Cooperative Collision Avoidance Research Articles

E 2 CoPre: Energy Efficient and Cooperative Collision Avoidance for UAV Swarms With Trajectory Prediction

<i>E</i> ² <i>CoPre</i>: Energy Efficient and Cooperative Collision Avoidance for UAV Swarms With Trajectory Prediction

Increasing Efficiency and Reliability in Multicast Routing based V2V Communication for Direction-Aware Cooperative Collision Avoidance

Mobile ad hoc networks (MANETs), which are a promising method for the intelligent transportation system, include vehicular ad hoc networks (VANETs) (ITS). Developing reliable and strong cooperative collision avoidance (CCA) strategy to mitigate the growing number of road fatalities each year is one of the main difficulties facing vehicular ad hoc networks (VANETs).A proper and successful routing method aids in the successful expansion of vehicular ad hoc networks. This study explains the architecture, interface layers, safety features, and implementation of a novel priority-based direction-aware collision avoidance system (P-DVCA). It distinguishes our study in the collision area of VANETs by accounting for realistic bi-directional traffic. The scheme begins with the development of dynamic clusters, which is difficult because of the bi-directional diverse traffic and the need to avoid collisions within and between clusters. The target node is sent an early warning message that includes the safe speed and the likelihood of a collision in order to notify it of an impending danger. To determine the least expensive, shortest one with the fewest hops between the source and the endpoint. A crucial problem with VANETs is the transmission of data from a source node to the base station. Cross-layer issues must be solved for a robust and stable collision avoidance programme to function properly in a VANET communication architecture. The results of the simulation show that the suggested scheme significantly outperforms CCM and C-RACCA in terms of cluster stability, fewer collisions, low latency, and low communication overhead. According to the findings, P-DVCA offers stable clustering, minimises network congestion, and lowers communication overhead and latency.

Game-Theoretic Shared Control Strategy for Cooperative Collision Avoidance Under Extreme Conditions

Game-Theoretic Shared Control Strategy for Cooperative Collision Avoidance Under Extreme Conditions

A novel cooperative collision avoidance system for vehicular communication based on deep learning

A novel cooperative collision avoidance system for vehicular communication based on deep learning

A Decision Support System Using Fuzzy Logic for Collision Avoidance in Multi-Vessel Situations at Sea

The increasing traffic and complexity of navigation at sea require advanced decision support systems to ensure greater safety. In this study, we propose a novel decision support system that employs fuzzy logic to improve situational awareness and to assist navigators in collision avoidance during multi-vessel encounters. The system is based on the integration of the rules of the Convention on International Regulations for Preventing Collisions at Sea (COLREGs) and artificial intelligence techniques. The proposed decision model consists of two main modules to calculate the initial encounter conditions for the target vessels, evaluate the collision risk and navigation situation based on COLREG rules, sort the target vessels, and determine the most dangerous vessel. Fuzzy logic is used to calculate the collision avoidance maneuver for the selected ship, considering the closest point of approach, relative bearing, and the ship’s own speed. Simulation tests demonstrate the effectiveness of the fuzzy-based decision model in scenarios with two ships. However, in complex situations with multiple ships, the performance of the model is affected by possible conflicts between evasive maneuvers. This highlights the need for a cooperative collision avoidance algorithm for all vessels in high traffic areas.

Decentralized Robust Collision-Avoidance for Cooperative Multirobot Systems: A Gaussian Process-Based Control Barrier Function Approach

Data-based Machine Learning methods have been successfully applied to control system design in recent years. However, safety during the learning and control process is difficult to guarantee due to the inherent uncertainties. In this work, we propose a barrier function-based robust cooperative collision avoidance control framework for heterogeneous multirobot systems with model learning. First, a new control barrier function (CBF) design is proposed for cooperative collision avoidance, which leads to less conservativeness of the feasible control actions. Then, decentralized robust CBF conditions are derived which incorporate the estimation of the individual model uncertainty using Gaussian Process models such that each robot can guarantee safety with a high probability. Finally, a quadratic programming problem is formulated to obtain a controller which is minimally invasive to the nominal controller and satisfies the CBF and velocity constraints simultaneously. The decentralized implementation of the robust collision avoidance control strategy is explicitly shown and proven. Two simulation examples are given to demonstrate the effectiveness of the proposed control framework.

Survey on Cooperative Collision Avoidance Research for Ships

Communication among the ships can provide additional information that assists ships in negotiating and collaborating with others to take effective actions. This paper provides a review on ship collision avoidance methods proposed during the last decade from the perspectives of communication and cooperation. Two innovatively indices are defined, i.e., the communication index and the cooperation index. The overview of the existing research shows that collision risks are the most often exchanged information, while weak cooperation based on predefined protocols or rules, like COLREGs, is the majority. Competition and high-level cooperation are rarely studied in the past, but these topics have attracted increasing attention. Moreover, the analysis of the two indices reveals that communication is the premise of cooperation. Higher-level cooperation requires detailed information, which means higher communication requirements. The main challenges for future research are identified, including cooperation among heterogeneous ships considering various autonomous levels and cooperation levels, the stability of the communication network and the reliability of the exchanged information, methods for obtaining the required information, and quantitative interpretations of COLREGs and seamanship.

