Vehicle Collision Avoidance Research Articles

Notice of Violation of IEEE Publication Principles: Sequential Convex Programming for Collaboration of Connected and Automated Vehicles

Notice of Violation of IEEE Publication Principles<BR><BR>"Sequential Convex Programming for Collaboration of Connected and Automated Vehicles," <BR>by Xiaoxue Zhang, Jun Ma, Zilong Cheng, Frank L. Lewis, and Tong Heng Lee, <BR>in IEEE Transactions on Intelligent Vehicles, Early Access <BR><BR>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <BR><BR>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <BR><BR>"Networked Model Predictive Control for Vehicle Collision Avoidance" <BR> by Bassam Alrifaee, <BR>in RWTH Aachen University, Dissertation, 04 May 2017 <BR>BR>"Sequential convex programming MPC for dynamic vehicle collision avoidance" <BR>by Bassam Alrifaee, Janis Maczijewski, Dirk Abel, <BR>in the Proceedings of the IEEE Conference on Control Technology and Applications, 2017, pp. 2202-2207 <BR><BR> <br/> This paper investigates the collaboration of multiple connected and automated vehicles (CAVs) in different scenarios. In general, the collaboration of CAVs can be formulated as a nonlinear and nonconvex MPC problem. Most of the existing approaches available for utilization to solve such an optimization problem suffer from the drawback of considerable computational burden, which hinders the practical implementation in real time. This paper proposes the use of SCP, which is a powerful approach to solving the nonlinear and nonconvex MPC problem in real time. To appropriately deploy the methodology, as a first stage, SCP requires linearization and discretization when addressing the nonlinear dynamics of the system model adequately. Based on the linearization and discretization, the original MPC problem can be transformed into a QCQP problem. Besides, SCP also involves convexification to handle the associated nonconvex constraints. Thus, the nonconvex QCQP can be reduced to a quadratic programming (QP) problem that can be solved rather quickly. Therefore, the computational efficiency is suitably improved despite the existence of nonlinear and nonconvex characteristics, whereby the implementation is realized in real time. Furthermore, simulation results in three different scenarios of autonomous driving are presented to validate the effectiveness and efficiency of our proposed approach.

Collision Avoidance and Path Point Tracking Control for Underactuated Unmanned Surface Vehicles With Unknown Model Nonlinearity

Collision Avoidance and Path Point Tracking Control for Underactuated Unmanned Surface Vehicles With Unknown Model Nonlinearity

Efficient collision avoidance for autonomous vehicles in polygonal domains

Efficient collision avoidance for autonomous vehicles in polygonal domains

Collision Avoidance for Autonomous Vehicles Based on MPC With Adaptive APF

Collision Avoidance for Autonomous Vehicles Based on MPC With Adaptive APF

Adaptive Neuroevolution With Genetic Operator Control and Two-Way Complexity Variation

Topology and weight evolving artificial neural network (TWEANN) algorithms optimize the structure and weights of artificial neural networks (ANNs) simultaneously. The resulting networks are typically used as policy models for solving control and reinforcement learning (RL) type problems. This paper presents a neuroevolution algorithm that aims to address the typical stagnation and sluggish convergence issues present in other neuroevolution algorithms. These issues are often caused by inadequacies in population diversity preservation, exploration/exploitation balance, and search flexibility. This new algorithm, called the Adaptive Genomic Evolution of Neural-Network Topologies (AGENT), builds on the neuroevolution of augmenting topologies (NEAT) concept. Novel mechanisms for adapting the selection and mutation operations are proposed to favorably control population diversity and exploration/exploitation balance. The former is founded on a fundamentally new way of quantifying diversity by taking a graph-theoretic perspective of the population of genomes and inter-genomic differences. Further advancements to the NEAT paradigm occur through the incorporation of variable neuronal properties and new mutation operations that uniquely allow both the growth and pruning of ANN topologies during evolution. Numerical experiments with benchmark control problems adopted from the OpenAI Gym illustrate the competitive performance of AGENT against standard RL methods and adaptive HyperNEAT, and superiority over the original NEAT algorithm. Further parametric analysis provides key insights into the impact of the new features in AGENT. This is followed by evaluation on an unmanned aerial vehicle collision avoidance problem where maneuver planning models are learnt by AGENT with 33% reward improvement over 15 generations.

