Collision Avoidance System Research Articles

Intelligent Control of a Driverless Energy Vehicle Based on an Environment Sensing Sensor

In order to solve the complex environment in the process of vehicle driving and the complexity of self-vehicle structure, intelligent vehicles are prone to rear end collision, lateral collision, and other safety accidents in the presence of tall trees, mountains, and other road environments, endangering the safety of people on board. According to parameters such as the speed of the vehicle, the movement of the blind spot, and the relationship between the vehicle and the blind spot, the model is based on the safety mode of the preceding vehicle. Based on the static obstacles that may exist in the sensing blind area, a sensor sensing blind area safety distance model is established. Based on the possible dynamic obstacles, the active collision avoidance algorithm based on the sensor perceived blind area is studied and simulated. The experimental results show that the selected sensor sensing blind area active collision avoidance controller can well adapt to a variety of special and emergency working conditions, can accurately complete the accurate control of sensor sensing blind area active collision avoidance, and avoid collision accidents to the greatest extent. Compared with the control group, the system designed in this paper can avoid more than 80% of the collision scenes compared with the previous anticollision system. It provides a reference for the future research of sensor sensing blind area-related topics and sensor sensing blind area active collision avoidance system. To a certain extent, it can improve the ability of intelligent vehicle environmental perception and reduce the incidence of rear end collision accidents.

Stochastic model predictive control-based countermeasure methodology for satellites against indirect kinetic cyber-attacks

The objective of this paper is to provide a stochastic framework to optimally avoid collision between a maneuverable spacecraft and a space object or debris. The satellite collision can be caused through a cyber-attack on a satellite by colliding it with a considered strategic satellite. Consequently, it is highly imperative that critical operational space assets be provided with autonomous collision avoidance systems. The collision avoidance methodology proposed in this paper will reduce the collision probability to an acceptable level and protect the satellite against indirect kinetic cyber-attacks initiated by designing optimal collision avoidance maneuvers using a stochastic model predictive control strategy. The collision probability is estimated using the available historical Two-Line Elements of determined objects, and the model predictive control scheme guarantees the safety of the space close approaches. The proposed and developed collision-avoidance countermeasure methodology is numerically simulated for the collision case study between the Iridium-33 and the Cosmos-2251 satellites. The results demonstrate and illustrate the effectiveness, capabilities, and advantages of our proposed methodology in avoiding probable collisions due to indirect kinetic cyber-attacks.

Segmented trajectory planning strategy for active collision avoidance system

This paper presents a segmented trajectory planning strategy for active collision avoidance system. Considering the longitudinal and lateral movement of the obstacle vehicle, as well as the ego vehicle and obstacle outer contour limitations, the collision avoidance trajectory is divided into three segments: lane changing, overtaking and back to original lane. The starting point and end point of lane-change are decided based on longitudinal and lateral safety distance model according to the relative speed and distance as well as the outer contour of the two vehicles. Based on system objective function and lane-change trajectory cluster, vehicle states, dynamic constraints and vehicle body kinematics constraints, the optimal trajectory can be selected, which can monitor the relative location of the obstacle vehicle constantly and then ensure the vehicle can accomplish the collision avoidance safely and smoothly. Simulation and experiment results demonstrate the effectiveness and feasibility of proposed trajectory planning strategy for the active collision avoidance.

Technology Focus: Intelligent Operations (May 2022)

