Emergency Collision Avoidance Research Articles

Emergency Collision Avoidance and Mitigation Using Model Predictive Control and Artificial Potential Function

Although extensive research in emergency collision avoidance has been carried out for straight or curved roads in a highway scenario, a general method that could be implemented for all road environments has not been thoroughly explored. Moreover, most current algorithms don't consider collision mitigation in an emergency. This functionality is essential since the problem may have no feasible solution. We propose a safe controller using model predictive control and artificial potential function to address these problems. A new artificial potential function inspired by line charge is proposed as the cost function for our model predictive controller. The vehicle dynamics and actuator limitations are set as constraints. The new artificial potential function considers the shape of all objects. In particular, the artificial potential function we proposed has the flexibility to fit the shape of the road structures, such as the intersection. We could also realize collision mitigation for a specific part of the vehicle by increasing the charge quantity at the corresponding place. We have tested our methods in 192 cases from 8 different scenarios in simulation with two different models. The simulation results show that the success rate of the proposed safe controller is 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> higher than using HJ-reachability with system decomposition by using a unicycle model. It could also decrease 43 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of collision that happens at the pre-assigned part. The method is further validated in a dynamic bicycle model.

Automatic Emergency Collision Avoidance of Straight-Crossing Intelligent Vehicle at a 90-Degree Intersection Based on Vehicle-to-Everything Technology

&lt;div&gt;In order to reduce collision at a 90-degree intersection, an automatic emergency collision avoidance control method for intelligent vehicles based on vehicle-to-everything (V2X) technology is proposed. Most of the existing automatic emergency braking (AEB) control algorithms are designed for a single high-friction road with reference to the European New Car Assessment Programme (Euro NCAP) evaluation procedures, and they do not consider changes in road friction. Thus, it may be difficult to avoid collision successfully on a low-friction road. Although some studies have considered the variation of road friction, they are only applicable to straight-line rear-end collisions and cannot be directly applied to intersections. In addition, most studies regard the vehicle only as a particle, ignoring the actual dynamic characteristics of the vehicle. The main contribution of this article is to present an AEB control strategy by V2X technology, which can make the intelligent vehicle avoid collisions at a 90-degree intersection effectively. The proposed time-to-collision (TTC) adaptive algorithm has considered various road surfaces, and its effectiveness is verified by the co-simulation of Matlab/Simulink, CarSim, and Prescan on a typical urban intersection road.&lt;/div&gt;

Collision Avoidance Path Planning for Vehicles Combining MPC and CACC Controllers

The vehicle collision avoidance system has problems such as poor emergency collision avoidance ability and susceptibility to noise interference. Therefore, this study adopts a vehicle tracking model that integrates dynamic constraints and a two car following model to construct a Collaborative Adaptive Cruise Control (CACC) model that integrates Multi Constraints Model Predictive Control (MPC). And conducted simulation experimental research. The results show that in emergency braking collision avoidance conditions, the collision avoidance model designed in this study can quickly respond to the distance control requirements, and the control stability is higher than that of common collision avoidance systems. The standard deviation of the rear car acceleration of the MPC+CACC, reinforcement learning (RL), and proportional-integral-differential (PID) models are 0.18m/s2, 0.25m/s2, and 0.46m/s2, respectively. The car spacing error of the MPC+CACC, RL, and PID models converge to 0m when the time is about 13 seconds, 33 seconds, and 40 seconds, respectively. This study has certain reference significance for improving the collision avoidance ability of China’s intelligent vehicle collision avoidance system.

A study of emergency collision avoidance by steering angle designed with energy-optimal control theory

A study of emergency collision avoidance by steering angle designed with energy-optimal control theory

Emergency collision avoidance strategy for autonomous vehicles based on steering and differential braking

This paper develops a novel integrated collision avoidance strategy for autonomous vehicles in an emergency based on steering and braking. Specifically, the framework of the collision avoidance strategy is composed of two parts: an up-level decision-making layer and a low-level controller layer. The purpose of the up-level is to select the appropriate control strategy based on the vehicle information, and the low-level is to drive the vehicle according to the instructions generated by the up-level. More concretely, a novel control strategy is proposed by integrating four-wheel steering, active rear steering, and differential braking with guaranteed path-tracking accuracy and driving stability by adaptive model predictive control (AMPC). Finally, extensive co-simulations in MATLAB/Simulink and CarSim are conducted to verify the effectiveness of the proposed collision avoidance strategy in terms of tracking error, yaw rate, and roll angle.

