Showing papers on "Collision avoidance published in 2014"

Safe Maritime Autonomous Navigation With COLREGS, Using Velocity Obstacles

Abstract: This paper presents an autonomous motion planning algorithm for unmanned surface vehicles (USVs) to navigate safely in dynamic, cluttered environments. The algorithm not only addresses hazard avoidance (HA) for stationary and moving hazards, but also applies the International Regulations for Preventing Collisions at Sea (known as COLREGS, for COLlision REGulationS). The COLREGS rules specify, for example, which vessel is responsible for giving way to the other and to which side of the “stand-on” vessel to maneuver. Three primary COLREGS rules are considered in this paper: crossing, overtaking, and head-on situations. For autonomous USVs to be safely deployed in environments with other traffic boats, it is imperative that the USV's navigation algorithm obeys COLREGS. Furthermore, when other boats disregard their responsibility under COLREGS, the USV must fall back to its HA algorithms to prevent a collision. The proposed approach is based on velocity obstacles (VO) method, which generates a cone-shaped obstacle in the velocity space. Because VOs also specify on which side of the obstacle the vehicle will pass during the avoidance maneuver, COLREGS are encoded in the velocity space in a natural way. Results from several experiments involving up to four vessels are presented, in what we believe is the first on-water demonstration of autonomous COLREGS maneuvers without explicit intervehicle communication. We also show an application of this motion planner to a target trailing task, where a strategic planner commands USV waypoints based on high-level objectives, and the local motion planner ensures hazard avoidance and compliance with COLREGS during a traverse.

Application of social force model to pedestrian behavior analysis at signalized crosswalk

Abstract: Limited pedestrian behavior models shed light on the case at signalized crosswalk, where pedestrian behavior is characterized by group or individual evasion with surrounding pedestrians, collision avoidance with conflicting vehicles, and response to signal control and crosswalk boundary. This study fills this gap by developing a microscopic simulation model for pedestrian behavior analysis at signalized intersection. The social force theory has been employed and adjusted for this purpose. The parameters, including measurable and non-measurable ones, are either directly estimated based on observed dataset or indirectly derived by maximum likelihood estimation. Last, the model performance was confirmed in light of individual trajectory comparison between estimation and observation, passing position distribution at several cross-sections, collision avoidance behavior with conflicting vehicles, and lane-formation phenomenon. The simulation results also concluded that the model enables to visually represent pedestrian crossing behavior as in the real world.

Towards a Socially Acceptable Collision Avoidance for a Mobile Robot Navigating Among Pedestrians Using a Pedestrian Model

Abstract: Safe navigation is a fundamental capability for robots that move among pedestrians. The traditional approach in robotics to attain such a capability has treated pedestrians as moving obstacles and provides algorithms that assure collision-free motion in the presence of such moving obstacles. In contrast, recent studies have focused on providing the robot not only collision-free motion but also a socially acceptable behavior by planning the robot’s path to maintain a “social distance” from pedestrians and respect their personal space. Such a social behavior is perceived as natural by the pedestrians and thus provides them a comfortable feeling, even if it may be considered a decorative element from a strictly safety oriented perspective. In this work we develop a system that realizes human-like collision avoidance in a mobile robot. In order to achieve this goal, we use a pedestrian model from human science literature, a version of the popular Social Force Model that was specifically designed to reproduce conditions similar to those found in shopping malls and other pedestrians facilities. Our findings show that the proposed system, which we tested in 2-h field trials in a real world environment, not only is perceived as comfortable by pedestrians but also yields safer navigation than traditional collision-free methods, since it better fits the behavior of the other pedestrians in the crowd.