The trend of increasing automation and integration of digital work flows continues in all disciplines within the industry. An obvious driver is the increasing digital nature of our world; today’s phones and tablets connected to the cloud perhaps have more computing capabilities than supercomputers from 5 or 10 years ago. Another key driver, arguably the reason why intelligent operations came on the radar of most companies, is being able to improve safety and efficiency in the complex operations that are routinely performed in the oil patch. The last couple of years, I have highlighted efforts around remote operations of fields, smart wells, and the acquisition and integration of real-time data. This year, I have focused on recent drilling-related papers that highlight how safety and performance can be improved with more intelligent operations. Paper SPE 208711 discusses a key requirement I see for successful intelligent operations, especially when the operations span disciplines and companies. Having a standard, consistent, and unambiguous lexicon is critical for automation and digital work flows. This paper highlights an effort to spur the standardization of codes across companies to describe drilling, completions, and other well activities. Anybody who has gone through old well files will recognize immediately the value and the need for such standardization of well activity codes. The other two papers (SPE 208764 and SPE 208784) highlight how automation can lead to increased safety on rigs during operations. Companies are using a combination of cameras, wearables, and other technology to monitor personnel in safety zones on rigs to ensure no one is unknowingly in the wrong spot at the wrong time. A familiar analog to this would be the safety improvements in driving because of collision-avoidance systems in cars (where a combination of technologies including cameras, radar, and lidar are used). Standardization of Well Activity Codes. - Paper SPE 208711 describes the efforts of one operator to standardize well activity reporting codes. A key highlighted improvement is a new coding system that captures both what was done (the typical focus of historical well activity codes) and the broader context on why the activity was performed. The new codes have enabled more granular tracking of performance metrics and a simplification of reporting. Significantly, the operator has recognized the need for a standardized set of codes across the industry and has donated the codes they have developed to an industrywide open-source environment. I hope this spurs wider adoption and refinement of reporting standards across the industry. Using Computer Vision To Monitor Safety Zones and Automate Drillpipe Tallies. - Paper SPE 208764 highlights how digitalization can result in safer operations and improved accuracy through the automation of manual tasks. The paper discusses a computer vision system coupled with a neural network model that detects if any personnel are within a restricted pipe-delivery red zone at an unsafe time and sets off an audible warning alarm. The paper also describes how the use of multiple cameras on different levels of the mast automates the measurement and tallying of the drillpipe being run into the hole. Both these aspects have enhanced existing processes (such as the use of buffer zones and barricades and the manual counting of drill pipe) to further improve safety and accuracy. This article in JPT might also be of interest. Automatic Kick Detection and Dynamic Well Monitoring. - Paper SPE 208784 discusses how to reduce the time gap between when drilling operators detect a kick and when they activate the blowout preventers (BOPs). The paper identifies accurate positioning of the drillstring before activating BOPs as a critical step that affects the time gap. The paper describes an architecture that allows automation of the space-out operation and achieves quick and accurate positioning of the tool joint with minimum movement of the drillstring. A critical enabler that the authors describe is being aware in real time of the depth of each tool joint in the wellbore.

Review Paper on Design and Fabrication Emergency Braking System in Four-Wheeler

Abstract: Most people prefer to use cars and four-wheelers for efficient transportation. Vehicle technology is on the rise, especially in braking and detection systems. Vehicles equipped with modern braking technology are designed with a simple collision avoidance system, which will help to detect a possible collision and to apply the emergency brake to avoid it. Such technologies will reduce the number of accidents that cause the most serious damage, serious injury and even death. In this automatic braking system, there is a four-wheeled trolley in which sparse wheels are motorized for the drive wheel. The front wheels have been pivoted for the fasteners. The new friction brakes are designed for emergency braking. The brakes are operated by a motorized mechanism, which is loaded with a spring. Driving is controlled remotely. Two sensors are used for the front and rare to avoid damaging the car when parking. The sensors used are of the capacitive type which can detect both metallic and non-metallic obstacles. Two sensor relays are also used to activate the brake motor when obstacles are detected either in the forward or reverse direction.

Automatic traffic scenarios generation for autonomous ships collision avoidance system testing

The Collision Avoidance (CA) system constitutes a key enabling technology for the Maritime Autonomous Surface Ships (MASS), the appropriate functionality of which is critical for assuring the navigation safety. Although several techniques including testing of the collision scenarios in a virtual environment can be employed, the trust of testing phase results depends on the number of tested scenarios and their coverage. This study aims at proposing a systematic and automatic process for the generation of the traffic scenarios that can be employed for the CA system testing. First, the range of the investigated parameters is defined, and samples of potential traffic parameters are generated using Sobol sequences. Subsequently, hazardous traffic scenarios are identified from the initially generated scenarios by using predefined rules. For these hazardous scenarios, a risk vector considering weather conditions and traffic conditions is calculated. A clustering algorithm is employed to identify the groups of traffic conditions that can be encountered based on each scenario risk vector and COLREGs traffic scenarios. For each of these groups, the riskiest scenario is provided as input for the test cases development, thus, simplifying the selection process of testing scenarios. The process is applied to a theoretical Short Sea Shipping autonomous vessel, whereas the derived results are employed to discuss the advantages and disadvantages of the developed process.