Integrated Longitudinal and Lateral Control of Emergency Collision Avoidance for Intelligent Vehicles under Curved Road Conditions

The operation of the automatic emergency braking (AEB) system may lead to a significant increase in lateral offset of vehicles in curved road conditions, which can pose a potential risk of collisions with vehicles in adjacent lanes or road edges. In order to address this issue, this study proposes an integrated longitudinal and lateral control strategy for collision avoidance during emergency braking, which utilizes a control algorithm based on Time to Collision (TTC) for longitudinal control and a control algorithm based on yaw angle and preview point lateral deviation for lateral control. On one hand, the AEB system facilitates proactive longitudinal intervention to prevent collisions in the forward direction. On the other hand, the Lane Keeping Assist (LKA) system allows for lateral intervention, reducing the lateral offset of the vehicle during braking. To evaluate the effectiveness of this integrated control strategy, a collaborative simulation model involving Matlab/Simulink, PreScan, and CarSim is constructed. Under typical curved road conditions, comparative simulations are conducted among three different control systems: ➀ AEB control system alone; ➁ independent control system of AEB and LKA; and ➂ integrated control system of AEB and LKA. The results indicate that although all three control systems are effective in preventing longitudinal rear-end collisions, the integrated control system outperforms the other two control systems significantly in suppressing the vehicle’s lateral offset. In the scenario with a curve radius of 60 m and an initial vehicle speed of 60 km/h, System ➀ exhibits a lateral offset from the lane centerline reaching up to 1.72 m. In contrast, Systems ➁ and ➂ demonstrate significant improvements with lateral offsets of 0.29 m and 0.21 m, respectively.

COLREGs-compliant unmanned surface vehicles collision avoidance based on improved differential evolution algorithm

Unmanned surface vessel (USV) has a wide range of applications in oceanographic research, resource development, environment detection, and security rescue due to its advantages of maneuverability, flexibility, fast response, and intelligence. The ability of USVs to autonomously and effectively avoid obstacles in highly dynamic and uncertain marine environments is a prerequisite for the successful completion of their tasks. Therefore, in this article, a USV collision avoidance based on International Regulations for Preventing Collisions at Sea and the Collision Risk Model with the Improved Differential Evolution Algorithm (CRI-DE) has been considered. Based on the International Regulations for Preventing Collisions at Sea (COLREGs) and common practices of seafarers, an improved ship collision risk model is proposed. Specifically, the model is innovatively combined with the differential evolution algorithm (DE) as a constraint condition to further realize path planning in complex situations. Moreover, chaotic multi-population parallel optimization, parameter adaptive adjustment strategy, and the construction of fitness function based on individual path points are added to the DE. In this way, the ability to escape from local optima and enrich population diversity can be guaranteed. Finally, experiments based on the proposed CRI-DE are conducted and the results indicate the efficiency and effectiveness of the proposed method.

A novel intelligent collision avoidance algorithm based on deep reinforcement learning approach for USV

Enhancing the efficiency of unmanned surface vehicles (USVs) collision avoidance can yield a significant impact, as it can result in safer navigation and lower energy consumption. This paper introduces a robust approach employing deep reinforcement learning theory to facilitate informed collision avoidance decisions within intricate maritime environments. The restrictions on USV maneuverability and international regulations for preventing collisions at sea are studied and quantified, particularly focusing on the shape and size changes of the ship’s domain caused by USV speed. Based on the deep Q network, an improved methodology is designed, incorporating a noisy network, prioritized experience replay, dueling neural network architecture, and double Q learning, resulting in a highly efficient sampling, exploration, and learning process. To curtail computational expenses associated with USVs, a novel dynamic area restriction technique is proposed. Furthermore, an innovative USV state clipping method is introduced to mitigate training complexities. By utilizing the Unity platform, a virtual environment characterized by complexity and stochasticity is constructed for training and testing the collision avoidance of USVs This novel approach surpasses the performance of the pre-improvement algorithm across multiple collision avoidance effectiveness indicators and performance metrics.