Trajectory tracking with collision avoidance for nonholonomic vehicles with acceleration constraints and limited sensing

Abstract: Nowadays, autonomously operated nonholonomic vehicles are employed in a wide range of applications, ranging from relatively simple household chores (e.g. carpet vacuuming and lawn mowing) to highly sophisticated assignments (e.g. outer space exploration and combat missions). Each application may require different levels of accuracy and capabilities from the vehicles, yet, all expect the same critical outcome: to safely complete the task while avoiding collisions with obstacles and the environment. Herein, we report on a bounded control law for nonholonomic systems of unicycle-type that satisfactorily drive a vehicle along a desired trajectory while guaranteeing a minimum safe distance from another vehicle or obstacle at all times. The control law is comprised of two parts. The first is a trajectory tracking and set-point stabilization control law that accounts for the vehicle's kinematic and dynamic constraints (i.e. restrictions on velocity and acceleration). We show that the bounded tracking control law enforces global asymptotic convergence to the desired trajectory and local exponential stability of the full state vector in the case of set-point stabilization. The second part is a real-time avoidance control law that guarantees collision-free transit for the vehicle in noncooperative and cooperative scenarios independently of bounded uncertainties and errors in the obstacles' detection process. The avoidance control acts locally, meaning that it is only active when an obstacle is close and null when the obstacle is safely away. Moreover, the avoidance control is designed according to the vehicle's acceleration limits to compensate for lags in the vehicle's reaction time. The performance of the synthesized control law is then evaluated and validated via simulation and experimental tests.

Cooperative Visibility Maintenance for Leader–Follower Formations in Obstacle Environments

Abstract: Vision-based formation control of multiple agents, such as mobile robots or fully autonomous cars, has recently received great interest due to its application in robotic networks and automated highways. This paper addresses the cooperative motion coordination of leader-follower formations of nonholonomic mobile robots, under visibility and communication constraints in known polygonal obstacle environments. We initially consider the case of N = 2 agents moving in L-F fashion and propose a feedback control strategy under which L ensures obstacle avoidance for both robots, while F ensures visibility maintenance with L and intervehicle collision avoidance. The derived algorithms are based on set-theoretic methods to guarantee visibility maintenance, dipolar vector fields to maintain the formation shape, and the consideration of the formation as a tractor-trailer system to ensure obstacle avoidance. We furthermore show how the coordination and control design extends to the case of N > 2 agents, and provide simulation results, which demonstrate the efficacy of the control solutions. The proposed algorithms do not require information exchange among robots, but are instead based on information locally available to each agent. In this way, the desired tasks are executed and achieved in a decentralized manner, with each robot taking care of converging to a desired configuration, while maintaining visibility with its target.

Adjustments of speed and path when avoiding collisions with another pedestrian.

Abstract: When walking in open space, collision avoidance with other pedestrians is a process that successfully takes place many times. To pass another pedestrian (an interferer) walking direction, walking speed or both can be adjusted. Currently, the literature is not yet conclusive of how humans adjust these two parameters in the presence of an interferer. This impedes the development of models predicting general obstacle avoidance strategies in humans’ walking behavior. The aim of this study was to investigate the adjustments of path and speed when a pedestrian is crossing a non-reactive human interferer at different angles and speeds, and to compare the results to general model predictions. To do so, we designed an experiment where a pedestrian walked a 12 m distance to reach a goal position. The task was designed in such a way that collision with an interferer would always occur if the pedestrian would not apply a correction of movement path or speed. Results revealed a strong dependence of path and speed adjustments on crossing angle and walking speed, suggesting local planning of the collision avoidance strategy. Crossing at acute angles (i.e. 45° and 90°) seems to require more complex collision avoidance strategies involving both path and speed adjustments than crossing at obtuse angles, where only path adjustments were observed. Overall, the results were incompatible with predictions from existing models of locomotor collision avoidance. The observed initiations of both adjustments suggest a collision avoidance strategy that is temporally controlled. The present study provides a comprehensive picture of human collision avoidance strategies in walking, which can be used to evaluate and adjust existing pedestrian dynamics models, or serve as an empirical basis to develop new models.