Transformer point net: cost-efficient classification of on-road objects captured by light ranging sensors on low-resolution conditions

The three-dimensional perception applications have been growing since Light Detection and Ranging devices have become more affordable. On those applications, the navigation and collision avoidance systems stand out for their importance in autonomous vehicles, which are drawing an appreciable amount of attention these days. The on-road object classification task on three-dimensional information is a solid base for an autonomous vehicle perception system, where the analysis of the captured information has some factors that make this task challenging. On these applications, objects are represented only on one side, its shapes are highly variable and occlusions are commonly presented. But the highest challenge comes with the low resolution, which leads to a significant performance dropping on classification methods. While most of the classification architectures tend to get bigger to obtain deeper features, we explore the opposite side contributing to the implementation of low-cost mobile platforms that could use low-resolution detection and ranging devices. In this paper, we propose an approach for on-road objects classification on extremely low-resolution conditions. It uses directly three-dimensional point clouds as sequences on a transformer-convolutional architecture that could be useful on embedded devices. Our proposal shows an accuracy that reaches the 89.74 % tested on objects represented with only 16 points extracted from the Waymo, Lyft's level 5 and Kitti datasets. It reaches a real time implementation (22 Hz) in a single core processor of 2.3 Ghz.

Product Tests in Virtual Reality: Lessons Learned during Collision Avoidance Development for Drones

Virtual reality (VR) and real-world simulations have become an important tool for product development, product design, and product tests. Product tests in VR have many advantages, such as reproducibility and shortened development time. In this paper, we investigate the virtual testing of a collision avoidance system for drones in terms of economic benefits. Our results show that virtual tests had both positive and negative effects on the development, with the positive aspects clearly predominating. In summary, the tests in VR shorten the development time and reduce risks and therefore costs. Furthermore, they offer possibilities not available in real-world tests. Nevertheless, real-world tests are still important.

ACAS installation on unmanned aerial vehicles: effectiveness and safety issues

PurposeThe paper aims to investigate the compatibility of manned-aircraft airborne collision avoidance systems (ACAS) for use on unmanned aerial vehicles (UAV).Design/methodology/approachThe paper uses the Fault Tree method for defining ACAS model adapted for the UAV operations, with the aim of showing the presence of certain factors that configure in such ACAS system, and whose failure can lead to an adverse event – mid-air collision.FindingsBased on the effectiveness analysis of ACAS solution adapted for the UAV operations, for given inputs, it can be concluded that the probability of ACAS failure is on the order of 10–4, as well as that in the case of autonomous ACAS solution for the UAV, the probability is reduced to 10–5. The most influential factors for the failure of the UAV’s ACAS are as follows: technical implications on the UAV, human factor, sensor error, communication and C2 link issue.Practical implicationsThe established model could be used both in the UAV’s ACAS design and application phases, with the aim of assessing the effectiveness of the adopted solution. The model outputs not only highlight the critical points of the system but also provide the basis for defining the Target Level of Safety (TLOS) for the UAV operations.Originality/valueThe developed model can be expected to speed up the design and adoption process of ACAS solutions for the UAVs. Also, the paper presents one of the first attempts to quantify TLOS for the UAV operations in the context of collision avoidance systems.

Development of a novel autonomous space debris collision avoidance system for uncrewed spacecraft

The objective of the study is to model a system which can effectively prevent the collision between space debris and spacecraft by robust design and algorithms of the system. The location and trajectory of the debris field at each and every instant are tracked with the help of image processing. The Voronoi diagram and Dijkstra’s algorithm are utilized to determine the alternate orbits for the spacecraft in order to avert the collision. The discrete subsystem, namely, orbit determination system, orbit maneuvering system, and reaction control system, is modeled and simulated using the Simulink platform. The collision avoidance system employs the most energy-efficient Hohmann maneuver to find the alternate orbits for spacecraft between circular orbits. The simulation results shown from the present work clearly evidences the effectiveness of collision avoidance. The present collision avoidance system always determines the safe and fuel economic trajectory for spacecraft independent of human interference.

