Airborne collision avoidance and other applications of time/frequency

Operations in the Next Generation Air Transportation System (NextGen) are planned to have a reduced dependency on primary surveillance radar and use Automatic Dependent Surveillance - Broadcast (ADS-B) to provide aircraft state information to Air Traffic Control (ATC) and onboard collision detection and avoidance systems. With the ongoing integration of Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS), ADS-B is expected to become the main surveillance input to UAS collision avoidance. Traffic Alert and Collision Avoidance System (TCAS) is unable to meet requirements in terms of adaptability to future operations with reduced separation margins, addition of new user groups (e.g., UAS), and support of additional surveillance inputs. Airborne Collision Avoidance System (ACAS X) is being designed to meet these requirements and to increase surveillance, alerting, and resolution performance. The RTCA has released Minimum Operational Performance Standards (MOPS) to provide system and operational performance characteristics that ADS-B must provide. This paper analyzes recorded ADS-B data from test flights that were performed in the Continental U.S. (CONUS) and verifies measured performance with specified operational characteristics in terms of coverage, accuracy, and integrity.

<div class="htmlview paragraph">Since 1955 the aviation community has been investigating the feasibility of an airborne collision avoidance system. These studies have included analytical and engineering design efforts as well as flight simulation tests and actual flight tests. The majority of the effort expended during these investigations has been devoted to the engineering aspects of the systems. Of equal importance in meeting the overall objective of collision avoidance is the transmission of system information to the crew in a form that can be used effectively in the operational environment. The Boeing Commercial Airplane Company fulfilled a contract for the FAA, using simulation to help determine flight deck display requirements and operational procedures for the traffic alert and collision avoidance system (TCAS II) in commercial transport aircraft.</div> <div class="htmlview paragraph">Using the experience gained from these studies, this paper describes the type of efforts required to develop pilot factor characteristics for future collision avoidance systems it addresses man-machine interface requirements to ensure that the system is compatible with the overall aircraft system. It presents some of the functional requirements that the display and control components of the system must meet to accomplish separation assurance. Some of the subjects that are discussed include: a) the impact of the collision avoidance system utilization philosophy on the display requirements; b) the importance of designing the collision avoidance display system so that it can be integrated into the flight deck environment; c) techniques for incorporating realistic human performance estimates into the system performance assumptions; d) the effect on system performance of using natural response patterns; e) techniques that can be used to generate pilot response time commensurate with the situational requirements; f) the role of simulation in the design process; and g) consideration for certification during the development of a collision avoidance system.</div>

A collision-avoidance (CA) algorithm is developed and implemented that is applicable to many different unmanned aerial systems (UAS), ranging from a single platform with the ability to perform all collision-avoidance functions independently to multiple vehicles performing functions as a cooperative group with collision-avoidance commands computed at a ground station. The algorithm leverages advances in several theoretical fields, including robotics, homing guidance, and airspace management, and considers several approaches to conflict detection and resolution, including the collision cone approach. The collision-avoidance system is exercised and tested by using operational hardware and platforms. Novel developments by using an aggregated collision cone approach allow each unmanned aircraft to detect and avoid collisions with two or more other aircraft simultaneously. The collision-avoidance system is implemented by using a miniature unmanned aircraft with an onboard autopilot. Various simulation and flight test cases are used to demonstrate the algorithm’s robustness to different collision encounters at various engagement angles. The flight test results are compared with ideal, software-in-the-loop, and hardware-in-the-loop tests. The results presented are the first known flight tests of two or more unmanned aircraft systems equipped with the same global, three-dimensional, geometric collision-avoidance system.

Both ground-based ATC (Air Traffic Control) and airborne CAS (Collision Avoidance Systems) are needed now and in the future to cope with the problem of collision aloft. They should be considered mutually complementary. The limited form of the CAS known as a PWI (Proximity Warning Indicator) is believed not to be sufficient for safety. The two essential elements of a CAS are 1) an indication of the effective maneuver to secure adequate miss distance, and 2) effective means for discovering and assessing the original risk. Miss distance produced by a maneuver never exceeds maneuver displacement. The ratio of the two may be low unless escape time is of the order of 30 seconds or more. In nearly all cases increasing escape time is more effective to insure adequate miss distance than is increasing maneuver severity. The preferred form of CAS would be self-sufficient, but this form appears not to be feasible for basic physical reasons. Cooperative CAS presents many problems, but is believed to have sufficient promise of success to justify careful study and substantial development effort.

