Cooperative vehicle path generation during merging using model predictive control with real-time optimization

This paper proposed a cooperative merging path generation method for vehicles to merge smoothly on the motorway using a Model Predictive Control (MPC) scheme which optimizes the motions of the relevant vehicles simultaneously. The cooperative merging is a merging in where the most relevant vehicle in the main lane would accelerate or decelerate slightly to let the merging vehicle merge in easily. The proposed path generation algorithm can generate the merging path ensuring the merging vehicle can access the whole acceleration area, and do not exceed it. We have introduced a state variable to the optimization problem by which the merging point for the merging vehicle is optimized. The simulation results showed that the cooperative merging path can be successfully generated under some typical traffic situations without re-adjustment of the optimization parameters.     

Receding horizon maneuver generation for automated highway driving

This paper focuses on the problem of decision-making and control in an autonomous driving application for highways. By considering the decision-making and control problem as an obstacle avoidance path planning problem, the paper proposes a novel approach to path planning, which exploits the structured environment of one-way roads. As such, the obstacle avoidance path planning problem is formulated as a convex optimization problem within a receding horizon control framework, as the minimization of the deviation from a desired velocity and lane, subject to a set of constraints introduced to avoid collision with surrounding vehicles, stay within the road boundaries, and abide the physical limitations of the vehicle dynamics. The ability of the proposed approach to generate appropriate traffic dependent maneuvers is demonstrated in simulations concerning traffic scenarios on a two-lane, one-way road with one and two surrounding vehicles.     

Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

The introduction of proportional-integral-derivative (PID) controllers into cooperative collision avoidance systems (CCASs) has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization (PSO) algorithm, and to better manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed (≤50 km/h) and high speed (≥100 km/h) cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.     

交通信息物理系统中的车辆协同运行优化调度

运输成本及温室气体的排放是衡量智能交通系统的重要指标，有效的运输调度可以降低运输成本和环境损害.针对路网中集成环保型货车的运输问题，本文提出一种基于交通信息物理系统（Transportation cyber physical system，TCPS）的大规模车辆协同调度及合并方案，以最大限度地降低运输成本和碳排放量.首先，采用局部调度策略，结合领队车辆选择算法及聚类分析，构建可合并车辆集合；然后，通过数学规划方法，实现每个车队集合中车辆路径与速度的改进优化处理；最后，通过突发情况的简易处理说明本文调度策略的可扩展性.仿真实验表明，用本文方法将车辆编组合并成车队行驶，较固定路径合并策略可显著降低路网中货运车辆的整体油耗.     

Parallel and diagonal parking in nonholonomic autonomous vehicles

This paper considers the problem of parallel and diagonal parking in wheeled vehicles. A method to plan in real-time a set of collision-free manoeuvres is presented. Artificial intelligent techniques, namely fuzzy logic, play an important role in the practical application of the method. Thus, a fuzzy system is used to select the most suitable manoeuvre from the solution set according with the environment, dealing with optimality, path tracking performance and collision avoidance trade-off. This technique has been implemented in a fuzzy behaviour-based control architecture combining planning and reactivity. The efficiency of the proposed method is demonstrated using the nonholonomic mobile robot ROMEO-3R designed and built at the University of Seville.     

Two-stage on-board optimization of merging velocity planning with energy management for HEVs

This paper proposes a two-stage hierarchy control system with model predictive control (MPC) for connected parallel HEVs with available traffic information. In the first stage, a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging. After formulating the merging problem into a nonlinear optimization problem, a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road, respectively. The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage. In the second stage, an energy management strategy (EMS) for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency. The formulated nonlinear optimization problem is solved by sequential quadratic programming (SQP) method under the same receding horizon. The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.     

Maximum-miss aircraft collision avoidance

Aircraft densities in terminal areas increase each year, and the risk of collision grows proportionally. The maintenance of clearance between aircraft in this environment sometimes calls for evasive maneuvers, which depend on the relative position and relative velocity of two aircraft. In this study, small-amplitude maneuvers are found for either or both aircraft in near-miss configurations. Using practical low-order dynamics, individual maneuvers are found that maximize the miss distance. These optimal maneuvers combine longitudinal (speed) and normal (lift) accelerations. The signs of the accelerations of both aircraft depend on their magnitudes. An evasive climb maneuver, for example, becomes a dive maneuver if the acceleration amplitude exceeds a certain value. The maximum-miss maneuvers appear to have practical potential, because they can be determined on-line from estimated position data for both aircraft, without consideration of detailed inertial and aerodynamic properties of the aircraft. Recommended by H. Stalford     