Algorithms for collision detection and avoidance for five-axis NC machining: A state of the art review ☆

Abstract: Five-axis CNC machine tools are more and more popular in machining area, because of their ability to machine parts with complex geometries efficiently as well as achieve higher dimensional accuracy. Since two additional rotational axes are introduced in five-axis machines, there are difficult geometric problems that need to be solved in order to take full advantages of five-axis machining, and the most complex problems are collision detection and avoidance. Due to its widespread importance, a lot of researches have been carried out to solve the collision detection and avoidance problems. These include the surface properties analysis based method, convex hull based method, C -space based method, accessibility based method, bounding volume and space partition method, distance calculation (vector) based method, rolling ball method, radial projection method, graphic-assisted method, and sweep plane approach. This paper aims at providing a state of the art review on algorithms for collision detection and avoidance in five-axis NC machining. In addition, a comparison of algorithms for collision detection and avoidance is considered.

Situation awareness in remote control centres for unmanned ships

Abstract: The feasibility of unmanned, autonomous merchant vessels is investigated by the EU project MUNIN (Maritime Unmanned Navigation through Intelligence in Networks). The ships will be manned during passage to and from port and unmanned during ocean-passage. When unmanned, the ships will be controlled by an automatic system informed by onboard sensors allowing the ship to make standard collision avoidance manoeuvres according to international regulation. The ship will be continuously monitored by a remote shore centre able to take remote control should the automatic systems falter. For the humans in the shore control centre the usual problems of automations remains as well as a pronounced problem of keeping up adequate situation awareness through remote sensing.

A method of vehicle motion prediction and collision risk assessment with a simulated vehicular cyber physical system

Abstract: Vehicular cyber physical system (VCPS) can comprehensively acquire road traffic safety related information, and provide drivers with early warning or driving assistance in emergency, in order to assist them avoid vehicle crash in the driving process. Literature review shows that previous studies mainly rely on observed vehicle motion/location data for assessing vehicle collision risk, where predicted vehicle motion/location, driver behavior and road geometry (e.g., curvature) are rarely considered. In this study, based on the simulated VCPS, a collision avoidance system that can explicitly consider the above issues is designed and presented in detail. Within the proposed collision avoidance system, an assessment method, which can predict collision risk by comprehensively considering vehicles motion/location, driver behavior and road geometry information from the VCPS, is developed. Firstly, the short-term motion of the objective vehicle and surrounding vehicles are predicted based on the Kalman Filter (KF) algorithm and the vehicle motion model. Furthermore, the proposed method that can explicitly take driver behavior and road curvature into account is used to predict vehicle location and calculate the traveled distance among vehicles in real-time. Then, the predicted vehicle gaps are compared with a safe distance threshold and the vehicle collision risk is predicted. Finally, the accuracy of the proposed collision risk assessment method is examined with a receiver operating characteristic (ROC) curve analysis over a section of curved road. Simulation results show that the proposed method is effective for detecting collision risk and providing accurate warnings in a timely fashion.

Vehicle-to-vehicle wireless communication for controlling accident avoidance procedures

Abstract: Methods, computer systems, and servers for processing collision avoidance feedback to vehicles using vehicle-to-vehicle wireless communication, are provided. One method includes detecting proximity separation between a first vehicle and a second vehicle (e.g., and other vehicles within the proximity separation). At least one of the sensors of the first vehicle or the second vehicle determine that a proximity separation is less than a threshold distance. A pairing algorithm is triggered between electronics of the first and second vehicle to enable direct communication for data exchange between the first and second vehicles. The method includes triggering a warning to one or both of the first and second vehicles if the data exchange determines that a probability exists that a heading of the first or second vehicles will result in a collision between the first and second vehicles. The method may initiate corrective action by one or both of the first or second vehicles if the data exchange between the first and second vehicles increase the probability that the heading will result in a collision between the first and second vehicles.

