Abstract

Because of the key characteristics of Unmanned Aerial Vehicles (UAVs), removal of pilot, UAVs will be highly suited for repetitive, dirty, and dangerous operations. A wide range of civil and military applications are being explored in the community (Clapper et al., 2007). As a result, UAVs are given serious considerations in worldwide, making them the next step in evolution of aviation. Whatever missions are chosen for UAVs, their number and use will significantly increase in future. Currently, UAVs do not have convenient access to civil and military operation theatres due to their inability to provide an equivalent level-of-safety comparable to see-and-avoid requirements for manned aircraft. The current procedure requires a certificate of authorization be applied for every mission. Obtaining such an authorization may take more than a month. This lengthy process is not in line with increasing number of UAVs development. Therefore, an autonomous collision sensing, detection, awareness and avoidance system will be a key enabler for the integration of unmanned with manned aircraft in a shared airspace. The main objective of the Collision Avoidance System (CAS) is to allow UAVs to operate safely within non segregated civil and military airspace on a routinely basis. For this purpose, the UAV must be able to identify and be identified by the surrounding traffic. The diversity of UAVs and their missions involve a wide-range of system operating concept. Current unmanned aircraft range in size from small hand launch vehicles to large fixed-wing UAV with a wing span similar to Boeing 737. In addition, some UAV autonomously, semiautonomous or completely guided by ground pilot. Furthermore, unmanned vehicles cruise speed, climb/dive rate, turn rate and operating altitudes are similarly varied. Therefore, many CAS methods were proposed to account for that variation and to ensure that the unmanned aircraft efficiently avoids other cooperative traffic while also avoids fixed and moving obstructions such as terrain, obstacles and no flying zones. Numerous technologies are being explored in the community addressing CAS systems. Much of the research in collision avoidance methods for UAVs had been imparted from the air traffic management, maritime and mobile ground robot research communities. However, aircraft complicates the avoidance problem by added dynamic constraints that must be fulfilled for Document type: Part of book or chapter of book

Abstract

Purpose Advanced Driver Assistance Systems (ADAS) have been among the key innovations in the automotive market for over a decade, since they promote traffic safety. This tendency is strengthened even more lately, with the introduction of the autonomous vehicles. A plethora of ADAS exist in the market today, using common warning thresholds for all drivers. However, since we are not all driving the same way, by offering common systems for all the drivers, neither the acceptance nor the effectiveness levels of ADAS are optimal. This manuscript attempts to optimize the Collision Avoidance System (CAS) warning, through intelligent personalized algorithms. Methods Starting with the identification of the dynamic parameters for driving behaviour modeling on the longitudinal road axis, the personalization parameters for ADAS are derived that form the basis for the algorithms developed. Also, based on literature studies, the safety boundaries for warning provision by the CAS are set and implemented in the algorithms. Results Specific personalized algorithms for the longitudinal road axis behaviour are developed, based on Time to Collision and Time Headway. The proposed algorithms based on Time Headway were assessed on-road with 10 drivers and were positively evaluated by the majority of the participants, with a varying degree of reliability and usability. Conclusions Based on the results obtained, it can be concluded that with the proposed algorithms, the initial hypothesis of the paper is verified, i.e. personalised warnings would get a greater acceptance by the drivers, of course without braking the safety limits. Further improvements of the algorithm could be achieved, possibly through a better determination of the car following event, since its definition includes a few assumptions. Document type: Article

Abstract

International audience; The Internet of Things (IoT) consists in connecting every aspect of daily and professional life to a common infrastructure, in order to improve considerably the efficiency of otherwise unthinking objects. The huge scale on which they operate, as well as the lack of adequate standards and infrastructures makes the development of IoT applications a task of gradually growing complexity. The objective of this work is to define a formal model with BiAgents (Bigraphical Agents) for IoT applications, based on a suggested generic multi-layered architecture. We show how bigraphs support the structural aspects modelisation of these applications while the agents specify their analytical and decisional aspects. We proceed then to the edition and execution of our model using the bigraph implementation tool (RCTool4 Bigraphs), and through the exploitation of its model-checker, we formally verify its most critical property. As a practical example, we study the case of a Collision Avoidance System.