Real-Time Emergency Collision Avoidance for Unmanned Surface Vehicles with COLREGS Flexibly Obeyed

This paper presents a real-time emergency collision-avoidance method for unmanned surface vehicles (USVs) with the International Regulations for Preventing Collisions at Sea (COLREGS) flexibly obeyed. The pivotal issue is that some traffic vessels may violate the demands of this convention, which would increase the risk of collision if the USV blindly obeys the COLREGS rules. To avoid mandatory compliance with these COLREGS rules, a real-time truncated velocity obstacle (TVO) algorithm is proposed to assign a collision-free velocity vector for the control system to realize. Considering a reasonable trade-off between safety and the COLREGS rules, the proposed collision-avoidance method expands the TVO’s area based on the velocity uncertainties of traffic vessels, which greatly enhance the safety of collision-avoidance operations and encourage the USV to follow the COLREGS rules. To promptly realize an assigned collision-free velocity, this paper also develops a discrete simultaneous planning and executing (SPAE) controller design. The proposed discrete controller is divided into three parts: online polynomial planning to satisfy the constraints of tracking errors, an accurate uncertainty estimation, and an algebraic control law to promptly execute the planned polynomial. Numerical results have validated the reliability and intelligibility of the proposed collision-avoidance method. Furthermore, simulated and experimental results have validated the effectiveness of the proposed controller design.

Collision Avoidance Decision Method for Unmanned Surface Vehicle Based on an Improved Velocity Obstacle Algorithm

To ensure navigation safety, unmanned surface vehicles (USVs) need to have autonomous collision avoidance capability. A large number of studies on ship collision avoidance are available, and most of these papers assume that the target ships keep straight or follows the International Regulations for Preventing Collisions at Sea (COLREGS). However, in the actual navigation process, the target ship may temporarily turn. Based on the above reasons, this paper proposes a multi-ship collision avoidance decision method for USVs based on the improved velocity obstacle algorithm. In the basic dynamic ship domain model, a collision risk model is constructed to improve the accuracy of the risk assessment between the USV and target ships. The velocity obstacle algorithm is combined with the dynamic ship domain, and the collision avoidance timing and method are judged according to the collision risk. The simulation results show that the decision method can handle the situation that the target ship temporarily turns and has an emergency collision avoidance capability. Compared with the traditional VO algorithm, the collision avoidance time of the method is shorter, and the number of course changes is less.

Model Predictive Path-Planning Controller With Potential Function for Emergency Collision Avoidance on Highway Driving

Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.

Potential field‐based path planning for emergency collision avoidance with a clothoid curve in waypoint tracking

AbstractA novel potential field‐based model curve fitting method (PF‐MCF) is presented in this paper to handle emergency collision avoidance in waypoint tracking (following waypoints from a leading vehicle). It is reported that the PF has high performance on real‐time obstacle avoidance in robotic path planning. However, some inherent limitations exist in the PF, such as no passage between closely spaced obstacles and oscillations in the presence of obstacles, which is rarely discussed in the research field of PF‐based autonomous vehicles (AVs), especially in waypoint tracking. To solve these problems, we propose the PF‐MCF method to transform the clothoid curve into a quadratic programming form and solve the optimization under reasonable constraints to consider the different waypoints from the PF and the leading vehicle. The proposed PF‐MCF method is validated via MATLAB/Simulink and CarSim to avoid multiple obstacle vehicles in waypoint tracking compared with the two latest methods (PF‐based model predictive controller and PF‐based curve fitting method). The simulation results confirm the efficiency of finding a passage in closely spaced obstacle vehicles and eliminating oscillations (under 0.5 [deg] in tire steering angle) in the presence of obstacles compared to other latest methods.