Path planning method for mobile robot in dynamic environment

Abstract: The invention discloses a double-layer planning method based on the combination of global path planning and local rolling prediction collision avoidance planning, so as to solve the problem of path planning for a mobile robot in a dynamic environment. The method mainly comprises two parts: the global path planning and the local rolling prediction collision avoidance planning. The path planning method can better realize robot navigation, and improve intelligence of the robot. The double planning method can be utilized to prevent the blindness of planning in the beginning, and searching space of the problem is reduced; based on the uncertainty of the moving direction of a dynamic barrier and by utilizing the two collision prediction strategies and two corresponding collision avoidance strategies, the dynamic barrier can be avoided well; and particularly, in order to adapt to the change of environment better, in the second layer of planning, a Follow_wall behavior based on behavior method is added, so that when the environment changes, the mobile robot can still arrive at the target without touching the barriers safely.

Adaptive Artificial Potential Field Approach for Obstacle Avoidance Path Planning

Abstract: This paper presents an adaptive artificial potential field method for robot's obstacle avoidance path planning. Despite the obstacle avoidance path planning based on the artificial potential field method is very popular, but there is local minima problem with this approach. As a result, this paper proposes an improved obstacle potential field function model considering for the size of the robot and the obstacles and changes the weight of the obstacle potential field function adaptively to make the robot escape from the local minima. Three simulations have been done and the simulation results show: the improved algorithm can make the robot escape from the local minima and accomplish the robot collision avoidance path planning well.

Systems and methods for determining a vehicle is at an elevated risk for an animal collision

Abstract: Methods and systems for analyzing environment data to determine whether a vehicle operator is at an elevated risk for an animal collision are provided. According to certain aspects, an insurance provider may assess elevated risk according to various factors and, if it is determined that the vehicle operator is at an elevated risk for an animal collision, the insurance provider may generate a warning and wirelessly communicate the warning to the vehicle operator. The factors analyzed may include past accident, driver characteristic, weather, calendar, time of day, animal, seasonal, and/or other information. The vehicle operator may be notified of the risk and optionally presented with tips to mitigate the risk. The vehicle operator may be notified of the risk by a mobile device and/or the vehicle, such as from a vehicle communication and control system. Animal collision avoidance functionality may be used to adjust insurance premiums, rates, or rewards.

Hybrid Long-Range Collision Avoidancefor Crowd Simulation

Abstract: Local collision avoidance algorithms in crowd simulation often ignore agents beyond a neighborhood of a certain size. This cutoff can result in sharp changes in trajectory when large groups of agents enter or exit these neighborhoods. In this work, we exploit the insight that exact collision avoidance is not necessary between agents at such large distances, and propose a novel algorithm for extending existing collision avoidance algorithms to perform approximate, long-range collision avoidance. Our formulation performs long-range collision avoidance for distant agent groups to efficiently compute trajectories that are smoother than those obtained with state-of-the-art techniques and at faster rates. Comparison to real-world data demonstrates that crowds simulated with our algorithm exhibit an improved speed sensitivity to density similar to human crowds. Another issue often sidestepped in existing work is that discrete and continuum collision avoidance algorithms have different regions of applicability. For example, low-density crowds cannot be modeled as a continuum, while high-density crowds can be expensive to model using discrete methods. We formulate a hybrid technique for crowd simulation which can accurately and efficiently simulate crowds at any density with seamless transitions between continuum and discrete representations. Our approach blends results from continuum and discrete algorithms, based on local density and velocity variance. In addition to being robust across a variety of group scenarios, it is also highly efficient, running at interactive rates for thousands of agents on portable systems.

Computation of object approach by a system of visual motion-sensitive neurons in the crab Neohelice.

Abstract: Similar to most visual animals, crabs perform proper avoidance responses to objects directly approaching them. The monostratified lobula giant neurons of type 1 (MLG1) of crabs constitute an ensemb...

