Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance:A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model errors.Furthermore,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon safety.In this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon control.To effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV.This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner.Additionally,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain disturbances.We also provide theoretical proof of the string stability under this control framework.Finally,extensive simulations are conducted to validate our approach.The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower bound.Moreover,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Local Path Planning and Tracking Control of Vehicle Collision Avoidance System

Automotive collision avoidance technology can effectively avoid the accidents caused by dangerous traffic conditions or driver's manipulation errors.Moreover,it can promote the development of autonomous driving for intelligent vehicle in intelligent transportation.We present a collision avoidance system,which is composed of an evasive trajectory planner and a path following controller.Considering the stability of the vehicle in the conflict-free process,the evasive trajectory planner is designed by polynomial parametric method and optimized by genetic algorithm.The path following controller is proposed to make the car drive along the designed path by controlling the vehicle's lateral movement.Simulation results show that the vehicle with the proposed controller has good stability in the collision process,and it can ensure the vehicle driving in accordance with the planned trajectory at different speeds.The research results can provide a certain basis for the research and development of automotive collision avoidance technology.

Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance(MVCA)algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently,without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore,MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay(< 100100 ms) and low packet loss(< 5%) can bring little influence to those trajectory planning algorithms that only depend on V2 V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.

Radar-Based Collision Avoidance for Unmanned Surface Vehicles'

Unmanned surface vehicles （USVs） have become a focus of research because of their extensive applications. To ensure safety and reliability and to perform complex tasks autonomously, USVs are required to possess accurate perception of the environment and effective collision avoidance capabilities. To achieve these, investigation into real- time marine radar target detection and autonomous collision avoidance technologies is required, aiming at solving the problems of noise jamming, uneven brightness, target loss, and blind areas in marine radar images. These technologies should also satisfy the requirements of real-time and reliability related to high navigation speeds of USVs. Therefore, this study developed an embedded collision avoidance system based on the marine radar, investigated a highly real-time target detection method which contains adaptive smoothing algorithm and robust segmentation algorithm, developed a stable and reliable dynamic local environment model to ensure the safety of USV navigation, and constructed a collision avoidance algorithm based on velocity obstacle （V-obstacle） which adjusts the USV＇s heading and speed in real-time. Sea trials results in multi-obstacle avoidance firstly demonstrate the effectiveness and efficiency of the proposed avoidance system, and then verify its great adaptability and relative stability when a USV sailing in a real and complex marine environment. The obtained results will improve the intelligent level of USV and guarantee the safety of USV independent sailing.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Application of GA, PSO, and ACO Algorithms to Path Planning of Autonomous Underwater Vehicles

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Hierarchical CNNPID Based Active Steering Control Method for Intelligent Vehicle Facing Emergency Lane-Changing

To resolve the response delay and overshoot problems of intelligent vehicles facing emergency lane-changing due to proportional-integral-differential(PID)parameter variation,an active steering control method based on Convolutional Neural Network and PID(CNNPID)algorithm is constructed.First,a steering control model based on normal distribution probability function,steady constant radius steering,and instantaneous lane-change-based active for straight and curved roads is established.Second,based on the active steering control model,a three-dimensional constraint-based fifth-order polynomial equation lane-change path is designed to address the stability problem with supersaturation and sideslip due to emergency lane changing.In addition,a hierarchical CNNPID Controller is constructed which includes two layers to avoid collisions facing emergency lane changing,namely,the lane change path tracking PID control layer and the CNN control performance optimization layer.The scaled conjugate gradient backpropagation-based forward propagation control law is designed to optimize the PID control performance based on input parameters,and the elastic backpropagation-based module is adopted for weight correction.Finally,comparison studies and simulation/real vehicle test results are presented to demonstrate the effectiveness,significance,and advantages of the proposed controller.

A review on COLREGs-compliant navigation of autonomous surface vehicles:From traditional to learning-based approaches

A growing interest in developing autonomous surface vehicles(ASVs)has been witnessed during the past two decades,including COLREGs-compliant navigation to ensure safe autonomy of ASVs operating in complex waterways.This paper reviews the recent progress in COLREGs-compliant navigation of ASVs from traditional to learning-based approaches.It features a holistic viewpoint of ASV safe navigation,namely from collision detection to decision making and then to path replanning.The existing methods in all these three stages are classified according to various criteria.An in-time overview of the recently-developed learning-based methods in motion prediction and path replanning is provided,with a discussion on ASV navigation scenarios and tasks where learning-based methods may be needed.Finally,more general challenges and future directions of ASV navigation are highlighted.

Collision Avoidance Strategy Supported by LTE-V-Based Vehicle Automation and Communication Systems for Car Following

We analyzed and improved a collision avoidance strategy, which was supported by Long Term EvolutionVehicle(LTE-V)-based Vehicle-to-Vehicle(V2 V) communication, for automated vehicles. This work was completed in two steps. In the first step, we analyzed the probability distribution of message transmission time, which was conditional on transmission distance and vehicle density. Our analysis revealed that transmission time exhibited a near-linear increase with distance and density. We also quantified the trade-off between high/low resource reselection probabilities to improve the setting of media access parameters. In the second step, we studied the required safety distance in accordance with the response time, i.e., the transmission time, derived on the basis of a novel concept of Responsibility-Sensitive Safety(RSS). We improved the strategy by considering the uncertainty of response time and its dependence on vehicle distance and density. We performed theoretical analysis and numerical testing to illustrate the effectiveness of the improved robust RSS strategy. Our results enhance the practicability of building driverless highways with special lanes reserved for the exclusive use of LTE-V vehicles.