Willingness to Pay for Advanced Safety Features in Vehicles: An Adaptive Choice-Based Conjoint Analysis Approach

Advanced safety features in vehicles are emerging features that car manufacturers design and produce to improve the safety of driving a vehicle. In recent years, car manufacturers increasingly have provided different safety features as options to their customers. Understanding consumers’ interests and priorities relating to these features in vehicles has motivated researchers to conduct market analysis studies. This study aims to analyze the willingness to pay (WTP) for the advanced safety features in vehicles by adults resident in the U.S.A. Implementing adaptive choice-based conjoint analysis (ACBC), which is a special market analysis technique, and using comprehensible and realistic attributes in the survey are the main innovations of this study. The results of this study show that surround monitoring and collision avoidance systems are the most important attributes for respondents in their next vehicle. After that, the vehicle’s connectivity system was the third most important factor. Respondents distinctly preferred a vehicle with fully automated crash avoidance features. Moreover, the vehicle with safety features having the highest utilities may capture almost 61% of the market.

Understanding realistic attacks on airborne collision avoidance systems

Airborne collision avoidance systems provide an onboard safety net should normal air traffic control procedures fail to keep aircraft separated. These systems are widely deployed and have been constantly refined over the past three decades, usually in response to near misses or mid-air collisions. Recent years have seen security research increasingly focus on aviation, identifying that key wireless links—some of which are used in collision avoidance—are vulnerable to attack. In this paper, we go one step further to understand whether an attacker can remotely trigger false collision avoidance alarms. Primarily considering the next-generation Airborne Collision Avoidance System X (ACAS X), we adopt a smodelling approach to extract attacker constraints from technical standards before simulating collision avoidance attacks against standardized ACAS X code. We find that in 44% of cases, an attacker can successfully trigger a collision avoidance alert which on average results in a 590 ft altitude deviation; when the aircraft is at lower altitudes, this success rate rises considerably to 79%. Furthermore, we show how our simulation approach can be used to help defend against attacks by identifying where attackers are most likely to be successful.

A Looming Spatial Localization Neural Network Inspired by MLG1 Neurons in the Crab Neohelice.

Similar to most visual animals, the crab Neohelice granulata relies predominantly on visual information to escape from predators, to track prey and for selecting mates. It, therefore, needs specialized neurons to process visual information and determine the spatial location of looming objects. In the crab Neohelice granulata, the Monostratified Lobula Giant type1 (MLG1) neurons have been found to manifest looming sensitivity with finely tuned capabilities of encoding spatial location information. MLG1s neuronal ensemble can not only perceive the location of a looming stimulus, but are also thought to be able to influence the direction of movement continuously, for example, escaping from a threatening, looming target in relation to its position. Such specific characteristics make the MLG1s unique compared to normal looming detection neurons in invertebrates which can not localize spatial looming. Modeling the MLG1s ensemble is not only critical for elucidating the mechanisms underlying the functionality of such neural circuits, but also important for developing new autonomous, efficient, directionally reactive collision avoidance systems for robots and vehicles. However, little computational modeling has been done for implementing looming spatial localization analogous to the specific functionality of MLG1s ensemble. To bridge this gap, we propose a model of MLG1s and their pre-synaptic visual neural network to detect the spatial location of looming objects. The model consists of 16 homogeneous sectors arranged in a circular field inspired by the natural arrangement of 16 MLG1s' receptive fields to encode and convey spatial information concerning looming objects with dynamic expanding edges in different locations of the visual field. Responses of the proposed model to systematic real-world visual stimuli match many of the biological characteristics of MLG1 neurons. The systematic experiments demonstrate that our proposed MLG1s model works effectively and robustly to perceive and localize looming information, which could be a promising candidate for intelligent machines interacting within dynamic environments free of collision. This study also sheds light upon a new type of neuromorphic visual sensor strategy that can extract looming objects with locational information in a quick and reliable manner.

Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles

According to World Health Organization (WHO), the leading cause of fatalities and injuries is rear-ending collision in vehicles. The critical challenge of the technologically rich transportation system is to reduce the chances of accidents between vehicles. For this purpose, it is especially important to analyze the factors that are the cause of accidents. Based on these factors’ results, this paper presents a driver assistance system for collision avoidance. There are many factors involved in collisions in the existing literature from which we identified some factors which can affect the accident occurrence probability. However, with advancements in the technologies of autonomous vehicles, these factors can be controlled using an onboard driver assistance system. We used MATLAB’s Fuzzy Inference System Tool to analyze the categories of accident contributing factors. Fuzzy results are validated using the VOMAS agent in the NetLogo simulation model. The proposed system can inform the vehicle’s automated system when chances of an accident are higher so that the vehicle may take control from the driver. The proposed research is extremely helpful in handling various kinds of factors involved in accidents. The results of the experiments demonstrated that multi-factor-enabled vehicles could better avoid collision as compared to other vehicles.

Automatic simulation-based testing of autonomous ships using Gaussian processes and temporal logic

A methodology for automatic simulation-based testing of control systems for autonomous vessels is proposed. The work is motivated by the need for increased test coverage and formalism in the verification efforts. It aims to achieve this by formulating requirements in the formal logic Signal Temporal Logic (STL). This enables automatic evaluation of simulations against requirements using the STL robustness metric, resulting in a robustness score for requirements satisfaction. Furthermore, the proposed method uses a Gaussian Process (GP) model for estimating robustness scores including levels of uncertainty for untested cases. The GP model is updated by running simulations and observing the resulting robustness, and its estimates are used to automatically guide the test case selection toward cases with low robustness or high uncertainty. The main scientific contribution is the development of an automatic testing method which incrementally runs new simulations until the entire parameter space of the case is covered to the desired confidence level, or until a case which falsifies the requirement is identified. The methodology is demonstrated through a case study, where the test object is a Collision Avoidance (CA) system for a small high-speed vessel. STL requirements for safety distance, mission compliance, and COLREG compliance are developed. The proposed method shows promise, by both achieving verification in feasible time and identifying falsifying behaviors which would be difficult to detect manually or using brute-force methods. An additional contribution of this work is a formalization of COLREG using temporal logic, which appears to be an interesting direction for future work.

Identification of Failure Modes in the Collision Avoidance System of an Autonomous Ferry using Adaptive Stress Testing

As complex autonomous systems emerge in the maritime sector, measures must be taken in order to ensure thorough safety assessment. Real-world testing can be costly and potentially dangerous, and therefore there is a need for suitable simulation-based methods. This paper presents an implementation of the Adaptive Stress Testing (AST) method applied to the collision avoidance (COLAV) system of a small passenger ferry. AST is a simulation-based technique which has shown promising results in safety assessment of aviation and automobile systems. Given a simulator of a system, AST uses reinforcement learning to optimize toward system failure, and returns the most likely failure scenarios. AST is here shown to successfully identify scenarios where the criteria for failure are met, which is when the ferry collides with an adversary vessel controlled by AST. However, most of the initial results exhibit failures where the COLAV system of the ferry is not responsible for the failure, making the results less valuable to system developers. To improve the relevance, augmentations are made to the optimization problem. The augmentations result in four distinct problem formulations presented in the paper. Finally, the results are clustered using an unsupervised machine learning method called Soft Dynamic Time Warping k-means clustering in order to present a general summary of the identified failure scenarios. Our results demonstrate the relevance and potential of AST for the maritime sector and illustrate how common drawbacks of AST can be circumvented by method adjustment.

A 5-GHz 0.15-mm² Collision-Avoiding RFID Employing Complementary Pass-Transistor Adiabatic Logic With an Inductively Connected External Antenna in 0.18-μm CMOS

A low-power, small form factor, and collision avoiding RFID tag is proposed. By applying complementary passtransistor adiabatic logic (CPAL) and two-phase clock, not only the required power of operation is lowered, but also large capacitors necessary for stable operation are eliminated, which leads to a reduced chip size. Additionally, the external antenna used is inductively connected instead of solder-bonded, which contributes to a lower assembly cost. A collision avoidance system exploiting counter and bit shift is also proposed. Results show the proposed RFID is capable of communication twice as far as the one using conventional adiabatic logic with one-eighth of the reader’s transmission power, as well as having the whole chip downsized by 94%. It is also confirmed that collisions between three tags are avoided thanks to the collision avoidance system.

