for the tasks of perception, navigation, and control on such platforms enable fast implementation and testing in scenarios similar to the real world. In this paper, we present a novel methodology for developing the assistance system for a 1/10 scale car, in which we use a simulated GPS to position the vehicle and to navigate on the test track. We propose a method for lane detection and tracking that is robust and accounts for the cases when one or both lines of the lane are missing or not seen in the image. We also present a new solution for detecting road traffic signs. In addition, we have implemented an application for map visualization, that enables us to test the correctness of our algorithms. Our miniature vehicle is capable of successfully navigating from a start point to a goal, while taking into account the lanes, intersections, traffic signs, and can perform lateral parking.

As the technological advancement in the automotive field increases and the complexity of vehicle and infrastructure applications is extremely high, new directions and approaches are needed in this field. Supporting and developing vehicular applications dedicated to road safety by analyzing the current behavior of existing networks in various forms is imperative. This paper studies and implements a DSRC-type communications infrastructure that receives a set of controllable and adjustable indicators, which can provide messages to network drivers in a timely manner. The implementation is based on the 802.11p protocol and initially addresses pedestrian infrastructure or pedestrian safety, controlled areas, and perimeters that allow intelligent communications. The design and setting of the communication parameters in the lower layer of the DSRC stack for vehicle applications are part of this work, aspects that are also relevant in the case of autonomous vehicles.