Showing papers on "Collision avoidance published in 2011"

Reciprocal n-Body Collision Avoidance

Abstract: In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace In our formulation, each robot acts fully independently, and does not communicate with other robots Based on the definition of velocity obstacles [5], we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear program We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute collision-free actions for all of them in only a few milliseconds To the best of our knowledge, this method is the first that can guarantee local collision-free motion for a large number of robots in a cluttered workspace

Reciprocal collision avoidance with acceleration-velocity obstacles

Abstract: We present an approach for collision avoidance for mobile robots that takes into account acceleration constraints. We discuss both the case of navigating a single robot among moving obstacles, and the case of multiple robots reciprocally avoiding collisions with each other while navigating a common workspace. Inspired by the concept of velocity obstacles [3], we introduce the acceleration-velocity obstacle (AVO) to let a robot avoid collisions with moving obstacles while obeying acceleration constraints. AVO characterizes the set of new velocities the robot can safely reach and adopt using proportional control of the acceleration. We extend this concept to reciprocal collision avoidance for multi-robot settings, by letting each robot take half of the responsibility of avoiding pairwise collisions. Our formulation guarantees collision-free navigation even as the robots act independently and simultaneously, without coordination. Our approach is designed for holonomic robots, but can also be applied to kinematically constrained non-holonomic robots such as cars. We have implemented our approach, and we show simulation results in challenging environments with large numbers of robots and obstacles.

Highway Capacity Benefits from Using Vehicle-to-Vehicle Communication and Sensors for Collision Avoidance

Abstract: Several automobile manufacturers are offering assisted driving systems that use sensors to automatically brake automobiles to avoid collisions. Before extensively deploying these systems, we should determine how they will affect highway capacity. The goal of this paper is to compare the highway capacity when using sensors alone and when using sensors and vehicle-to-vehicle communication. To achieve this goal, the rules for using both technologies to prevent collisions are proposed, and highway capacity is estimated based on these rules. We show that both technologies can increase highway capacity. The increase in capacity is a function of the fraction of the vehicles that use a technology. If all of the vehicles use sensors alone, the increase in highway capacity is about 43%. While if all of the vehicles use both sensors and vehicle-to-vehicle communication, the increase is about 273%.

Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller

Abstract: Collision avoidance is one of the most difficult and challenging automatic driving operations in the domain of intelligent vehicles. In emergency situations, human drivers are more likely to brake than to steer, although the optimal maneuver would, more frequently, be steering alone. This statement suggests the use of automatic steering as a promising solution to avoid accidents in the future. The objective of this paper is to provide a collision avoidance system (CAS) for autonomous vehicles, focusing on pedestrian collision avoidance. The detection component involves a stereo-vision-based pedestrian detection system that provides suitable measurements of the time to collision. The collision avoidance maneuver is performed using fuzzy controllers for the actuators that mimic human behavior and reactions, along with a high-precision Global Positioning System (GPS), which provides the information needed for the autonomous navigation. The proposed system is evaluated in two steps. First, drivers' behavior and sensor accuracy are studied in experiments carried out by manual driving. This study will be used to define the parameters of the second step, in which automatic pedestrian collision avoidance is carried out at speeds of up to 30 km/h. The performed field tests provided encouraging results and proved the viability of the proposed approach.

Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions

Abstract: This paper focuses on a fuzzy logic based intelligent decision making system that aims to improve the safety of marine vessels by avoiding collision situations. It can be implemented in a decision support system of an oceangoing vessel or included in the process of autonomous ocean navigation. Although Autonomous Guidance and Navigation (AGN) is meant to be an important part of future ocean navigation due to the associated cost reduction and improved maritime safety, intelligent decision making capabilities should be an integrated part of the future AGN system in order to improve autonomous ocean navigational facilities. In this study, the collision avoidance of the Target vessel with respect to the vessel domain of the Own vessel has been analyzed and input, and output fuzzy membership functions have been derived. The if–then rule based decision making process and the integrated novel fuzzy inference system are formulated and implemented on the MATLAB software platform. Simulation results are presented regarding several critical collision conditions where the Target vessel fails to take appropriate actions, as the “Give way” vessel to avoid collision situations. In these situations, the Own vessel is able to take critical actions to avoid collisions, even when being the “Stand on” vessel. Furthermore, all decision rules are formulated in accordance with the International Maritime Organization Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), 1972, to avoid conflicts that might occur during ocean navigation.