Distributed Coordinated Brake Control for Longitudinal Collision Avoidance of Multiple Connected Automated Vehicles

The emerging connected automated vehicle (CAV) technique brings a new opportunity for the development of cooperative collision avoidance systems. This paper proposes a distributed coordinated brake control (DCBC) algorithm for longitudinal collision avoidance of multiple CAVs. Firstly, the studied scenario is defined, within which the communication topology and vehicle dynamics are modeled for a vehicle string and the control objective is designed to minimize the total relative kinetic energy. Next, the control problem is formulated into a centralized model predictive control problem, which is then transformed into a quadratic programming (QP) problem. By separating the QP problem into several sub-problems assigned to each CAV, distributed optimization is introduced to solve the QP problem in a distributed manner. Elaborate numerical simulation comparisons are conducted to demonstrate the advantage of the proposed DCBC algorithm.

Autonomous navigation for multiple unmanned aerial vehicles (UAVs) in urban environments

This paper presents an autonomous navigation system for multiple unmanned aerial vehicles (UAVs) in urban environments. The navigation system comprises a long-term route planner and a short-term cooperative collision avoidance function. A 3D model of a real urban terrain is used to represent the environment within which the UAVs operate, and the system takes the motion constraints of the UAVs into account when performing planning. The navigation system enables multiple independent UAVs to travel from their departure points to their destinations while predicting and avoiding collisions with one another and with static terrain. Monte Carlo simulations on the system indicate a worst-case route planner replan rate of 11.65 %, route planner failure rate of 9.82×10-4 %, and collision resolution failure rate of 0.12 % for the collision avoidance function.

Decision-making of active collision avoidance system based on comprehensive evaluation method of dangerous scenarios

Dangerous scenario modeling is of great significance for the development of automatic driving functions, and the active collision avoidance system directly affects the safety of vehicles and the intelligence level of vehicles. However, the current existing collision risk evaluation methods are difficult to comprehensively evaluate different dangerous scenarios, the evaluation methods are complicated and there is no normalized description form. In addition, the traditional decision-making method of active collision avoidance system based on collision time distance will lead to the problems of inconsistent risk evaluation scale and single way of collision avoidance. To solve the above problems, the mechanism analysis of the longitudinal, lateral, and parallel collision risks is carried out respectively, and then the model based on kinematics and dynamics is established. After that, the corresponding expression form of collision risks is derived. Finally, the comprehensive evaluation method of dangerous collision scenarios is proposed. Secondly, based on the comprehensive evaluation method of collision risk, considering longitudinal constraints, lateral constraints, parallel constraints, and road regulations, an active collision avoidance decision-making method that used the cooperative collision avoidance mode of breaking and steering is designed. Thirdly, different simulation experiments are designed based on PanoSim/Simulink. The results show the comprehensive evaluation method can effectively model the collision risk of vehicles in the motion process. At the same time, the decision-making method of the active collision avoidance system achieves the expected working effect, thus improving driving stability and safety.

Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

AbstractCollision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

Ship Collaborative Path Planning Method Based on CS-STHA

Ship path planning is one of the key technologies for ship automation. Establishing a cooperative collision avoidance (CA) path for multi-ship encounters is of great value to maritime intelligent transportation. This study aims to solve the problem of multi-ship collaborative collision avoidance based on the algorithm of Conflict Search (CS) and Space-Time Hybrid A-star (STHA). First, a static CA path is searched for each ship by using the space-time Hybrid A-star algorithm, and the conflict risk area is determined according to the ship safety distance constraint and fuzzy Collision Risk Index (CRI). Secondly, the space-time conflict constraint is introduced into the multi-ship cooperative CA scheme, and the binary tree is used to search for an optimal navigation path with no conflict and low cost. In addition, the optimal path is smoothed by using cubic interpolation to make the path consistent with actual navigation practice and ship maneuvering characteristics. Finally, considering the constraints of the International Regulations for Preventing Collisions at Sea (COLREGs), the typical two-ship and multi-ship encounter scenarios are designed and simulated to verify the effectiveness of the proposed method. Furthermore, a comparative analysis of actual encounters and encounters based on CS-STHA is also carried out. The results indicate that the proposed algorithm in the study can obtain an optimal CA path effectively and provide a reference of CA decision-making for autonomous ships.