With the development of marine technology, the research of unmanned surface vehicles (USVs) has received wide attention from the world. Designing effective collision avoidance algorithm in dynamic sea environment is necessary for the navigation of USVs. This paper describes a collision avoidance system (CAS) with COLREGs compliance to improve autonomous navigational ability of USV. For small podded propulsion USV, a three-DOFs maneuvering model is established and the parameters of model are determined by the parameter identification method based on field experiments. About the COLREGs rules, the different maneuvering behaviors are summed up according to encounter situation and collision risk assessment. In the CAS, finite control set model predictive control is adopted, and COLREGs rules are taken as an evaluation standard in the quality function. Finally, the system is verified to avoid multiple dynamic obstacles and compliance vessel in the simulation and real environment, and the results of collision avoidance under four encounter situations are described in detail.

This research develops a performance evaluation model of humans in avoiding ship collision situations compared to the AI ship collision avoidance system (CAS). A human, the remote operator (RO) of Maritime Autonomous Surface Ships (MASS), ought to make compact collision avoidance (CA) decisions to secure safety and efficiency during remote operations. Hence, evaluating RO’s CA performance is important; however, research on developing evaluation methods concentrates merely on evaluating trainees based on instructor’s guidelines, while artificial intelligence (AI) makes CA decisions through parameter-based calculation. Therefore, this research proposes a CA performance evaluation model for RO of MASS based on CAS intervening of RO trainee’s simulation training in ship collision avoidance. In each time interval, the RO’s decisions showed divergent behaviors under given situational conditions compared to the CAS’s behaviors in equivalent situations. Findings denote the benefits of CAS intervention methods and RO performance evaluation model based on the proposed performance features.

Collision Avoidance Systems (CASs) are attracting a lot of attention as one of the most preferred solutions for advanced driver assistance and autonomous driving. However, scene understanding, which is an essential functionality in CASs, remains a major challenge mainly due to the need for real-time understanding of highly dynamic and complex environment. In this research, a number of robust and low complexity vision based scene understanding techniques for collision avoidance on roadway have been proposed. It has been well recognized in the literature that road surface detection in a dynamic environment is both challenging and computationally intensive. An efficient non-parametric road surface detection algorithm that exploits the depth cue is proposed to overcome the limitations of existing road surface detection methods. Unlike existing methods that attempt to fit the road surface into rigid models, the proposed method results in low computational complexity, mainly due to the reliance on four intrinsic road scene attributes observed under stereo geometry. It has been demonstrated that the proposed method is capable of detecting both planar and non-planar road surfaces. Extensive experimental results using three challenging benchmarks (i.e. enpeda, KITTI stereo/flow, and Daimler) show that the proposed road surface detection algorithm outperforms the baseline algorithms both in terms of detection accuracy (up to 23.12%) and runtime performance (up to 95.00%). Next, robust and low complexity algorithm for computing the ego-vehicle’s motion state is proposed. The proposed method estimates the ego-motion of the vehicle by first employing a novel pruning technique to reduce the computational complexity of the corner feature detection process without compromising on the quality of the extracted corner features. A robust and compute-efficient KLT tracker is proposed to facilitate the generation of the feature correspondences. Finally, an early RANSAC termination condition is introduced to the Gaussian-Newton optimization scheme to achieve rapid convergence of the motion estimation process. Evaluations based on the KITTI odometry benchmark show that the proposed visual odometry method outperforms the baseline algorithms both in terms of accuracy (up to 48.36%) and runtime performance. In addition, the proposed algorithm is placed among the top 15% when evaluated using the well-known KITTI odometry platform. Methods for robust and low complexity stereo-vision based obstacle detection and tracking are proposed. Unlike the works that focus only on the detection of vehicles or pedestrians, the proposed obstacle detection method relies on u-v disparity space to detect all obstacles in the scene. A Space of Interest (SOI) is defined to greatly reduce the search space of obstacles prior to employing adaptive hysteresis thresholding and connected component labeling techniques to segment SOI into sets of obstacles. Method for tracking obstacles across frames is also proposed by constructing a distinctive object appearance model. A number of strategies to further increase the distinctiveness and reduce the computational complexity for constructing the object model are also adopted. Finally, an online multi-object tracking framework is proposed by integrating the obstacle detection and data association modules in a robust way. Evaluations using the KITTI tracking benchmark confirm that the proposed obstacle detection and tracking method outperforms the baseline algorithm in terms of tracking accuracy by up to 51.78%. In addition, compared to the baseline algorithm that achieves about 0.23 frame per second (fps), the proposed method lends well for real-time performance with 20 fps. Finally, an efficient and robust risk assessment framework is proposed by integrating the obstacle detection and tracking, and visual odometry methods proposed in this thesis. The Extended Kalman Filter is customized to enhance the robustness of the predicted trajectory of the obstacles for assessing the collision risk. The robustness of collision prediction has been enhanced by accommodating positioning uncertainty. Evaluations based on the KITTI tracking dataset demonstrate that the proposed method are capable of robust and efficient assessment of the collision risk in diverse traffic scenarios. The proposed vision based scene understanding techniques in this research have paved the way towards realizing a real-time capable collision avoidance system that is both affordable and dependable.