Collision Detection as a Model for Sensory-Motor Integration

Abstract: Visually guided collision avoidance is critical for the survival of many animals. The execution of successful collision-avoidance behaviors requires accurate processing of approaching threats by the visual system and signaling of threat characteristics to motor circuits to execute appropriate motor programs in a timely manner. Consequently, visually guided collision avoidance offers an excellent model with which to study the neural mechanisms of sensory-motor integration in the context of a natural behavior. Neurons that selectively respond to approaching threats and brain areas processing them have been characterized across many species. In locusts in particular, the underlying sensory and motor processes have been analyzed in great detail: These animals possess an identified neuron, called the LGMD, that responds selectively to approaching threats and conveys that information through a second identified neuron, the DCMD, to motor centers, generating escape jumps. A combination of behavioral and in vivo ...

Interrelated WiFi and USB Protocols and Other Application Framework Processes, Circuits and Systems

Abstract: A master electronic circuit (300) includes a storage (324) representing a wireless collision avoidance networking process (332) involving collision avoidance overhead and combined with a schedulable process (345) including a serial data transfer process and a scheduler, a wireless modem (350) operable to transmit and receive wireless signals for the networking process (332), and a processor (320) coupled with the storage (324) and with the wireless modem (350) and operable to execute the scheduler to establish and transmit a schedule (110) for plural serial data transfers involving the processor (320) and distinct station identifications, and to execute the serial data transfers inside the wireless networking process and according to the schedule so as to avoid at least some of the collision avoidance overhead. Other electronic circuits, processes of making and using, and systems are disclosed.

Correct, Reactive, High-Level Robot Control

Abstract: In this article, we presented an overview of how temporal logic synthesis, coupled with abstractions and continuous bisimilar controllers, can be used to generate high-level, reactive robot control. We illustrated the ideas using the DUC mission, and we presented two approaches to deal with the inherent state explosion problem.

Probabilistic Collision Checking With Chance Constraints

Abstract: Obstacle avoidance, and by extension collision checking, is a basic requirement for robot autonomy. Most classical approaches to collision-checking ignore the uncertainties associated with the robot and obstacle's geometry and position. It is natural to use a probabilistic description of the uncertainties. However, constraint satisfaction cannot be guaranteed, in this case, and collision constraints must instead be converted to chance constraints. Standard results for linear probabilistic constraint evaluation have been applied to probabilistic collision evaluation; however, this approach ignores the uncertainty associated with the sensed obstacle. An alternative formulation of probabilistic collision checking that accounts for robot and obstacle uncertainty is presented which allows for dependent object distributions (e.g., interactive robot-obstacle models). In order to efficiently enforce the resulting collision chance constraints, an approximation is proposed and the validity of this approximation is evaluated. The results presented here have been applied to robot-motion planning in dynamic, uncertain environments.

Robust Airborne Collision Avoidance through Dynamic Programming

Abstract: The Traffic Alert and Collision Avoidance System (TCAS) uses an on-board beacon to monitor the local air traffic and logic to determine when to alert pilots to potential conflict. The current TCAS logic was the result of many years of development and involved the careful engineering of many heuristic rules specified in pseudocode. Unfortunately, due to the complexity of the logic, it is difficult to revise the pseudocode to accommodate the evolution of the airspace and the introduction of new technologies and procedures. This This report summarizes recent advances in computational techniques for automatically deriving the optimal logic with respect to a probabilistic model and a set of performance metrics. Simulations demonstrate how this new approach results in logic that significantly outperforms TCAS according to the standard safety and operational performance metrics.

Unmanned Aircraft Collision Avoidance using Continuous-State POMDPs.