Timing of stand-on ship actions for cooperative collision avoidance under calm water condition

Rule 17 of the COLREGS states that a stand-on ship should take cooperative action to avoid a collision when the action of the give-way ship will not be sufficient to avoid a collision. However, most studies on collision avoidance have focused on the action of the give-way ship and not on what the stand-on ship should do. In this study, a novel criterion was developed for assessing when a stand-on ship should take action for collision avoidance that is called the computed distance at collision (CDC). Simulations were performed of the maneuvering motions of two ships in various situations to assess whether or not a collision would occur. The results of the collision assessment were assessed by the CDC, which is calculated from the ship's parameters such as the ship's heading, length, and collision angle between the two ships. Meanwhile, the actual distance (AD) between the two ships can be calculated according to their coordinates. When the CDC is greater than the AD, this means that a collision is inevitable. In other words, the stand-on ship should take action to avoid a collision while the CDC is still less than the AD. Of course, this study may have the following limitations: The study was conducted on the same model ship under calm water conditions. The interactions of hull-propeller-rudder-engine of ship and interaction of hull-hull between the two ships was not considered, and uncertainty of MMG (Maneuvering Modeling Group) model may exist. However, this study should help determine the timing of actions by the stand-on ship for collision avoidance, whether manned or unmanned.

V2X Communication for Message Transmission and Warning Detection

Currently, the way that smart cars operate is that they have sensors all around the vehicle to identify and learn about adjacent objects. However, the precision of these sensors frequently has a relatively short range. Reliable long-distance data collection is becoming increasingly important as autonomous driving systems gain in popularity. The suggested remedy for this problem is a gadget that would be mounted on every moving vehicle. This gadget would transmit data about the car (size, speed, acceleration, position, heading, etc.) and receive data about nearby cars with comparable equipment. This eliminates the need for intelligent vehicles to guess what other vehicles are doing around them. Vehicles can instead get this information directly from other vehicles on the road and spend more resources monitoring/tracking non-vehicular objects and feed their information directly to an intelligent system for further complex decision making. This report mainly consists of collecting data for M2M communication and analyzing data for V2V based protocols; establishing simple V2V communication with software tool and sample data; code for V2V communication using CCA with the help of AoDV; design and simulation of hardware architecture in its application. The simulation is performed on an open-source software which is created on its latest release. The test cases run is in a simulated environment of nodal communication representing clusters of vehicles on the radar interacting with each other. These nodes have reactive protocols such as Co- operative Collision Avoidance (CCA) and Ad-Hoc On Demand Distance Vectoring (AoDV). These aim at providing early warning and message routing using unicast and multicast. The results presented in this report show that the through-put, network-life, energy consumed and distance between the nodes are crucial factors that help route message passing between nodes in a given Wireless Sensor Network (WSN). The results obtained from the project discuss WSN communication using AoDV and CCA, along with this the hardware application is built on the idea to transfer vehicle information such as acceleration and displacement using the ESP-32 control module to establish communication that validates the message transmission between these modules in offline mode.

Review of multiple unmanned surface vessels collaborative search and hunting based on swarm intelligence

In recent years, the research of multiple unmanned surface vessels collaboration has received great attention. More and more researchers have proposed different methods of multiple unmanned surface vessels collaboration, such as cooperative collision avoidance, formation, and rendezvous. Based on the significant advantages of biological swarm intelligence applications in these collaborative methods, this article summarizes the research methods of multiple unmanned surface vessels collaborative search and hunting from the perspective of swarm intelligence. First of all, this article summarizes the key technologies of multiple unmanned surface vessels collaborative search and hunting from the aspects of the multi-robot system, group communication, environment modeling, collaboration mechanism, and path planning. Then, it reviews some classic swarm intelligence algorithms, analyzes the advantages and disadvantages of these algorithms, and proposes optimization directions for existing disadvantages based on relevant literature. Finally, the article points out some existing problems in every stage and suggestions for future research.

Distributed model predictive control for multi‐robot systems with conflicting signal temporal logic tasks

In this paper, the control problem of multi-robot systems under temporal logic tasks with limited time and logic constraints is studied, where each robot is required to reach the specified region in a given time and avoid collision all the time. Since the cooperative collision avoidance task of one robot depends on other robots' behaviors, the satisfaction of all the tasks may be conflicting. In this work, a distributed model predictive control (DMPC) strategy is proposed for conflicting temporal logic tasks. First, signal temporal logic (STL) is adopted to formally describe the temporal logic tasks. Based on robust semantics of STL formulas, a reference trajectory for the satisfaction degree of the task is designed to determine the short-term task in the optimization horizon. In the DMPC optimization problem, the compatibility constraints are introduced to redesign the collision avoidance constraints, such that the collision avoidance tasks can be fulfilled using neighbouring robots' predicted information of the last time instant. Then, the terminal constraint is designed by the short-term motion task which enforces each robot to move towards the goal region within the specified time interval. For conflicting tasks, a slack parameter is introduced in the terminal set to relax the motion task of each robot. The recursive feasibility of the DMPC algorithm is guaranteed, and the relaxed temporal logic tasks are fulfilled. In the proposed method, the discrete events including limited time and logic requirements are incorporated into the DMPC optimization problem, such that the motion task can be satisfied as much as possible and safety requirements are fulfilled. The effectiveness of the algorithm is illustrated by simulation and experiment results.