Research on the High-Speed Collision Avoidance Method of Distributed Drive Electric Vehicles

High-speed autonomous emergency collision avoidance for intelligent vehicles is a challenging task. Many existing studies have not considered the effect of the road adhesion coefficient in the vehicle high-speed collision avoidance planning layer. Additionally, in lateral control, the effect of strong coupling of vehicle motion on the system model is not considered. This effect increases the uncertainty in the collision avoidance process and correlates with driving safety. In this regard, this paper conducted relevant studies. First, a distributed drive electric vehicle (DDEV) with controlled longitudinal speed was selected as the research platform and derived the transformed square-root cubature Kalman filter (T-SCKF) algorithm with cubature point sampling rule transformation to estimate the road adhesion coefficient. Second, the road adhesion coefficient information was used to calibrate the following vehicle safety distance model and the lane change collision avoidance lateral acceleration information was used to determine the vehicle autonomous emergency collision avoidance trajectory and to optimize the planning decision of the vehicle collision avoidance process. Last, the DDEV was laterally controlled to complete the tracking of the high-speed emergency collision avoidance trajectory by the adaptive model predictive control (Adaptive MPC) method with real-time updating of the system model. The relevant validation shows that the T-SCKF algorithm proposed in this paper can effectively estimate the road adhesion coefficient. The tracking control of the high-speed collision avoidance trajectory by the adaptive MPC method has better tracking accuracy and stability than traditional MPC and linear quadratic regulator (LQR) methods.

A comprehensive survey on non‐cooperative collision avoidance for micro aerial vehicles: Sensing and obstacle detection

AbstractIn recent years, unmanned aerial vehicles (UAVs) have been confirmed as a powerful tool for countless applications in nearly every industry, in which collision avoidance plays a vital role for micro aerial vehicles (MAVs) performing autonomous missions. The complexity of collision avoidance systems is directly related to the complexity of the navigation environment, the avoidance of highly uncertain obstacles being a more thoroughly researched critical aspect. The non‐cooperative collision avoidance strategy is necessary when communication between the agents is unavailable, under the case with many existing passive obstacles and non‐cooperative flying objects. In this article, we review the topic of non‐cooperative collision avoidance for MAVs in dynamic environments by covering two main issues: onboard sensing and obstacle description algorithms. A detailed discussion of these two topics is provided to face non‐cooperative collision avoidance technology for MAVs. Finally, the paper summarizes the open challenges and foreseen future directions in this field.

Autonomous Collision Avoidance of Unmanned Surface Vehicles Based on Improved A-Star and Dynamic Window Approach Algorithms

Unmanned surface vessel (USV) autonomous navigation on the open sea involving real-time path planning and collision avoidance is still one of the essential problems to ensure the USV’s safe and efficient navigation. Especially in a congested and uncertain marine traffic environment, not only will static obstacles be taken into account but other target vessels in motion should also be considered. Also, the general requirement of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) should be satisfied. Hence, an improved A-star algorithm for USV path planning and improved dynamic window approach (IDWA) for collision avoidance were proposed. First, considering the requirement of COLREGs, the velocity search space was filtered again, and the quantity of USV trajectories was reduced. Then, the improved A-star algorithm was introduced to let the USV avoid static obstacles and reach its destination without trapping in local optimization. Moreover the Deep Q-network method was utilized to train weight coefficients of the IDWA objective function. Thereby, the improved algorithm-generated path during the process of collision avoidance was more reasonable and safer. To verify feasibility of the proposed path-planning algorithm, a comparison experiment with the traditional DWA method was carried out. The results showed that whether it was for a single USV to a single target or for multiple USVs to multiple targets, path planning, the proposed method, could work effectively to avoid obstacles safely and reach the destination quickly. The improved algorithm will be expected to provide a reference for USV path planning and collision avoidance as well as contribute to the implementation of autonomous ship navigation.