Automatic collision avoidance for manually tele-operated unmanned aerial vehicles

Abstract: In this paper we present an approach that aids the human operator of unmanned aerial vehicles by automatically performing collision avoidance with obstacles in the environment so that the operator can focus on the global direction of motion of the vehicle. As opposed to systems that override operator control as a last resort in order to avoid collisions (such as those found in modern automobiles), our approach is designed such that the operator can rely on the automatic collision avoidance, enabling intuitive and safe operator control of vehicles that may otherwise be difficult to control. Our approach continually extrapolates the future flight path of the vehicle given the current operator control input. If an imminent collision is predicted our algorithm will override the operator’s control input with the nearest control input that will actually let the vehicle avoid collisions with obstacles. This ensures safe flight while simultaneously maintaining the intent of the human operator as closely as possible. We successfully implemented our approach on a physical quadrotor system in a laboratory environment. In all experiments the human operator failed to crash the vehicle into floors, walls, ceilings, or obstacles, even when deliberately attempting to do so.

A Lane Change Warning System Based on V2V Communication

Abstract: A lane change warning system is proposed, which gets the surrounding vehicle's motion states based on V2V communication. The safe distance between the ego-vehicle and the rear vehicle in a target lane is analyzed according to the goal of both collision avoidance and vehicle following safety. Safe distance between the ego-vehicle and the front vehicle in the target lane is analyzed to ensure safety in emergency collision avoidance. Safe distances between the ego-vehicle and the front and rear vehicles in the original lane are analyzed to realize safe following. Meanwhile, a driving style index is proposed to adapt the warning system to different kinds of drivers. Simulation results show that the lane change warning system can make a composite analysis of the collision risk during the lane-changing, and provide an accurate real-time warning to assist drivers.

Active collision avoidance system for steering control of autonomous vehicles

Abstract: The study proposes an active collision avoidance system to allow safe lane-changing manoeuvres by self-steering vehicles in the presence of the uncertainties associated with nearby vehicles and the surrounding environment This system integrates estimation of conflict probability, model predictive control and dedicated short-range communications (DSRC) techniques to ensure a collision-free operation To accomplish this, the proposed system uses model predictive control to predict the future positions of vehicles and estimates the conflict probability so as to reduce the risk of collision The system also exploits DSRC techniques to facilitate the gathering of information from nearby vehicles so that potential conflicts can be detected at an earlier stage Autonomous vehicles can thus make adjustments based on the acquired data to avoid collisions in a real communication environment The effectiveness of the method has been verified under experimental conditions The influences of key parameters in the control method are examined

A real-time monocular vision-based frontal obstacle detection and avoidance for low cost UAVs in GPS denied environment

Abstract: This paper presents a novel monocular vision-based realtime obstacle detection and avoidance for a low cost unmanned aerial vehicle (UAV) in an unstructured, GPS denied environment We propose a mathematical model to estimate the relative distance from the UAV's camera to an obstacle which will subsequently be used in a collision avoidance algorithm We validate our model with some real time experiments under both stationary and dynamic motion of the UAV during its flight The results show good agreement with the ground truth values with an acceptable percentage of error in estimation under 3% thus proving it can facilitate obstacle detection and avoidance for low cost and lightweight UAVs

A Survey on Ship Collision Risk Evaluation

Abstract: Recently, ship collision avoidance has become essential due to the emergence of special vessels like chemical tankers and VLCCs (very large crude carriers), etc. The information needed for safe navigation is obtained by combining electrical equipment with real-time visual information. However, misjudgements and human errors are the major cause of ship collisions according to research data. The decision support system of Collision avoidance is an advantageous facility to make up for this. Collision risk evaluation is one of the most important problems in collision avoidance decision supporting system. A review is presented of different approaches to evaluate the collision risk in maritime transportation. In such a context, the basic concepts and definitions of collision risk and their evaluation are described. The review focuses on three categories of numerical models of collision risk calculation: methods based on traffic flow theory, ship domain and methods based on dCPA and tCPA.