Comparison of Routing Protocols and Communication Interfaces for the Implementation of Collision Avoidance Capabilities in Fleets of Industrial Mobile Robots

In this paper a novel approach to a collision avoidance system between forklifts based on a wireless communication is proposed in order to test its performance and feasibility. First of all an overview on collision avoidance systems between vehicles inside warehouses is introduced with their main advantages and disadvantages. Then the reasons that led to the study of the new solution proposed in this article are motivated. Subsequently, a brief survey on mobile ad hoc networks and their main routing protocols that allow multi-hop communication is presented. For the implementation of the simulations OMNeT++ and SUMO software were used, which allowed to develop the model and the warehouse scenarios. Furthermore, we report the collision avoidance algorithm used in this study for the generation of a plausible network data load in order to collect and analyze simulation statistics. In particular, two applications have been developed, DiscoveryApp and AnticollisionApp, which are run by each node. Several simulations were then carried out, varying the size of the scenario, the communication interface standards and the routing protocols. In particular, communication interfaces based on the IEEE 802.15.4 and 802.11g standards and AODV, DSDV, GPSR routing protocols were tested. The last part of the paper concerns the analysis of the results based on the metrics of average end to end delay, overhead, average goodput and packet delivery ratio in order to determine the best combination of technologies for an application of this type.

Modeling of a Negative Refractive Index Metamaterial Unit-Cell and Array for Aircraft Surveillance Applications

In this paper, a unique negative refractive index-metamaterial (NRI-MTM) structure is proposed for traffic alert and collision avoidance system (TCAS) for aircraft application. The NRI-MTM unit-cell structure is a combination of square and circular split ring structures. The proposed square and circular split ring metamaterial (SCM) is designed on the top of a 1.6 mm thick FR-4 substrate, with a single circular split ring on the bottom of the substrate. The total dimension of the SCM unit-cell is 23 × 23mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.08λ × 0.08 λ), resonating at 1.06 GHz. This unit-cell is further used to create a 5×4 array structure with a dimension of 130 × 111mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (0.46 λ × 0.4 λ). A High-Frequency Structure Simulator (HFSS) is used to assess the electromagnetic properties of the proposed structures. Furthermore, in order to analyse the double negative (DNG) behaviour of the designed SCM structure, the unit-cell is rotated from 0 to 360 degrees with a step of 60 degrees along the azimuthal plane. On doing so, it is observed that, the proposed SCM structures, including unit-cell and array structures, exhibit DNG and NRI properties over the frequency band of 0.5 to 1.2 GHz, thus making it optimum for aircraft surveillance application. The SCM unit-cell and 5 × 4 SCM array structure were fabricated, and the measured results are in well agreement with the simulated results. Further, the array structure is used as a superstrate on the patch antenna to analyze the impact of the MTM on the antenna performance. In comparison to the antenna without the MTM structure, the 5 × 4 SCM array loaded antenna has a gain improvement of 2.8dB and a bandwidth enhancement of 31.5MHz.

Toward Optimal MEC-Based Collision Avoidance System for Cooperative Inland Vessels: A Federated Deep Learning Approach

Cooperative collision avoidance between inland waterway ships is among the envisioned services on the Internet of Ships. Such a service aims to support safe navigation while optimizing ships' trajectories. However, to deploy it, timely and accurate prediction of ships' positioning with real-time reactions is needed to anticipate collisions. In such a context, ships positions are usually predicted using advanced Machine Learning (ML) techniques. Traditionally, ML schemes require that the data be processed in a centralized way, e.g., a cloud data center managed by a third party. However, these schemes are not suitable for the collisions avoidance service due to the inaccessibility of ships' positioning data by this third party, and allowing connected ships to get access to sensitive information. Therefore, in this paper, we design a new cooperative collision avoidance system for inland ships, while ensuring data security and privacy. Our system is based on deep federated learning to collaboratively build a model of ship positioning prediction, while avoiding sharing their private data. In addition, it is deployed at multi-access edge computing (MEC) level to provide low-latency communication to ensure fast responses during collision detection. Furthermore, it relies on Blockchain and smart contracts to ensure trust and valid communications between ships and MEC nodes. We evaluate the proposed system using a generated dataset representing ships mobility in France. The results, which demonstrate the accuracy of our prediction model, prove the effectiveness of our cooperative collision avoidance system in ensuring timely and reliable communications and avoiding collisions between ships.