一种基于分类回归树的无人车汇流决策方法

决策规划是无人驾驶技术中的重要环节.由于道路结构变化或障碍物引起的车辆被动换道多采用基于逻辑规则或优化算法的决策方式.本文以通行量为优化目标,提出一种基于分类回归树（Classification and regression tree,CART）的汇流决策方法.依据交通流参数,选择大量具有代表性的车辆汇流场景.对场景中车辆的汇流决策序列进行编码,采用遗传算法搜索使得通行量最大的决策方案.将寻优获得的大量汇流决策序列作为样本,训练分类回归树.选取车辆自身信息及与周围车辆的关系等以描述环境特征,运用分类回归树描述环境特征与决策结果的映射关系,获得一种通行量最优的汇流决策方法.在软件中进行仿真实验,对比既有方法,基于分类回归树的汇流方法能够有效减少汇流行为对车流的扰动,在大流量情形下依旧能保持较高的通行效率.此外,该方法对实际实施中可能存在的环境感知误差,如定位误差,有一定的鲁棒性.     

基于多曲率拟合模型的无人车路径规划研究

针对无人驾驶车辆的局部路径规划问题,提出一种基于多次曲率拟合模型的路径规划算法。鉴于无人车辆需要生成无碰路径的特点,建立了由三次曲率多项式产生的候选路径集,采用四重参数循环法解决了三次曲率多项式参数计算的问题,进而能够根据评价函数从候选路径集中选择出当前环境下的最优路径。所提出的无人车局部路径规划算法经现场多个路口通过性实验证明了有效性。     

基于轮胎状态刚度预测的极限工况路径跟踪控制研究

为解决高速极限工况下自动驾驶车辆紧急避撞时传统路径跟踪控制方法因轮胎力表达不精确导致的路径跟踪失败问题, 提出一种基于轮胎状态刚度预测的模型预测路径跟踪控制方法. 首先, 基于非线性UniTire轮胎模型求解的轮胎状态刚度对非线性轮胎力进行线性化处理. 其次, 基于期望路径信息提出状态刚度预测方法, 实现预测时域内轮胎力的预测和线性化. 最后, MATLAB和CarSim联合仿真实验表明: 所提出的方法能够明显改善高速极限工况下的避撞控制效果.     

On‐line estimation and path planning for multiple vehicles in an uncertain environment

A unified approach to cooperative target tracking and path planning for multiple vehicles is presented. All vehicles, friendly and adversarial, are assumed to be aircraft. Unlike the typical target tracking problem that uses the linear state and nonlinear output dynamics, a set of aircraft nonlinear dynamics is used in this work. Target state information is estimated in order to integrate into a path planning framework. The objective is to fly from a start point to a goal in a highly dynamic, uncertain environment with multiple friendly and adversarial vehicles, without collision. The estimation architecture proposed is consistent with most path planning methods. Here, the path planning approach is based on evolutionary computation technique which is then combined with a nonlinear extended set membership filter in order to demonstrate a unified approach. A cooperative estimation approach among friendly vehicles is shown to improve speed and routing of the path. Copyright © 2004 John Wiley & Sons, Ltd.     

改进人工势场法的移动机器人避障轨迹研究

针对传统人工势场法在多障碍物复杂环境的全局路径规划中出现的目标不可达、易陷入陷阱区域以及局部极小点问题,提出一种简化障碍物预测碰撞人工势场法(simplified obstacles and predict collision of artificial potential field method,SOPC-APF)...     

基于时间窗的AGV动态避碰路径规划方法

为了解决多AGV在动态不稳环境下的无碰撞路径规划和系统效率提升的问题,提出了基于时间窗的AGV无碰撞路径规划方法。首先建立了多AGV的避碰模型,并结合时间窗模型,将多AGV的无碰撞路径规划分为预先规划和实时规划两阶段,预先规划阶段进行多AGV无冲突时间窗的计算和最大化系统中AGV的流通量,实时规划阶段通过改变AGV在避碰模型上的占用优先级和局部重规划的方法进行动态避碰。最后以某智能仓储为应用案例进行仿真实验,证明了该算法能有效避免多AGV的碰撞,提高AGV的流通量,同时在动态环境下具有较好的鲁棒性和柔性。     

Trajectory optimization and state selection for urban automated driving

The automated driving is an emerging technology in which a car performs recognition, decision making, and control. The decision-making system consists of route planning and trajectory planning. The route planning optimizes the shortest path to the destination like an automotive navigation system. According to static and dynamic obstacles around the vehicle, the trajectory planning generates lateral and longitudinal profiles for vehicle maneuver to drive the given path. This study is focused on the trajectory planning for vehicle maneuver in urban traffic scenes. This paper proposes a trajectory generation method that extends the existing method to generate more natural behavior with small acceleration and deceleration. This paper introduces an intermediate behavior to gradually switch from the velocity keeping to the distance keeping. The proposed method can generate smooth trajectory with small acceleration/deceleration. Numerical experiments show that the vehicle generates smooth behaviors according to surrounding vehicles.     