Structured road-oriented motion planning and tracking framework for active collision avoidance of autonomous vehicles

This paper proposes a novel motion planning and tracking framework based on improved artificial potential fields(APFs) and a lane change strategy to enhance the performance of the active collision avoidance systems of autonomous vehicles on structured roads. First, an improved APF-based hazard evaluation module, which is inspired by discrete optimization, is established to describe driving hazards in the Frenet-Serret coordinate. Next, a strategy for changing lane is developed in accordance with the characteristics of the gradient descent method(GDM). On the basis of the potential energy distribution of the target obstacle and road boundaries, GDM is utilized to generate the path for changing lane. In consideration of the safety threats of traffic participants, the effects of other obstacles on safety are taken as additional safety constraints when the lane-changing speed profile for ego vehicles is designed. Then, after being mapped into the Cartesian coordinate, the feasible trajectory is sent to the tracking layer, where a proportional-integral control and model predictive control(PI-MPC) based coordinated controller is applied. Lastly, several cases composed of different road geometrics and obstacles are tested to validate the effectiveness of the proposed algorithm. Results illustrate that the proposed algorithm can achieve active collision avoidance in complex traffic scenarios.

Self-learning drift control of automated vehicles beyond handling limit after rear-end collision

Vehicles involved in traffic accidents generally experience divergent vehicle motion,which causes severe damage.This paper presents a self-learning drift-control method for the purpose of stabilizing a vehicle’s yaw motions after a high-speed rear-end collision.The struck vehicle generally experiences substantial drifting and/or spinning after the collision,which is beyond the handling limit and difficult to control.Drift control of the struck vehicle along the original lane was investigated.The rear-end collision was treated as a set of impact forces,and the three-dimensional non-linear dynamic responses of the vehicle were considered in the drift control.A multi-layer perception neural network was trained as a deterministic control policy using the actor-critic reinforcement learning framework.The control policy was iteratively updated,initiating from a random parameterized policy.The results show that the self-learning controller gained the ability to eliminate unstable vehicle motion after data-driven training of about 60,000 iterations.The controlled struck vehicle was also able to drift back to its original lane in a variety of rear-end collision scenarios,which could significantly reduce the risk of a second collision in traffic.

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

自动驾驶汽车避撞极限研究

精确计算不同避撞控制策略的极限避撞距离是自动驾驶汽车避撞决策与控制的基础。为厘清差动制动控制对避撞距离的影响,探究转向和差动制动集成控制的极限避撞能力,提高极限避撞距离的计算精度,本研究基于非线性车辆集成动力学和最优控制理论提出一种自动驾驶汽车极限避撞距离计算方法。首先,建立了非线性7自由度车辆动力学模型和复合滑移工况的Pacejka轮胎模型。进一步地,基于上述模型构建了极限避撞距离求解问题,并将其转化为最优控制问题。然后,设计了高斯伪谱法将最优控制问题转化为非线性规划问题并求解。最后,分析了转向控制、制动控制、转向和制动集成控制、转向和差动制动集成控制的极限避撞距离,并与基于质点模型计算和CarSim测试的结果进行了对比。结果表明:转向和差动制动集成控制能够进一步减少自动驾驶汽车的避撞距离,显著提高其避撞能力;本研究所提方法能够显著提高极限避撞距离的计算精度和避撞决策结果的可靠性。

周车轨迹预测不确定性智能车避撞策略研究

提出了一种基于周车轨迹预测不确定性的智能汽车避撞策略研究方法。轨迹预测模块,将基于物理的轨迹预测模型和数据驱动模型相结合构建物理引导的轨迹预测模型(PG-LSTM),模型输出关于周车预测轨迹的二维高斯分布参数,以表征驾驶员行为的不确定性;风险评估及避撞策略模块,结合轨迹预测模型的输出结果,提出一个新的风险度量--预测驾驶风险PDR和预测相对驾驶风险指数PRDRI作为评估未来风险的参考指标,建立紧急工况下避撞决策机制。通过Carsim搭建复杂紧急工况场景进行仿真实验。仿真结果表明:所提出的驾驶风险评估模型可以准确地辨识复杂行车场景未来驾驶风险,同时基于驾驶风险所提出的避撞决策机制能够提升智能汽车的避撞安全性。

一种智能汽车驾驶过程避碰控制算法的研究

针对智能汽车驾驶系统,提出了一种集成路径跟踪、碰撞避免和碰撞缓解控制算法。该算法考虑了轮胎联合滑移力,通过监控每个轮胎的滑移情况,实现主动前转向和制动调节的控制,以提高极端条件下车辆行驶过程中的稳定性和跟踪精度。设计了不依赖于单独路径生成模块的新型切换机构,用于避碰和缓解阶段,并在各种恶劣的驾驶条件下进行了验证。仿真结果表明,该算法不仅能在正常行驶阶段跟踪期望路径,而且能在保证车辆稳定性的前提下避免碰撞,降低碰撞严重程度。