In this paper, a new collision avoidance switching system is proposed to address the lack of adaptability of intelligent vehicles under different collision avoidance operating conditions. To ensure the rationality of the collision avoidance switching strategy for intelligent vehicles, the NGSIM road dataset is introduced to analyse the driver’s collision avoidance behaviour, and a two-layer fuzzy controller considering the overlap rate is established to design the collision avoidance switching strategy. In order to achieve real-time collision avoidance system activation, a lane change collision avoidance model based on MPC control is also developed. Finally, a simulation environment was created using Matlab/CarSim for simulation verification. The simulation results show that the collision avoidance switching system is more responsive and has a shorter start-up distance and is more adaptable to different driving conditions.

The scan-100 collision Avoidance System (CAS) has been designed to assist in the avoidance of collision at sea by automating the radar plotting functions in a small digital computer linked to the ship's radar. A clear and easily understandable display is provided to present numerical data and computer-generated plots to the ship's officer for evaluation of the radar data and for planning a safe and timely action to avoid collisions. This paper discusses the system design philosophy as implemented and tested in an engineering prototype of the SCAN-100 CAS. This philosophy utilizes an integrated man-machine concept which employs the best capabilities of each. The routine functions of radar tracking and plotting are performed by the system, which then presents the information to the operator in the form of displays combining graphic plots and numerical data. Automatic alarms are provided by the computer, one based on closest-point-of-approach (CPA) and another based on target arrival at a guard-ring distance. A vital requirement for the effective operation of such a radar-based system is the processing of radar data prior to tracking. In particular, the system must distinguish between receiver noise, sea clutter, and valid targets. The SCAN-100 uses a radar preprocessor, especially designed for this purpose, as well as computer processing to achieve the necessary radar signal enhancement. Several aspects of this processing technique are discussed in the paper. The system has recently undergone testing. Results of these tests are also described.

In-vehicle multiplexing and networking, including sharing information and control between different in-vehicle data buses and between the vehicle and the infrastructure, provides both opportunities and challenges for safety system designers. This paper provides an overview of in-vehicle networking and discuses trends that affect the application of in-vehicle networking to future collision avoidance systems. An overview of the classification of vehicle networking developed by the Society of Automotive Engineers (SAE) as well as descriptions of several emerging in-vehicle network standards provides background information necessary to understand the potential use of in-vehicle data buses. While most existing collision avoidance warning systems use only dedicated wiring, some emerging safety systems are beginning to employ in-vehicle networking to enhance their performance. Because use of in-vehicle networks requires careful design, the paper presents simulation results of network performance when supporting safety systems including collision avoidance systems. These results not only indicate the types of systems that can be supported by the various data bus standards, but also identify key design parameters. In addition, the paper discusses key trends such as internetworking between multiple in-vehicle data buses.