Abstract: An effective collision avoidance system for unmanned aircraft will enable them to fly in civil airspace and greatly expand their applications. One promising approach is to model aircraft collision avoidance as a partially observable Markov decision process (POMDP) and automatically generate the threat resolution logic for the collision avoidance system by solving the POMDP model. However, existing discrete-state POMDP algorithms cannot cope with the high-dimensional state space in collision avoidance POMDPs. Using a recently developed algorithm called Monte Carlo Value Iteration (MCVI), we constructed several continuous-state POMDP models and solved them directly, without discretizing the state space. Simulation results show that our 3-D continuous-state models reduce the collision risk by up to 70 times, compared with earlier 2-D discrete-state POMDP models. The success demonstrates both the benefits of continuous-state POMDP models for collision avoidance systems and the latest algorithmic progress in solving these complex models.

Vehicular Networks for Collision Avoidance at Intersections

Abstract: A substantial fraction of automotive collisions occur at intersections. Statistics collected by the Federal Highway Administration (FHWA) show that more than 2.8 million intersection-related crashes occur in the United States each year, with such crashes constituting more than 44 percent of all reported crashes [12]. In addition, there is a desire to increase throughput at intersections by reducing the delay introduced by stop signs and traffic signals. In the future, when dealing with autonomous vehicles, some form of co-operative driving is also necessary at intersections to address safety and throughput concerns. In this paper, we investigate the use of vehicle-to-vehicle (V2V) communications to enable the navigation of traffic intersections, to mitigate collision risks, and to increase intersection throughput significantly. Specifically, we design a vehicular network protocol that integrates with mobile wireless radio communication standards such as Dedicated Short Range Communications (DSRC) and Wireless Access in a Vehicular Environment (WAVE). This protocol relies primarily on using V2V communications, GPS and other automotive sensors to safely navigate intersections and also to enable autonomous vehicle control. Vehicles use DSRC/WAVE wireless media to periodically broadcast their position information along with the driving intentions as they approach intersections. We used the hybrid simulator called GrooveNet [1, 2] in order to study different driving scenarios at intersections using simulated vehicles interacting with each other. Our simulation results indicate that very reasonable improvements in safe throughput are possible across many practical traffic scenarios.

A comparison of motion planning algorithms for cooperative collision avoidance of multiple cognitive automobiles

Abstract: Automated cooperative collision avoidance of multiple vehicles is a promising approach to increase road safety in the future. This approach requires a real-time motion planner which computes cooperative maneuvers of multiple cognitive vehicles. As motion planning is a task of high computational complexity, computing times of the planner have to be traded off against solution quality. This contribution compares several cooperative motion planning algorithms with respect to these criteria. The considered algorithms are a tree search algorithm relying on precomputed lower bounds, the elastic band method, mixed-integer linear programming, and a priority-based approach. Success rates and computing times on various simulated scenarios are reported.

An Efficient Computational Architecture for a Collision Early-Warning System for Vehicles, Pedestrians, and Bicyclists

Abstract: We describe a computational architecture of a collision early-warning system for vehicles and other principals. Early-warnings allow drivers to make good judgments and to avoid emergency stopping or dangerous maneuvering. With many principals (vehicles, pedestrians, bicyclists, etc.) coexisting in a dense intersection, it is difficult to predict, even a few seconds in advance, since there are many possible scenarios. It is a major challenge to manage computational resources and human attention resources so that only the more plausible collisions are tracked, and of those, only the most critical collisions prompt warnings to drivers. In this paper, we propose a two-stage collision risk assessment process, including the following: 1) a preliminary assessment via simple efficient geometric computations, which thoroughly considers surrounding principals and identifies likely potential accidents, and 2) a specialized assessment that computes more accurate collision probabilities via sophisticated statistical inference. The whole process delivers an expected utility assessment to available user interfaces (UIs), allowing the UIs to make discriminating choices of when to warn drivers or other principals.

Comparative evaluation of range sensor accuracy in indoor environments

Abstract: 3D range sensing is one of the important topics in robotics, as it is often a component in vital autonomous subsystems like collision avoidance, mapping and semantic perception. The development of ...