基于自适应权重MPC的AEB-P控制策略研究

为进一步优化面向行人的汽车自动紧急制动系统(AEB-P)控制算法,提出了一种综合考虑驾驶舒适性和行人损伤风险的AEB-P分层控制策略。针对C-NCAP的AEB-P评价标准,设计了考虑制动时驾驶员舒适感的制动安全距离模型;通过引入模糊规则综合考虑行人损伤风险和场景工况得到权重系数调整策略,并基于此设计自适应权重系数MPC上层控制器,采用PID下层控制器对自车实际减速度进行修正;建立车辆纵向动力学模型并通过CarSim与Matlab/Simulink搭建测试场景和控制算法,通过硬件在环实验对本文方法和固定TTC阈值算法进行对比。结果表明,所提控制算法能够在93.75%的测试工况中有效避撞,而固定TTC阈值算法避障成功率仅有43.75%。相较于传统控制策略,该方法能使自车和前方行人保持较稳定的最小间距,鲁棒性更好,可为AEB-P控制理论提供参考依据。

基于深度强化学习的无人机集群数字孪生编队避障

无人机集群在各个领域中扮演着重要角色,具有丰富的应用场景.然而,将深度强化学习方法应用于自主无人机面临着诸多严峻挑战.本文基于多智能体深度强化学习,通过使用局部信息建立单个无人机的状态空间,并使用多智能体近端策略优化(Multi-agent proximal policy optimization,MAPPO)的在线策略算法来训练策略网络,从而克服了环境的不确定性和对全局信息的依赖.同时,引入了数字孪生的概念,为资源紧张型算法提供了新思路.为了解决采样困难和资源紧张的问题,基于数字孪生技术,构建了一个用于无人机编队避障策略模型训练的架构.首先,构建了多个数字孪生环境,用于强化学习算法在任务开始之前进行交互采样的预训练,以使集群具备基本的任务能力.然后,使用在真实环境中采集的数据进行补充训练,使得集群能够更好地完成任务.对采用这种两阶段训练架构的效果进行了对比,同时与其他策略算法进行比较,验证了MAPPO的样本效率性能.最后,设计了实际飞行验证测试,验证了从孪生环境中获得的策略模型的实用性和可靠性.

基于改进人工势场法的多无人艇避障策略

针对多无人艇编队避障问题,对静态避障的路径消耗问题进行建模分析,在动态避障时提出一种偏置人工势场法使策略符合艇群国际海上避碰规则(swarm International Regulations for Preventing Collisions at Sea,sCOLREGS)。本方法首先对传统人工势场法进行改进,定义符合艇群会遇态势判断需求的sCOLREGS,通过速度障碍法实时判断碰撞风险,然后利用偏置斥力区域的改进人工势场法实现对规则的遵守。仿真实验表明,本文方法在障碍物与编队大小相当时可显著减少避障路程,在确保避障实时性的同时,较好地遵守了国际海上避碰规则相关条例。研究结论可为海面无人艇集群安全航行提供参考。

基于系统物理参数测量和几何关系的车辆定位

对采用可见光通信(Visible Light Communication,VLC)的车辆定位方法进行了研究,研究、分析并比较了4种基于VLC的车辆定位方法;基于假设的系统模型和接收到的VLC信号数学模型,分析了每种方法所采用的TX位置与系统物理参数的测量过程,利用这些参数与TX位置之间的几何关系构成一个观测模型,获得车辆位置估计;基于VLC定位方法的观测模型得到每种方法关于位置精度的Cramer-Rao下界(Cramer-Rao Lower Bound,CRLB);在一般有限传播延迟、视距(Line of Sight,LoS)和加性高斯白噪声(Additive White Gaussian Noise,AWGN)的VLC信道模型下,对于真实道路的避碰和列队行驶场景,仿真了每种方法的系统物理参数测量,并基于测量结果,对每种方法的定位精度的CRLB进行了评价。

Path planning and stability control of collision avoidance system based on active front steering

Vehicle collision avoidance system is a kind of auxiliary driving system based on vehicle active safety,which can assist the driver to take the initiative to avoid obstacles under certain conditions,so as to effectively improve the driving safety of vehicle.This paper presents a collision avoidance system for an autonomous vehicle based on an active front steering,which mainly consists of a path planner and a robust tracking controller.A path planner is designed based on polynomial parameterization optimized by simulated annealing algorithm,which plans an evasive trajectory to bypass the obstacle and avoid crashes.The dynamic models of the AFS system,vehicle as well as the driver model are established,and based on these,a robust tracking controller is proposed,which controls the system to resist external disturbances and work in accordance with the planning trajectory.The proposed collision avoidance system is testified through CarSim and Simulink combined simulation platform.The simulation results show that it can effectively track the planning trajectory,and improve the steering stability and anti-interference performance of the vehicle.