Research on Emergency Collision Avoidance System of Man-Machine Cooperative Driving Vehicles Based on Additional Yaw Moment Control

Drivers of man-machine cooperative driving intelligent vehicles are affected by driving skills, physiological reactions, and other factors. Under emergency conditions, they often subconsciously forcefully take over control rights and produce unreasonable stress steering, which brings new accident risks to vehicles. To avoid collisions, this paper proposes an emergency collision avoidance control strategy for man-machine cooperative driving vehicles. In the collision avoidance path planning layer, considering the obstacle distance, road adhesion coefficient, vehicle speed, steering wheel stress angle, and driver’s linear steering cognition, a circular arc lane-change path is designed. The curvature mutation is smoothed using the third-order Bezier function. In the tracking control layer, a method of additional yaw moment control is designed by using the model predictive control (MPC) algorithm to track the path. The accuracy and safety of vehicle tracking are guaranteed only by adjusting the braking torque of each wheel of the vehicle, to correct the unreasonable input when the driver forces to take over. The co-simulation results show that the collision avoidance control system can effectively correct the unreasonable input during forced take-over, and ensure the safety of stress steering.

Planning the Emergency Collision Avoidance Strategy Based on Personal Zones for Safe Human‐Machine Interaction in Smart Cyber‐Physical System

Human contact is a key issue in social interactions for autonomous systems since robots are increasingly appearing everywhere, which has led to a higher risk of conflict. Particularly in the real world, collisions between humans and machines may result in catastrophic accidents or damaged goods. In this paper, a novel stop strategy related to autonomous systems is proposed. This control method can eliminate the vibrations produced by a system’s movement by analysing the poles and zeros in the model of autonomous vehicles and goods. Using the pole placement technique, the motion of a system is guaranteed to be more stable, more flexible and smoother. Moreover, several control profiles are employed in the switching mechanism to choose the proper vibration‐free effect. The main contributions of this paper are (i) the recommendation of an active stopping planner using different smooth generators from a modelling study, (ii) the validation of their physical characteristics and (iii) the launching of a switching algorithm based on the socially aware navigation framework of a robot. This theoretical work is based on the virtual environment of MATLAB, and the experiment is implemented in the practical platform of an automated guided vehicle. From these results, it can be seen that the proposed approach is robust, effective and feasible for applications in storehouse management, public transportation or factory manufacturing.

Safe Distance Model for Control of Vehicle Emergency Collision Avoidance

The vehicle needs to be able to assist the driver in realizing the control strategy of emergency collision avoidance to ensure the safety of car drivers during driving. This article introduces the basic principles of vehicle emergency collision avoidance using time to collision (TTC) and Berkeley safety distance models. In addition, the Berkeley model was improved so that it could be adjusted for different driving styles. On the simulation platform, simulation experiments were carried out on the driving conditions of the front vehicle at a standstill state, at a constant speed and at a deceleration state. The performance of three collision avoidance strategies were analysed. The results showed that the extroverted driver had the fastest response speed, followed by the moderate driver and the introverted driver; no matter what the working condition, the increase in the driving speed of the driver could reduce the distance between the vehicle and the vehicle in front when the vehicle stopped due to braking, increasing the risk of rear-end collision; no matter what the working condition, the anti-collision strategy that adopted the improved Berkeley model could effectively adapt to different driving styles, keeping a stable distance from the front vehicle when stopping due to braking and the distance between the vehicles was larger than the other two collision avoidance strategies.

A hierarchical framework of emergency collision avoidance amid surrounding vehicles in highway driving

Collision avoidance is a key issue for high-speed vehicles in emergency situations, particularly as it pertains to surrounding vehicles. Accordingly, a hierarchical framework for human-driven or autonomous vehicles is proposed that ensures the safe operation of vehicles in emergency driving scenarios while considering surrounding vehicles. The developed emergency collision-avoidance system consists of an estimator module, a prediction module, a manoeuvre decision-making module, and a manoeuvre control module. The core module for manoeuvre decision making uses finite state machine (FSM) technology to determine the appropriate manoeuvre for avoiding collisions. In particular, a collision risk model is developed in this module, taking into account risk related to surrounding vehicles in the overlapping region, road adhesion, and stabilization performance of vehicle. In the manoeuvre control module, accounting for safe gaps to surrounding vehicles in an adjacent lane, the longitudinal manoeuvre is generated by a model predictive-control algorithm. A yaw stability controller is designed using lateral tire force estimate-based sliding mode control, accounting for both the collision and vehicle stabilization. To mediate the demands of yaw stabilization and collision risk, particularly for reducing the loss of velocity, an integrated controller with propulsion is developed. Modeling vehicles driving under different road conditions with various road adhesion and surrounding-vehicle settings, the results of experiments in a hardware-in-the-loop (HIL) system have successfully demonstrated the effectiveness of the proposed collision avoidance system.

