Recently, the problem of fully autonomous navigation of vehicle has gained major interest from research institutes and private companies. In general, these researches rely on GPS in fusion with other sensors to track vehicle in outdoor environment. However, as indoor environment such as car park is also an important scenario for vehicle navigation, the lack of GPS poses a serious problem. This study presents an approach to use WiFi Fingerprinting as a replacement for GPS information in order to allow seamlessly transition of localization architecture from outdoor to indoor environment. Often, movement speed of vehicle in indoor environment is low (10-12km/h) in comparison to outdoor scene but still surpasses human walking speed (3-5km/h, which is usually maximum movement speed for effective WiFi localization). This paper proposes an ensemble classification method together with a motion model in order to deal with the above issue. Experiments show that proposed method is capable of imitating GPS behavior on vehicle tracking.
The current health situation with the use of masks complicates the analysis of gaze and head direction in driver monitoring systems based on facial detection since landmarks are not working properly. Due to this issue, the need to solve occlusion problems using an alternative method to the current ones has increased. On the other hand, the deployment of these systems inside the vehicles must be carried out in the least intrusive way possible for the driver. This article presents an approach for driver distraction analysis based on the driver's eyes without using landmarks applying Deep Learning methods, and the study of different parameters such as detection speed for the deployment of the best accuracy-speed method in an embedded platform. Different state-of-the-art and open source neural networks have been used and tuned to address our current problem. On the other hand, as is well known, training these models requires an enormous amount of data. In the case of gaze, there are very few data sets dedicated specifically to it. UnityEyes software has been used to create the training and test datasets for the system since it creates the necessary amount of data needed by the models easily.
This work presents the implementation of an adaptable emergency braking system for low speed collision avoidance, based on a frontal laser scanner for static obstacle detection, using a D-GPS system for positioning. A fuzzy logic decision process performs a criticality assessment that triggers the emergency braking system and modulates its behavior. This criticality is evaluated through the use of a predictive model based on a kinematic estimation, which modulates the decision to brake. Additionally a critical study is conducted in order to provide a benchmark for comparison, and evaluate the limits of the predictive model. The braking decision is based on a parameterizable braking model tuned for the target vehicle, that takes into account factors such as reaction time, distance to obstacles, vehicle velocity and maximum deceleration. Once activated, braking force is adapted to reduce vehicle occupants discomfort while ensuring safety throughout the process. The system was implemented on a real vehicle and proper operation is validated through extensive testing carried out at Tecnalia facilities.
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