This paper presents a collision avoidance system based on human-robot interaction for mobile robots, which are working alongside to humans. In the future work environments, mobile robots will work side by side to humans. This raises big challenges related to the safety of the human and the ability of the robot to identify the people, interact with them and avoid physical accidents to them. To realize these concepts, a human-robot interaction system together with a collision avoidance system are developed to enable a safe navigation of the mobile robots. In this system, when a robot meets people in its path, it will try to interact with the human to execute one of the three actions: move forward, move backward, and collision avoidance; or the robot will implement the collision-avoidance autonomously when no people interacted with it. The interaction is based on the gestures obtained from the Kinect 2.0 sensor, and the system was tested using a H20 Robot (Canada). The experimental results proved the validity of the proposed system in interacting with the humans, and avoiding them.

To reduce the possibility of mid-air collisions, Federal Aviation Administration has developed the Traffic-Alert-and-Collision-Avoidance-System. TCAS is mandated worldwide on all large aircraft. In this paper, an overall description of the existing TCAS system is presented, followed by a detailed description of the system and its functional performance. The current collision avoidance system has been in operation for more than a decade. But major transformations to the National-Airspace-System are being implemented through FAA's Next-Generation-Air-Transportation-System in the coming years and that will require substantial modification to the existing system. The development of such new collision avoidance system which is Airborne-Collision-Avoidance-System-X has been pioneered. This survey article also describes the basic concept behind ACAS-X and briefly demonstrates how this new flexible approach in collision avoidance logic is fully compatible with NextGen airspace procedures and technologies. The aim of this work is to review, understand and compare the current and next generation aircraft collision avoidance system.

Since some years, research on the use of unmanned aerial vehicles (UAVs) has been very popular, and because of that a wide range of commercial and military applications already exists. Nowadays, the trend towards autonomous flying is growing steadily. Autonomous flying requires UAVs to have the ability to navigate in urban or other challenging environments without causing collisions or endangering humans. To achieve this objective, a safe and reliable collision avoidance system (CAS) needs to be used. In particular, a CAS needs to successfully sense and detect a possible collision with an object to efficiently avoid the obstacle. Multiple different techniques exist to implement a CAS. In this paper, we propose a collision avoidance protocol which is based on magnetic attraction and repulsion forces. The results show that, with the use of our protocol, collisions are safely avoided in various scenarios, while maintaining the overhead time to a minimum.

Many challenges arise when attempting to use unmanned aerial vehicles (UAVs) in indoor environments, such as the lack of a GPS signal for use in navigation and the smaller margin of error in movements. Typically, those challenges are addressed by using a collision avoidance system. However, most commercially available collision avoidance systems are expensive, limited in suppliers, and are restricted to use on a specific platform. Additionally, some of the collision avoidance systems choose to forego obstacle detection in one or more directions, usually the upward direction. This work proposes that it is possible to develop a custom, low-cost collision avoidance system with modular capabilities, allowing it to be adapted to any UAV platform. The feasibility of the proposed system was determined by creating a single-direction prototype that was implemented on a small quadcopter and tested by flying the quadcopter towards a wall at slow speeds. To develop the system's control algorithm a model of a quadcopter was built. Using the model, a feedback control algorithm was developed and tested via simulation with the model. The feasibility of the proposed collision avoidance system is promising with the prototype able to prevent the quadcopter from colliding with a wall. However, further refinement in the methodology and techniques used to develop the system is needed to improve performance and reliability of the system, especially as obstacle detection is added in other directions of motion.

A collision avoidance system, also known as a pre-crash system, forward collision warning system, or collision mitigation system, is a sophisticated driver-assistance system that aims to avoid or mitigate the severity of a collision. For this research, collision avoidance system will be fabricating to show that this system can detect avoidance range before apply the braking action to prevent collision. The ultrasonic sensor will be used in this system to detect the avoidance range. In this collision avoidance system, there will be uses of Field Programmable Gate Array (FPGA) and Complex Programmable Logic Device (CPLD). This research will observe how to implement FPGA and CPLD in the collision avoidance system using VHSIC Hardware Description Language (VHDL). The VHDL will be done in Quartus II 15.0 Software. In this research, Terasic DE-10 Standard board has been used. It contains FPGA microcontroller model Cyclone V SoC 5CSXFC6D6F31C6N. Max II board is used because it contains CPLD microcontroller model EPM240T100C5.