Reactive Collision Avoidance Using Nonlinear Geometric and Differential Geometric Guidance

Abstract: U NMANNED Aerial Vehicles (UAVs) hold good promise for autonomously carrying out complex civilian and military operations. However, many of these missions require them to fly at low altitudes, making them vulnerable to collision with both stationary as well as moving obstacles. Hence, it is vital that UAVs are equipped with autonomous capability to sense and avoid collisions, especially for the pop-up threats. When such a threat is sensed and a collision is predictedwithin a short time ahead, theUAV should be able to react and maneuver away quickly so that the collision is avoided. An algorithmwhich can assure such amaneuver is called a “reactive collision avoidance algorithm.” Since the available reaction time in such a scenario is usually is small and UAVs are usually limited by computational resources, such an algorithm should also be computationally very efficient (it should preferably be noniterative). It is also required that whilemaneuvering away, it should notmaneuver toomuch away from the obstacle either. This is both to avoid collision from other nearby obstacles and not to compromise on the overall mission objective. There are various attempts in the literature to develop algorithms for collision avoidance purpose, many of which are inspired from global path planning algorithms. The artificial potential field method is such an approach where the motion of the vehicle is guided under the influence of a potential field. The potential field (which is essentially a cost function) is designed in such a way that obstacles have repulsive fields while the destination has an attractive field. The safe path of the UAV is then found by optimizing the carefully selected cost function. To tune this basic philosophy for reactive collision avoidance, a model predictive control-based algorithm has been proposed in the literature. This algorithm essentially assures path following under safe conditions (i.e. if no collision is predicted in the near future) and invokes the potential field function when new collisions are sensed. However, in potential field based techniques the associated optimization process is typically done in an iterative manner. Because of this they are usually computationally intensive and hence are not suitable for reactive collision avoidance of airborne UAVs in general. A promising algorithm in collision avoidance and global path planning is the philosophy of rapidly-exploring random tree (RRT) [1], which has also been used for reactive collision avoidance. However, there are many concerns about the RRT approach, which can largely be attributed to the random nature of the algorithm. For example, the path predicted by RRT is usually a sting of connected straight lines that does not reflect the path followed by a vehicle with nonholonomic constraints. More important, it is a probabilistic approach and hence there is no guarantee of finding a feasible path within a limited finite time. Other graph search algorithms such as best-first search are also implemented for reactive collision avoidance [2]. However, this is not systematic approach and could result in the algorithm searching far too many nodes under some conditions. Moreover, precomputing motion primitives and saving them in a lookup table is infeasible for UAVs, which are usually resource-limited. An interesting perspective to collision avoidance problem is the minimum effort guidance [3], where an optimal control-based approach has been proposed after applying the collision cone philosophy to detect collisions. This method is computationally nonintensive as a closed form solution has been proposed. Even though this is an interesting idea, by minimizing the lateral acceleration, perhaps it imposes unwanted extra constraint on the problem formulation as reactive collision avoidance problems do not necessarily have to be carried out with minimum lateral acceleration. More important, one can observe that this formulation only assures position guarantee and no constraint is imposed on the velocity vector. Hence, even though it guides the vehicle to a carefully selected target point on the safety boundary (we call it the “aiming point”), it causes the vehicle to maneuver until this point. This can be risky as the vehicle may enter the safety ball before reaching the aiming point. Even though the collision cone based aiming point philosophy is a very good idea, the authors of this Note strongly believe that instead of only position guarantee, rather the velocity vector should be aligned towards the aiming point as soon as a collision is detected (which will automatically lead to position guarantee as well). Towards this objective, two new nonlinear guidance laws are proposed in this Note, which are named as nonlinear geometric guidance (NGG) and differential geometric guidance (DGG). These guidance laws are inspired by the philosophy of “aiming point guidance” (APG) [4], which has been proposed in missile guidance literature. It turns out that the APG is a simplified case of the NGG where the associated since function is replaced by its linear approximation (hence, for a systematic discussion, it is renamed as linear geometric guidance (LGG) in this Note). Both of the guidance algorithms proposed in this Note quickly align the velocity vector of the UAValong the aiming point within a part of the available time-togo, which ensures quick reaction and hence safety of the vehicle. The main feature of this philosophy is that they effect high maneuvering at the beginning, causing the velocity vector of the UAV to align with the aiming point direction quickly and then settling along it. Therefore there is no need to maneuver all the way until the aiming point is reached and hence the chance of the UAV entering into the safety ball is minimized. Using the point of closest approach (PCA) [5], the proposed NGG andDGGalgorithms have also been extended for collision avoidance with moving obstacles in both cooperative as well as ignorant scenarios. Mathematical correlations between the guidance laws have also been established, which show that the NGG and DGG are exactly correlated to each other with appropriate gain selections, while the LGG is an approximation of DGG. A “sphere-tracking algorithm” is also proposed in this Note where the UAV is guided to track the surface of the safety spherewhenever a brief violation of the safety boundary occurs after reaching the aiming point because of the location of the next aiming point (which may include the target in Presented as Paper 2010-8315 at the AIAA Guidance, Navigation and Control, Toronto, 2–5August 2010; received 26May 2010; revision received 4 October 2010; accepted for publication 6 October 2010. Copyright © 2010 by Radhakant Padhi. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0731-5090/11 and $10.00 in correspondence with the CCC. ∗Former Project Assistant, Department of Aerospace Engineering; anushamujumdar87@gmail.com. Associate Professor, Department of Aerospace Engineering; padhi@ aero.iisc.ernet.in. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 34, No. 1, January–February 2011