Cooperative Navigation Strategy for Connected Autonomous Vehicle Operating at Smart Intersection

This paper focuses on the cooperative navigation strategy for connected autonomous vehicles operating at smart intersections. The goal of this work is a cooperative navigation system to achieve cooperative collision avoidance for enhancing the safety and capacity of the intersection. This work considers cooperative connected autonomous vehicles operating simultaneously with non-cooperative autonomous vehicles. This work uses beyond visual range scenarios to reduce the vulnerable situations. Beyond visual range, information is implemented by using the data from the roadside units, autonomous intersection management system, smart traffic lights, and onboard units. The efficacy of this work is validated in MATLAB/Simulink environment. The simulation results show the separation time within the set upper and lower bounds. That ensures that the ego vehicle does not collide with others at the intersection. The cooperative collision avoidance algorithm guides the ego vehicle as soon as the ego vehicle comes in the range of the intersection service area, which increases the safety and capacity of the intersection. This strategy is comfortably used for both an unsignalized and signalized intersection. In an unsignalized intersection scenario, the ego vehicle uses an onboard unit. In signalized intersection scenario, the ego vehicle uses a roadside unit, onboard unit, autonomous intersection management system, and smart traffic lights. As no such framework is found in the literature. The proposed framework is the near-future requirement where the connected autonomous vehicle utilizes the information from smart infrastructure devices.

Coordination Between Connected Automated Vehicles and Pedestrians to Improve Traffic Safety and Efficiency at Industrial Sites

Transportation in controlled industrial sites provides a conducive environment for technologies of Connected Automated Vehicles (CAV). Recent studies show that safe and efficient road sharing between CAVs and pedestrians is challenging. Besides safety issues, a significant loss of time occurs when pedestrians cross a stream of CAVs. Currently, many techniques have been employed to improve the coordination between CAVs and pedestrians. They focus on pedestrian detection, display of the intention of CAVs, and cooperative collision avoidance. However, one of the most significant sources of information that the pedestrian uses for her/his decision-making is the speed profile of CAVs. This paper aims to provide a safe and efficient pedestrian crossing at industrial sites through communicative crossing behavior. To this end, a suitable speed profile of the CAV is designed by assuming that pedestrians and CAV play a cooperative game to move as close as possible to their desired speed. First, a system analysis is proposed to derive the optimal decision and trajectory for each agent. Then, Deep Reinforcement Learning (DRL) is used to control the longitudinal speed of the CAVs. Compared with Model Predictive Control approach, DRL allows coping with unforeseeable pedestrian behaviors (e.g. long reaction time, varying ideal speed, stop in the middle, etc.). Simulations and experiments with real human testers based on immersive hamlet are performed. Results show that the proposed speed profile outperforms significantly the collision avoidance approach.

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.

Intelligent Cooperative Collision Avoidance at Overtaking and Lane Changing Maneuver in 6G-V2X Communications

The rapid growth in Autonomous Vehicle (AV) technology endeavors increased attention towards road safety in recent days. Particularly, a higher number of road accidents occurs when the AV tries to overtake or change the lane. To cut down the number of accidents and improve traffic reliability, the AV should be capable of making intelligent decisions and communicating those to other AVs. Therefore, in this paper, a Cooperative Collision avoidance scheme for AVs at Overtaking and Lane Changing maneuver (CCAV-OLC) is proposed. The Inverse Reinforcement Learning (IRL) in the CCAV-OLC scheme, processes on the given number of expert demonstrations for automatically acquiring the reward function, and thereby imitating actual human driving strategy and decisions. However, the adaptability of IRL to a high-dimensional AV environment restricts the performance of the CCAV-OLC scheme. To overcome this, the IRL in CCAV-OLC leverages the Gaussian Process (GP) regression model (IRL-GP), which enables data-efficient Bayesian prediction even when the number of demonstrations is very low. After taking intelligent decisions in overtaking and lane changing maneuvers, the AVs cooperatively communicate and exchange the decisions with each other by 6th Generation Vehicle-to-Everything (6G-V2X) communications, which further improves the accuracy and lessens the time taken for making optimal decisions. The experimental results show that the AVs clone the expert's optimal driving strategy and avoid the collisions to a greater extent.