A survey on current and next generation aircraft collision avoidance system

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>

Time/frequency technology provides a reliable aircraft collision avoidance system (CAS) that can operate in either synchronous or asynchronous modes. Precision time-ordered techniques of CAS provide both range and range-rate measurements in a one-way sense to all aircraft as well as ground stations within range of transmitted microwave signals. The cooperative system utilizes exact frequency references coupled with precise synchronization: control of frequency to 1 part in 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> and time to less than 1 µs. In addition to performing specific functions of protecting aircraft, the time/frequency CAS provides a means for wide dissemination of submicrosecond timing. Flying clocks, which are an integral part of the airborne CAS, have been providing transcontinental and intercontinental transfer of time since 1964. CAS ground stations can serve as depositories of time and frequency derived from flying clocks, satellites, Loran-C and Omega navigation systems, or television transfer referenced to national and international time/frequency standards. Airborne relay of CAS time provides a ready means for local transfer of time to users that are not within line of sight to a ground station. Thus time and frequency can be disseminated for applications other than collision avoidance. In the process of developing the technology and originating CAS, new systems for communication, position determination, navigation, and vehicle surveillance evolved. Such systems can be structured to operate at high data rates, provide improved accuracy and use less RF spectrum than existing techniques.

This paper discusses the requirements for surveillance and collision avoidance equipment in the National Airspace System (NAS), the current avionics equipage by the U.S. aircraft fleet, and the GA avionics market in general. The operational and functional requirements for a general aviation traffic alert system are presented accompanied by concept design, development and schedule information. The paper also discusses the issue of achieving compatibility with the existing Traffic Alert and Collision Avoidance System (TCAS) community. This paper contains only the understanding and views of the authors and is not intended to reflect the official position or view of the U.S. Government or Federal Aviation Administration (FAA). >

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.

The Next-Generation Airborne Collision Avoidance System (ACAS X) is intended to be installed on all large aircraft to give advice to pilots and prevent mid-air collisions with other aircraft. It is currently being developed by the Federal Aviation Administration (FAA). In this paper, we determine the geometric configurations under which the advice given by ACAS X is safe under a precise set of assumptions and formally verify these configurations using hybrid systems theorem proving techniques. We consider subsequent advisories and show how to adapt our formal verification to take them into account. We examine the current version of the real ACAS X system and discuss some cases where our safety theorem conflicts with the actual advisory given by that version, demonstrating how formal hybrid systems proving approaches are helping to ensure the safety of ACAS X. Our approach is general and could also be used to identify unsafe advice issued by other collision avoidance systems or confirm their safety.

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.

This paper describes the specification-based testing, analysis tools, and associated processes that were used to independently validate, verify, and ultimately provide for certifying, safety-critical software developed for the Traffic Alert and Collision Avoidance System (TCAS II) program. These tools and processes comprise an effective and Independent Validation and Verification (IV&V) activity applied to the Collision Avoidance Subsystem (CAS) software development process. A requirements specification language called the Requirements State Machine Language (RSML), originally developed by the University of California, Irvine (UCI), was employed for the specification of CAS. The end result is the next generation of TCAS II collision avoidance logic, referred to as Version 7, that is of a higher quality than its predecessors, meets the certification requirements of DO-178B Level B, and can be shown to satisfy the new operational requirements it was developed to address.

Collision avoidance systems, whether for manned or unmanned aircraft, must reliably prevent collision while minimizing alerts. Deciding what action to execute at a particular instant may be framed as a multiple-objective optimization problem has explored methods of efficiently computing the that can be solved offline by computers. Prior work optimal collision avoidance logic from a probabilistic model of aircraft behavior and a cost function. One potential concern with using a probabilistic model to construct the logic is that the model may not adequately represent the real world. Inaccuracies in the model could lead to vulnerabilities in the system when deployed. This paper evaluates the robustness of collision avoidance optimization to modeling errors.

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.

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.

Various collision avoidance systems (CASs) have been developed and employed in human-operated vehicles as well as more recently in autonomous vehicles. Most of the existing CASs perform an override function to actuate automatic emergency braking in a critical situation based on the current traffic information obtained from in-vehicle sensors or short-range vehicular communications. These CASs focus on the critical situation in the vicinity of the subject vehicle, which means they may have negative influences on the subject vehicle and its following vehicles, particularly when the leader vehicle of a platoon with short headway applies harsh braking to mitigate an impending collision risk. This study proposes a risk predictive CAS (RPCAS) which executes predictive deceleration with mild braking in advance to prevent a potential rear-end collision by predicting the collision risk arising from a downstream site. To evaluate the performance of the RPCAS, the proposed system is compared with several existing CASs in various car-following cases based on a microscopic traffic simulation. The simulation results show that the RPCAS can effectively reduce the rear-end collision risk with less harsh braking compared with the existing CASs. Furthermore, the RPCAS enables vehicles arriving from upstream to anticipate a potential crash, which provides them with sufficient time to reduce their current speeds proactively. The research findings suggest that the proposed system can attenuate the negative impacts of the previous CASs in relation to traffic and vehicular safety.