空地异构机器人系统协作巡逻路径规划方法EI北大核心CSCD

空地异构机器人系统由空中无人机和地面无人车组成,当两者协作执行持续巡逻任务时,使用无人车充当无人机的地面移动补给站能够解决无人机续航能力不足的问题.运动受限于路网中的无人车必须在适当地点为无人机补充能量,这使得两者的路径高度耦合,给空地协作路径规划带来了挑战.针对此问题,本文通过分析无人机能量、路网、空地汇合时间、巡逻任务全覆盖等多种约束,以无人机完成全部巡逻任务的总距离为代价,建立了空地协作巡逻路径规划模型.该模型可推广至多架无人机与多辆无人车协作的情形.然后,采用遗传算法与蚁群算法相融合的方法,对无人机巡逻路径和无人车能量补给路径进行优化求解.仿真实验表明,本文的方法不仅可以得到很好的路径规划结果,而且较其他算法具有更优的收敛性和执行速度.     

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 (${ < 100}$ ms) and low packet loss (${ < 5\%}$) can bring little influence to those trajectory planning algorithms that only depend on V2V 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.      

A control law for vehicle merging inspired by dragonfly behavior

Autonomous control of vehicles has recently attracted considerable attention. In this sense, vehicle merging has become an important topic in this field of research. However, in conventional studies, the controlled vehicle must calculate the movement of other surrounding vehicles to complete the merge, requiring high computational costs. In this paper, we focus on dragonfly behavior to solve this issue. Indeed, insects can behave adaptively in the complex real world in spite of the limited size of their brains. They reduce the computational requirements of their brain by relying on different properties of their surroundings, basing their intelligent behaviors on simple strategies. The behavior of a dragonfly when chasing a prey is an example of these strategies. In this study, we address the vehicle merging maneuver by applying dragonfly’s strategies to control the movement of the merging vehicle. We propose a simple control method inspired by the aforementioned strategies and, finally, we present simulation results that were conducted to demonstrate the effectiveness of this method.     

基于改进蚁群算法的钻井救援车辆路径规划

为了解决救援车辆路途时间过长导致钻井事故应急救援不及时的问题, 提出一种基于改进蚁群算法的钻井救援车辆路径规划方法. 首先针对基本蚁群算法易陷入局部最优, 且在求解转移概率时仅依据信息素含量和路径长度, 未考虑实际路网中影响道路通行的外界因素等不足, 通过引入路径权重因子和改进路径选择策略, 对基本蚁群算法进行了改进; 然后利用改进的蚁群算法, 以用时最少为目标建立了救援车辆路径规划模型; 最后进行了救援车路径规划仿真实验和实际应用测试, 结果表明本文提出的方法可以合理规划出一条全局最优的救援路径, 能有效地解决钻井救援车辆路径规划问题.     

Tool path generation for multi-axis freeform surface finishing with the LKH TSP solver

In freeform surface finishing, there are three major types of tool path topologies: the direction-parallel type, the contour–parallel type and the space-filling curve (SFC) type. The SFC topology is capable of covering the whole surface with only one path. In this paper, we present a new way of planning the SFC type tool path by formulating the planning task as a traveling salesman problem (TSP). The optimal path is generated in two steps. Firstly, a set of regular cutter contact (CC) points is generated on the input surface. A cutting simulation method is developed to evaluate the scallop error and determine the position of the next CC point in cross-feed direction. This method is free of local surface curvature assumptions and is therefore accurate for big cutters. Secondly, the obtained CC points are input into an efficient TSP solver LHK for the optimal CC point linking sequences. To stop the CC points from diagonal linking or penetrating linking, the Euclidean distance evaluation function for two CC points is redefined in LHK. The proposed tool path generation method is verified with several freeform surface examples; the results show that the method can automatically find the optimal feed direction and it can generate shorter tool path than the traditional SFC method. The feasibility of the proposed method is also verified by a cutting experiment.     

Distributed reactive collision avoidance

The work contained in this paper concerns a novel approach to the n-vehicle collision avoidance problem. The vehicle model used here allows for three-dimensional movement and represents a wide range of vehicles. The algorithm works in conjunction with any desired controller to guarantee all vehicles remain free of collisions while attempting to follow their desired control. This algorithm is reactive and distributed, making it well suited for real time applications, and explicitly accounts for actuation limits. A robustness analysis is presented which provides a means to account for delays and unmodeled dynamics. Robustness to an adversarial vehicle is also presented. Results are demonstrated in simulation.