Design and evaluation of steering protection for avoiding collisions during a lane change

Abstract: This paper discusses the design of a driver assistance system for avoiding collisions with vehicles in blind spots. The following three types of support systems are compared: (1) a warning system that provides the driver with an auditory alert, (2) a 'soft' protection system that makes the steering wheel stiffer to tell the driver that a lane-change manoeuvre is not recommended and (3) a 'hard' protection system that cancels the driver's input and controls the tyre angle autonomously to prevent lane departure. The results of an experiment showed that the hard protection system was more effective for collision avoidance than either the warning or the soft protection system. The warning and soft protection systems were almost the same in terms of collision avoidance. The results suggest that the human-centred automation principle, which requires the human to have the final authority over the automation, can be violated depending on the context.

Real-time collision avoidance in human-robot interaction based on kinetostatic safety field

Abstract: This paper addresses the problem of collision avoidance in human-robot interaction. To this end, we introduce the concept of kinetostatic safety field, a novel safety assessment about the risk in the vicinity of a rigid body (including a robot link or a human body part). The safety field depends on the position and velocity of the body but it is also influenced by its real shape and size. Since all the computation can be performed in closed form, the safety field is suitable for real-time applications. Moreover, we present a safety-oriented control strategy for redundant manipulators, based on safety field and developed entirely on the kinematic level, where the kinematic redundancy is exploited for simultaneous task performance and collision avoidance, such as self-collision avoidance and human-robot coexistence. The proposed control strategy is validated through experiments performed on ABB's FRIDA dual arm robot.

A rear-end collision avoidance system of connected vehicles

Abstract: The rear-end collision is one of the main types of accident, and it brings unnecessary casualties and property losses. Aiming at reducing the rear-end collision, some researchers focus on the front collision avoidance system. Generally, the rear-end collision avoidance system detects risk situation based on the information collected by radar or camera. When the following car is in risk of crash, the system will inform or warn the driver that the current speed is not safe, and help drivers to decelerate or brake. With the development of vehicle-to-vehicle communication (V2V), a new way to develop the rear-end collision avoidance system is put forward. By using the connected vehicles, the system can collect information and share message through the communicate equipment. In this paper, the authors introduce a collision avoidance system which is based on the vehicle-to-vehicle communication. This rear-end collision avoidance is with some driver behavior by using a car-following model based on risk perception. At last, the authors provide some experimental date about collision avoidance, and demonstrate the feasibility of this system in risk detection and crash avoidance.

Driving-Simulator-Based Test on the Effectiveness of Auditory Red-Light Running Vehicle Warning System Based on Time-To-Collision Sensor

Abstract: The collision avoidance warning system is an emerging technology designed to assist drivers in avoiding red-light running (RLR) collisions at intersections. The aim of this paper is to evaluate the effect of auditory warning information on collision avoidance behaviors in the RLR pre-crash scenarios and further to examine the casual relationships among the relevant factors. A driving-simulator-based experiment was designed and conducted with 50 participants. The data from the experiments were analyzed by approaches of ANOVA and structural equation modeling (SEM). The collisions avoidance related variables were measured in terms of brake reaction time (BRT), maximum deceleration and lane deviation in this study. It was found that the collision avoidance warning system can result in smaller collision rates compared to the without-warning condition and lead to shorter reaction times, larger maximum deceleration and less lane deviation. Furthermore, the SEM analysis illustrate that the audio warning information in fact has both direct and indirect effect on occurrence of collisions, and the indirect effect plays a more important role on collision avoidance than the direct effect. Essentially, the auditory warning information can assist drivers in detecting the RLR vehicles in a timely manner, thus providing drivers more adequate time and space to decelerate to avoid collisions with the conflicting vehicles.