Evolving Philosophies on Autonomous Obstacle/Collision Avoidance of Unmanned Aerial Vehicles

Abstract: Much of the benefits of deploying unmanned aerial vehicles can be derived from autonomous missions. For such missions, however, sense-and-avoid capability (i.e., the ability to detect potential collisions and avoid them) is a critical requirement. Collision avoidance can be broadly classified into global and local path-planning algorithms, both of which need to be addressed in a successful mission. Whereas global path planning (which is mainly done offline) broadly lays out a path that reaches the goal point, local collision-avoidance algorithms, which are usually fast, reactive, and carried out online, ensure safety of the vehicle from unexpected and unforeseen obstacles/collisions. Even though many techniques for both global and local collision avoidance have been proposed in the recent literature, there is a great interest around the globe to solve this important problem comprehensively and efficiently and such techniques are still evolving. This paper presents a brief overview of a few promising and evolving ideas on collision avoidance for unmanned aerial vehicles, with a preferential bias toward local collision avoidance.

Humanoid navigation with dynamic footstep plans

Abstract: Humanoid robots possess the capability of stepping over or onto objects, which distinguishes them from wheeled robots. When planning paths for humanoids, one therefore should consider an intelligent placement of footsteps instead of choosing detours around obstacles. In this paper, we present an approach to optimal footstep planning for humanoid robots. Since changes in the environment may appear and a humanoid may deviate from its originally planned path due to imprecise motion execution or slippage on the ground, the robot might be forced to dynamically revise its plans. Thus, efficient methods for planning and replanning are needed to quickly adapt the footstep paths to new situations. We formulate the problem of footstep planning so that it can be solved with the incremental heuristic search method D* Lite and present our extensions, including continuous footstep locations and efficient collision checking for footsteps. In experiments in simulation and with a real Nao humanoid, we demonstrate the effectiveness of the footstep plans computed and revised by our method. Additionally, we evaluate different footstep sets and heuristics to identify the ones leading to the best performance in terms of path quality and planning time. Our D* Lite algorithm for footstep planning is available as open source implementation.

System and method for collision avoidance maneuver path determination with jerk limit

Abstract: In a vehicle, an optimal path curvature limited by one or more constraints may be determined. The constraints may be related to lateral jerk and one or more vehicle dynamics constraints. Based on the optimal path curvature, an optimal vehicle path around an object may be determined. The optimal vehicle path may be output to a collision avoidance control system. The collision avoidance control system may cause the vehicle to take a certain path.

Vision based rail track and switch recognition for self-localization of trains in a rail network

Abstract: A collision avoidance system for railroad vehicles needs to determine their location in the railroad network precisely and reliably. For a vehicle-based system, that is independent from the infrastructure, it is vital to determine the direction a railroad vehicle turns at switches. In this paper a vision based approach is presented that allows to achieve this reliably, even under difficult conditions. In the images of a camera that observes the area in front of a railroad vehicle the rail tracks are detected in real-time. From the perspective of the moving railroad vehicle rail tracks branch and join from/to the currently travelled rail track. By tracking these rail tracks in the images, switches are detected as they are passed. It is shown that the followed track can be determined at branching switches. The approach is tested with real data from test rides in different locations and under a variety of weather conditions and environments. It proved to be very robust and of high practical use for track-selective self-localization of railroad vehicles, mandatory for collision avoidance.