Emergency Collision Avoidance by Steering in Critical Situations

In this paper, an emergency collision avoidance system is proposed by including not only braking but also steering control actions. The minimum distance to avoid collision is calculated separately for braking and steering based on the relative motion to the surrounding vehicles and the lane information obtained through the vision sensor. For steering avoidance control, an optimal control input is calculated through the model predictive control that satisfies constraints such as safe avoidance region created by surrounding vehicles and capacity of the vehicle actuator. In particular, for avoiding collision by lane changing, the maximum lateral acceleration and the maximum angle of the trajectory are considered. In addition, the abrupt lateral movement in avoidance causes nonlinear characteristics in tires and, thus, tire parameters are estimated through EKF (Extended Kalman Filter) to improve model prediction accuracy. The control intervention time of avoidance maneuvering is determined for braking and steering, respectively. The simulation results demonstrate that the proposed algorithm of integrating AEB (Autonomous Emergency Braking) and AES (Autonomous Emergency Steering) can effectively avoid the collision in critical situations and that the host vehicle can still maintain the safety inside the road boundary.

Integrated Steering and Differential Braking for Emergency Collision Avoidance in Autonomous Vehicles

Controlling the lateral dynamics of an autonomous vehicle confronting a sudden obstacle requires optimal use of tires' force capacities. In these situations, autonomous steering may not be able to respond fast enough to prevent collision or instability. This paper presents an integrated controller for autonomous vehicles, capable of suitably reacting to emergency situations when a sudden obstacle appears on the road. The proposed controller employs differential braking conservatively when needed, to produce an additional yaw moment, thereby improving a vehicle's lateral agility and responsiveness without endangering vehicle stability. A longitudinal controller is also designed to track a desired longitudinal velocity. Model predictive control (MPC) method is used for developing a combined path planning and tracking controller with a hierarchical structure that prioritizes (1) collision avoidance, (2) vehicle stability, and (3) path tracking. The effectiveness of the proposed integrated MPC controller is evaluated by simulating an experimentally validated CarSim model to demonstrate the controller's capability in preventing instability and collisions.

Vehicle path tracking Based on Strong Tracking Cubature Kalman Filter While Encountering An Emergency Collision Avoidance

Considering the high speed emergency avoidance situations, the vehicle path tracking was studied in this paper. The three degrees of freedom nonlinear vehicle model and the cubature Kalman filter are applied to tracking vehicle emergency avoidance path. For the degradation of adaptive tracking performance in the vehicle emergency avoidance, an improved strong tracking cubature Kalman filter algorithm is proposed by introducing the fading factor into filtering process which is learnt from strong tracking filter. The algorithm has a simple implementation, high estimation accuracy and good robustness. Different target paths are selected and the cubature Kalman filter is applied to simulate path tracking in the vehicle emergency avoidance. The simulation results show that the proposed path tracking method can control the vehicle tracking the ideal collision avoidance path rapidly without collisions.

A study of decomposition of a group of ships for preliminary forecasting of dangerous approaching

The conducted studies of emerging situations of dangerous approaching of several ships revealed the existence of classification of ships that are target ships relative to the base ship. These target ships can be dangerous, safe, safe but dangerous under certain conditions. In turn, dangerous that require the maneuver of standard collision avoidance, as well as the targets that require the maneuver of emergency collision avoidance. It is proved that the specified classification of ships can be carried out using the belonging of target ships to different subsets, which depend on their motion parameters relative to the base ship.This made it possible to view a group of several ships as a set of targets.Experimental studies using computer simulations confirmed that decomposition of the ship approaching situation into subsets, each of which is different from the other in relative motion parameters, is possible. These parameters are relative course, bearing, maximum allowable closest point of approach for normal and emergency maneuvering, time until reaching the closest point of approach. The speed ratio between of the base and target ships, the ratio of the closest point of approach and the maximum allowable distance also affect the belonging to a certain subset.This suggests that there is a clear dependence of the collision probability of ships on their belonging to different subsets, which can be calculated for each ship participating in the collision avoidance maneuver. It is shown that when the base ship performs the collision avoidance maneuver, there are changes in relative motion parameters, and hence the belonging of surrounding target ships to different subsets.Thus, there are grounds to argue that using the specified classification of target ships, preliminary forecasted belonging of ships to different subsets can be calculated. The probability of dangerous approaching and/or collision of ships can also be calculated for each course alternation of the base ship relative to each of the surrounding target ships.