A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

Most of the existing research in the field of autonomous vehicles (AVs) addresses decision making, planning and control as separate factors which may affect AV performance in complex driving environments. A hierarchical framework is proposed in this paper to address the problem mentioned above in environments with multiple lanes and surrounding vehicles. Firstly, high-level decision making is implemented by a finite-state machine (FSM), according to the relative relationship between the ego vehicle (EV) and the surrounding vehicles. After the decision is made, a cluster of quintic polynomial equations is established to generate the path connecting the initial position to the candidate target positions, according to the traffic states of the EV and the target vehicle. The optimal path is selected from the cluster, based on the quadratic programming (QP) framework. Then, the speed profile is generated, based on the longitudinal displacement–time graph (S–T graph), considering the vehicle motion state constraints and collision avoidance. The smoothed speed profile is created through another QP formulation, in the space created by the dynamic-programming (DP) method. Finally, the planned path and speed profile are combined and sent to the lower level control module, which consists of the linear quadratic regulator (LQR) for lateral trajectory tracking control, and a double PID controller for longitudinal control. The performance of the proposed framework was validated in a co-simulation environment using PreScan, MATLAB/Simulink and CarSim, and the results demonstrate that the proposed framework is capable of addressing most ordinary scenarios on a structured road, with reasonable decisions and controlling abilities.

A Novel Prediction Algorithm of Pedestrian Activity Region for Intelligent Vehicle Collision Avoidance System

With the development of intelligent vehicle technology, pedestrian trajectory prediction techniques play a key role in collision avoidance systems. Activity regions are commonly adopted to cover the uncertainty of predicted trajectories. However, setting a rational region size for each pedestrian is a challenging issue. Excessive region size may lead to over-reactions of vehicles and limit the degree of freedom of the path planning while insufficient region size may fail to cover the uncertainty. This work proposes a novel dynamic activity region (DAR) prediction algorithm and a relevant collision risk evaluation method. In the prediction module, the proposed method predicts a rational activity region for each pedestrian based on the historical trajectory in contrast to the conventional methods which adopt preset static activity regions (SAR) for all pedestrians. The comparative study shows that the proposed method reduces the sizes of pedestrian activity regions by 10.5% on average with similar precision. In the cases where the trajectory is highly nonlinear, the region size reduction can reach 12.1%. In the collision risk evaluation module, the risk statuses of pedestrians are classified based on the relationship between their DARs and the future trajectory of the vehicle. This method can produce a more detailed classification. The effectiveness and improvement of the proposed method are validated by experiments and simulations.

Proximal policy optimization with reciprocal velocity obstacle based collision avoidance path planning for multi-unmanned surface vehicles

The challenge of solving the collision avoidance path planning problem lies in adaptively selecting the optimal agent velocity in complex scenarios full of reciprocal obstacles. To achieve autonomous collision avoidance for unmanned surface vehicles (USVs), a distributed multi-USVs navigation method based on deep reinforcement learning (DRL) is proposed, which combines the concept of reciprocal velocity obstacle (RVO) with a DRL scheme to solve the collision avoidance path planning problem with limited information. The collision avoidance behavior in USV navigation is based on the International Regulations for Preventing Collisions at Sea, and to obtain an improved proximal policy optimization (PPO) algorithm, the RVO algorithm is used to improve the action space and reward function of the PPO algorithm. Gate Recurrent Unit-based neural network is used to map the states of different number of surrounding obstacles directly into actions. The effectiveness of the method in various situations was verified through simulation experiments. It is also shown that the algorithm can accurately determine collision situations, give reasonable collision avoidance behaviors, and achieve effective collision avoidance in complex environments with dynamic and static obstacles. The research can provide a theoretical basis and methodological reference for USV autonomous navigation.