Automated Vehicle-to-Vehicle Collision Avoidance at Intersections

Abstract: The authors present experimental results for an active control Intersection Collision Avoidance (ICA) system implemented on modified Lexus IS250 test vehicles. The system utilizes vehicle-to-vehicle (V2V) Dedicated Short-Range Communications (DSRC) to share safety critical state information, allowing for distributed implementation of the provably safe algorithms. Safety is achieved in potential collision scenarios by controlling the velocities of both vehicles with automatic brake and throttle commands. Automatic commands can never cause the violation of predefined upper and lower speed limits.

Aircraft Collision Avoidance Using Monte Carlo Real-Time Belief Space Search

Abstract: The aircraft collision avoidance problem can be formulated using a decision-theoretic planning framework where the optimal behavior requires balancing the competing objectives of avoiding collision and adhering to a flight plan. Due to noise in the sensor measurements and the stochasticity of intruder state trajectories, a natural representation of the problem is as a partially-observable Markov decision process (POMDP), where the underlying state of the system is Markovian and the observations depend probabilistically on the state. Many algorithms for finding approximate solutions to POMDPs exist in the literature, but they typically require discretization of the state and observation spaces. This paper investigates the introduction of a sample-based representation of state uncertainty to an existing algorithm called Real-Time Belief Space Search (RTBSS), which leverages branch-and-bound pruning to make searching the belief space for the optimal action more efficient. The resulting algorithm, called Monte Carlo Real-Time Belief Space Search (MC-RTBSS), is demonstrated on encounter scenarios in simulation using a beacon-based surveillance system and a probabilistic intruder model derived from recorded radar data.

Close target reconnaissance with guaranteed collision avoidance

Abstract: This manuscript considers the problem of close target reconnaissance by a group of autonomous agents. The overall close target reconnaissance (CTR) involves subtasks of avoiding inter-agent collisions, reaching a close vicinity of a specific target position, and forming an equilateral polygon formation around the target. The agents performing the task fly at a constant speed to mimic the velocity behavior of small fixed-wing unmanned aerial vehicles (UAV). A decentralized control scheme is developed for this overall task and the finite-time convergence of the system under the proposed control law is established. Furthermore, it is guaranteed that no collision occurs among the agent. The relevant analysis and simulation test results are provided. Copyright © 2010 John Wiley & Sons, Ltd.

Reactive obstacle avoidance for Rotorcraft UAVs

Abstract: We present a goal-directed 3D reactive obstacle avoidance algorithm specifically designed for Rotorcraft Unmanned Aerial Vehicles (RUAVs) that fly point-to-point type trajectories The algorithm detects potential collisions within a cylindrical Safety Volume projected ahead of the UAV This is done in a 3D occupancy map representation of the environment An expanding elliptical search is performed to find an Escape Point; a waypoint which offers a collision free route past obstacles and towards a goal waypoint An efficient occupied voxel checking technique is employed which approximates the Safety Volume by a series of spheres, and uses an approximate nearest neighbour search in a Bkd-tree representation of the occupied voxels Tests show the algorithm can typically find an Escape Point in under 100 ms using onboard UAV processing for a cluttered environment with 20 000 occupied voxels Successful collision avoidance results are presented from simulation experiments and from flights with an autonomous helicopter equipped with stereo and laser range sensors

Collision avoidance systems in air and maritime traffic

Abstract: In each transport mode, collisions between vehicles are one of the major operational risks. In maritime traffic collisions and groundings is the category with the highest frequency of all accidents. Although new navigational equipment, often combined with enhanced computer-based systems, is installed on ships’ bridges, the number of collisions is still at a high level. The equivalent of ship collisions in air traffic is the mid-air collision. Compared to the maritime accident rate, mid-air collisions are very rare. In this paper we investigate the onboard collision avoidance process by focusing on technical support systems with implemented alerts to support the human operator. Empirical field studies have been undertaken to analyse the situation regarding the occurrence and handling of alerts onboard seagoing vessels. Especially with respect to collision avoidance alarms, the situation was found to be unsatisfactory. The algorithms in use are based on fixed static limit values and little selected informat...