Coordinated Control of Motor-Driven Power Steering Torque Overlay and Differential Braking for Emergency Driving Support

Abstract: This paper describes a coordinated control of motor-driven power steering (MDPS) torque overlay and differential braking for emergency driving support (EDS). The coordinated control algorithm is designed to assist drivers to overcome hazardous situations. Electrically controllable MDPS and brake system are used as actuators, and a radar and a camera are used as a sensor system. Using environmental and vehicle information obtained from the sensor system, a risk of collision and driver's intention are determined, and a collision avoidance trajectory is generated, incorporating the driver's intention. Based on the generated collision avoidance trajectory, the MDPS overlay torque is determined to assist the driver's speed of response, and differential braking is determined to maximize the minimum vehicle-to-vehicle distance to avoid collision. The performance of the proposed algorithm has been investigated via computer simulations and real-time (RT) human-in-the-loop simulations. The simulation studies show that the controlled vehicle can secure additional vehicle-to-vehicle distance in severe lane change maneuvering for collision avoidance. The success rate of collision avoidance has been investigated for eight test drivers using the human-in-the-loop simulations. It has been shown that most of the test drivers can benefit from the proposed support system.

The obstacle avoidance planning of USV based on improved artificial potential field

Abstract: The autonomous obstacle avoidance planning of USV is the guarantee and the precondition of carrying out the performance. Obstacle avoidance planning is required to possess high accuracy and instantaneity due to a complex environment and faster speed. The algorithm of Artificial Potential Field has the advantage of sample mathematical model, which is easy to understand and implement, and facilitate the underlying control. However, application of traditional Artificial Potential Field has the problems of local minimum, destination unreachable, and poor accuracy of algorithm. Aiming at these issues, a method of the obstacle avoidance planning of Unmanned Surface Vehicle based on improved Artificial Potential Field is proposed in this article, and its feasibility is demonstrated using MATLAB simulation.

3-D Reciprocal Collision Avoidance on Physical Quadrotor Helicopters with On-Board Sensing for Relative Positioning.

Abstract: In this paper, we present an implementation of 3-D reciprocal collision avoidance on real quadrotor helicopters where each quadrotor senses the relative position and velocity of other quadrotors using an on-board camera. We show that using our approach, quadrotors are able to successfully avoid pairwise collisions in GPS and motion-capture denied environments, without communication between the quadrotors, and even when human operators deliberately attempt to induce collisions. To our knowledge, this is the first time that reciprocal collision avoidance has been successfully implemented on real robots where each agent independently observes the others using on-board sensors. We theoretically analyze the response of the collision-avoidance algorithm to the violated assumptions by the use of real robots. We quantitatively analyze our experimental results. A particularly striking observation is that at times the quadrotors exhibit "reciprocal dance" behavior, which is also observed when humans move past each other in constrained environments. This seems to be the result of sensing uncertainty, which causes both robots involved to have a different belief about the relative positions and velocities and, as a result, choose the same side on which to pass.

Risk Assessment for Collision Avoidance Systems

Abstract: Collision Avoidance Systems need to perform scene analysis and risk assessment in order to react conveniently. Based on the information provided by the perception system, scene analysis has to predict the evolution of the current driving situation for the near future. Thanks to the predicted trajectories of the relevant traffic participants, the risk of collision on the ego vehicle can be calculated. In many cases, a predicted trajectory is not defined with explicit equations but is given as a set of sampled poses, each one corresponding to a different future time instant. A predicted trajectory being always uncertain, confidence has to be estimated on the so predicted poses. The authors present a method that propagates the known error covariance matrix of the current pose of the ego vehicle by considering local approximations of the predicted trajectory. This allows to estimate the risk of collision of the ego vehicle with a considered target object. The proposed approach uses a Monte Carlo simulation to approximate the probability that the ego vehicle and the object come into collision at a given future time instant. Each sample time of the whole prediction horizon is considered as a potential collision time so that a curve describing the variation of the risk of collision is obtained. This allows the system to have a better comprehension of the scene and to react proportionally to the threat. The overall approach has been tested with simulated data and the consistency of results is shown.