Risk Factor Attitude Survey and Step of Road Detection Method About Wheelchair of Elderly Person

We propose a step-detection system to improve the safety of electric wheelchairs for the elderly. In automobile driving support systems, roads and other environments are being improved, and collision detection and avoidance for vehicles and other pedestrians are being studied and prioritized. However, in the driving environment of electric wheelchairs, it is important to deal with bumps and one-way slopes on the road. In this study, we conducted a survey on elderly people’s awareness of risk factors related to wheelchairs and categorized these risk factors. In addition, we confirmed the usefulness of the step-detecting device by conducting a driving experiment.

Fuzzy Logic Approaches for Pedestrian Collision Avoidance in Intelligent Vehicles

Fuzzy Logic Approaches for Pedestrian Collision Avoidance in Intelligent Vehicles

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

Active Braking Strategy Considering VRU Motion States in Curved Road Conditions

Currently, the active braking system is primarily used to avoid collisions between vehicles and vulnerable road users (VRUs) traversing the road at a constant speed in straight road conditions. Research on the collision avoidance strategy has been conducted to enhance the protective effects of the active braking system for VRUs traversing the road in various motion states in curved road conditions. Firstly, the spatial position relationships between VRUs and turn-taking vehicles are established when VRUs traverse the road in various motion states from the outside or the inside of the curved road; a mathematical model is established to identify whether VRUs are in a dangerous condition. Secondly, the safe distance model is established to formulate the vehicle collision avoidance strategy. Thirdly, the active braking controller is designed based on the upper sliding mode control and the lower discrete PID control. Finally, the six collision test scenarios on the curved road are constructed by utilizing the Prescan and Matlab/Simulink software. The results show that the active braking system can avoid the collision between the vehicle and VRUs traversing the curved road in various motion states.

Research on Active Collision Avoidance and Hysteresis Reduction of Intelligent Vehicle Based on Multi-Agent Coordinated Control System

This paper provides a multi-agent coordinated control system to improve the real-time performance of intelligent vehicle active collision avoidance. At first, the functions and characteristics of longitudinal and lateral collision avoidance agents are analyzed, which are the main components of the multi-agent. Then, a coordinated solution mechanism of an intelligent vehicle collision avoidance system is established based on hierarchical control and blackboard model methods to provide a reasonable way to avoid collision in complex situations. The multi-agent coordinated control system can handle the conflict between the decisions of different agents according to the rules. Comparing with existing control strategies, the proposed system can realize multi decisions and planning at the same time; thus, it will reduce the operation time lag during active collision avoidance. Additionally, fuzzy sliding mode control theory is introduced to guarantee accurate path tracking in lateral collision avoidance. Finally, co-simulation of Carsim and Simulink are taken, and the results show that the real-time behavior of intelligent vehicle collision avoidance can be improved by 25% through the system proposed.

Integrating Both Routing and Scheduling Into Motion Planner for Multivehicle System

In multi-automated guided vehicle (AGV) control, optimization and collision avoidance are two of the key issues. To deal with these problems of the AGV fleet, motion planning is a good solution. This method usually comprises two steps as follows: routing and scheduling that are always separately executed in conventional routine. This scheme still exists some drawbacks, such as limitation of candidate paths or lack of flexibility in handling collisions. Besides, with a specific layout, the algorithm needs to be modified to be proper with that application. The warehouse with grid-based layout employed popularly in logistics and supply chain is our concern. To overcome this theme, a time-frame-based routing and scheduling (TFRS) algorithm for motion planning of vehicles is proposed for this warehouse application. In detail, TFRS can also be called an enhanced Dijkstra’s algorithm (EDA) with adaptive weights for every segment and node. It was designed to gain several benefits of time due to the shortest path, free collision, and proper for chessboard layout. The main idea is that while conducting path routing, certain circumstances of potential accidents are detected and dealt by scheduling in every loop. Due to simultaneous policies of routing and scheduling, the optimization and secure operation could be achieved in the AGV system. Numerous situations in danger of collision are experimented to verify the effectiveness, flexibility, and correctness of the proposed algorithm.