Method of Emergency Collision Avoidance for Unmanned Surface Vehicle (USV) Based on Motion Ability Database

Abstract The unmanned surface vehicles (USV) are required to perform a dynamic obstacle avoidance during fulfilling a task. This is essential for USV safety in case of an emergency and such action has been proved to be difficult. However, little research has been done in this area. This study proposes an emergency collision avoidance algorithm for unmanned surface vehicles (USVs) based on a motion ability database. The algorithm is aimed to address the inconsistency of the existing algorithm. It is proposed to avoid collision in emergency situations by sharp turning and treating the collision avoidance process as a part of the turning movement of USV. In addition, the rolling safety and effect of speed reduction during the collision avoidance process are considered. First, a USV motion ability database is established by numerical simulation. The database includes maximum rolling angle, velocity vector, position scalar, and steering time data during the turning process. In emergency collision avoidance planning, the expected steering angle is obtained based on the International Regulations for Preventing Collisions at Sea (COLREGs), and the solution space, with initial velocity and rudder angle taken as independent variables, is determined by combining the steering time and rolling angle data. On the basis of this solution space, the objective function is solved by the particle swarm optimization (PSO) algorithm, and the optimal initial velocity and rudder angle are obtained. The position data corresponding to this solution is the emergency collision avoidance trajectory. Then, the collision avoidance parameters were calculated based on the afore mentioned model of motion. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under emergency situation. Results show the validity of the algorithm. Hence results of this research can be useful for performing intelligent collision avoidance operations of USV and other autonomous ships

A new strategy for rear-end collision avoidance via autonomous steering and differential braking in highway driving

ABSTRACTThis paper proposes a new safety system named emergency steering assist (ESA), which consists of the autonomous steering subsystem and differential braking subsystem. The control system is developed to mediate the conflict objectives of vehicle stabilisation and rear-end collision avoidance in highway driving. Instead of predefining a collision-free trajectory in the entire process of collision avoidance, the process of collision avoidance is divided into two stages. In stage 1, where the vehicle is guided to enter the adjacent lane with a constant centripetal acceleration to avoid collision, the steering manoeuvre is determined by the feedforward controller based on the steering dynamics. In stage 2, where the vehicle is guided along the centreline of the adjacent lane, the steering manoeuvre is determined by the controller based on the theory of model predictive control. In addition, the differential braking subsystem is designed with a comprehensive treatment to the requirements of collision avoidance and vehicle stabilisation. Finally, the simulation results demonstrate that the proposed ESA system can effectively achieve better balance between an emergency collision avoidance manoeuvre and vehicle stabilisation at high speeds in different conditions. The hardware-in-the-loop experiment developed by the authors is used to validate the real-time performance and effectiveness of the proposed control scheme.

Feedback Game-Based Shared Control Scheme Design for Emergency Collision Avoidance: A Fuzzy-Linear Quadratic Regulator Approach

Driver-machine shared control scheme opens up a new frontier for the design of driver assistance system, especially for improving active safety in emergency scenario. However, the driver's stress response to steering assistance and strong tire nonlinearity are main challenges suffered by controller designing for collision avoidance. These unfavorable factors are particularly pronounced during emergency steering maneuvers and sharply degrade shared control performance. This paper proposes a fuzzy-linear quadratic regulator (LQR) game-based control scheme to simultaneously enhance vehicle stability while compensating driver's inappropriate steering reaction in emergency avoidance. A piecewise linear-based Takagi–Sugeno (T–S) fuzzy structure is presented to mimic driver's knowledge about vehicle lateral nonlinearity, and the control authority is shared between driver and emergency steering assistance (ESA) system through steer-by-wire (SBW) assembly. An identical piecewise internal model is chosen for ESA and the shared lane-keeping problem is modeled as a fuzzy linear quadratic (LQ) problem, where the symmetrical fuzzy structure further enhances vehicle's ability to handle extreme driving conditions. In particular, the feedback Stackelberg equilibrium solutions of the fuzzy-LQ problem are derived to describe the interactive steering behavior of both agents, which enables the ESA to compensate driver's irrational steering reaction. Hardware-in-the-loop (HIL) experiment demonstrates the ESA's capability in compensating driver's aggressive steering behavior, as well as the copiloting system's excellent tracking and stabilizing performance in emergency collision avoidance.