Rear-end accidents, which result in severe personal casualties and property damage, constitute the most prevalent type of vehicle collisions on roadways. This paper proposes a virtual Mass-Spring-Damper (MSD) model composed of two vehicles to investigate the mechanisms and processes of rear-end accidents. Furthermore, the spring stiffness and damping coefficient of the virtual MSD model are optimized to prevent such collisions. Initially, when the leading vehicle stops abruptly or decelerates suddenly, the proposed MSD model is employed to describe the dynamic behavior of the following vehicle under three distinct damping conditions: under-damping, critical-damping, and over-damping. Subsequently, to determine the optimal parameters of the virtual MSD model for both static and dynamic obstacles ahead, various combinations of spring stiffness and damping coefficients are evaluated under constraints involving the deceleration and its derivative of the following vehicle, the most appropriate parameters for the virtual MSD model can then be selected from the candidate combinations using an optimization algorithm. Furthermore, the response function of the relative spacing between the two vehicles and the motion of the following vehicle under the external excitation caused by the leading vehicle are derived. By minimizing the relative spacing as the objective function, the optimal values of spring stiffness and damper coefficient under various curb weights and different initial velocities are incorporated into the established virtual MSD model. Finally, the shortest safety spacing between the two vehicles is calculated based on these optimized parameters, thereby ensuring that the following vehicle can effectively avoid rear-end accidents under various driving conditions. The application of the virtual MSD model in multi-vehicle collision avoidance and the associated parameter optimization methods are elaborated upon in Part II of this study.

A driver-assisted fault-tolerant control (FTC) based on a neural network-improved Nash game framework is proposed to maintain vehicle trajectory tracking and stability when the steer-by-wire (SbW) system suffers actuator functional faults. A radial basis function (RBF) neural network with an improved activation function is used to train a driver model online to capture difficult-to-model drivers' behavior accurately and then combine it with a physical vehicle model to construct a data-physical hybrid driver-vehicle system. The differential drive-assisted steering (DDAS) moment generated by the front axle is used to correct the abnormal steering function. Still, the effect of the direct yaw moment control (DYC) is affected due to the conflict between the trajectory tracking target and the lateral stability target. A distributed model predictive control (MPC)-based noncooperative game model is built by mapping the two control targets as the game players. The data-physical hybrid driver-vehicle model is used to predict the dynamic behavioral changes of the players. Finally, a Nash equilibrium solution is obtained to balance the target conflict. Simulation and hardware-in-the-loop (HIL) tests show that the designed method can improve the stability of faulty vehicles under different styles of drivers by up to 60.48% and the trajectory tracking performance by up to 31.55% while maintaining good real-time performance.

ObjectiveThe traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of changes in curvature and road bends. This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigating curves and paths. Although extending the time horizon of the standard fixed time horizon model predictive control (MPC) can improve curve path tracking accuracy, it comes with high computational costs, making it impractical in situations with restricted computing resources. Consequently, an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing.MethodsInitially, it was crucial to establish a kinematic model for the mowing robot, which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking. The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing. Second, the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot. The region located a certain distance ahead of the robot's current position, was identified to as the preview region, enabling a more accurate prediction of the robot's future traveling conditions. Calculations for both the curve factor and curve change factor were carried out within this preview region. The curvature factor represented the initial curvature of the path, while the curvature change factor indicated the extent of curvature variation in this region. These two variables were then fed into a fuzzy controller, which adjusted the prediction time horizon of the MPC. The integration enabled the mowing robot to promptly adjust to changes in the path's curvature, thereby improving its accuracy in tracking the desired trajectory. Additionally, a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance. This approach ensured that MPC activation occurred only when needed, rather than at every time step, resulting in reduced computational expenses especially during periods of smooth robot motion where unnecessary computation overhead could be minimized. By meeting kinematic and dynamic constraints, the optimization algorithm successfully identified an optimal control sequence, ultimately enhancing stability and reliability of the control system. Consequently, these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex environments.Results and DiscussionThe adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maximum absolute lateral error to within 0.13 rad and 11 cm, respectively, surpassing the performance of the MPC controller in the control group. Moreover, compared to both the first and fourth groups, the adaptive time-horizon MPC controller achieved a remarkable reduction of 75.39% and 57.83% in mean values for lateral error and heading error, respectively (38.38% and 31.84%, respectively). Additionally, it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral error (0.025 6 m) and course error (0.025 5 rad), outperforming all four fixed time-horizon MPC controllers tested in the study. Furthermore, this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.004 9 s, notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC.ConclusionsThe experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC. By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events, this approach can more effectively handle scenarios with restricted computational resources, ensuring superior control precision and stability. Furthermore, it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots, offering a more practical and reliable solution for their practical application.

ObjectiveHigh-precision localization technology serves as the crucial foundation in enabling the autonomous navigation operations of intelligent agricultural robots. However, the traditional global navigation satellite system (GNSS) localization method faces numerous limitations, such as tree shadow, electromagnetic interference, and other factors in the agricultural environment brings challenges to the accuracy and reliability of localization technology. To address the deficiencies and achieve precise localization of agricultural robots independent of GNSS, a localization method was proposed based on the fusion of three-dimensional light detection and ranging (LiDAR) data and inertial measurement unit (IMU) information to enhance localization accuracy and reliability.MethodsLiDAR was used to obtain point cloud data in the agricultural environment and realize self-localization via point cloud matching. By integrating real-time motion parameter measurements from the IMU with LiDAR data, a high-precision localization solution for agricultural robots was achieved through a specific fusion algorithm. Firstly, the LiDAR-obtained point cloud data was preprocessed and the depth map was used to save the data. This approach could reduce the dimensionality of the original LiDAR point cloud, and eliminate the disorder of the original LiDAR point cloud arrangement, facilitating traversal and clustering through graph search. Given the presence of numerous distinct crops like trees in the agricultural environment, an angle-based clustering method was adopted. Specific angle-based clustering criteria were set to group the point cloud data, leading to the segmentation of different clusters of points, and obvious crops in the agricultural environment was effectively perceived. Furthermore, to improve the accuracy and stability of positioning, an improved three-dimensional normal distribution transform (3D-NDT) localization algorithm was proposed. This algorithm operated by matching the LiDAR-scanned point cloud data in real time with the pre-existing down sampled point cloud map to achieve real-time localization. Considering that direct down sampling of LiDAR point clouds in the agricultural environment could result in the loss of crucial environmental data, a point cloud clustering operation was used in place of down sampling operation, thereby improving matching accuracy and positioning precision. Secondly, to address potential constraints and shortcomings of using a single sensor for robot localization, a multi-sensor information fusion strategy was deployed to improve the localization accuracy. Specifically, the extended Kalman filter algorithm (EKF) was chosen to fuse the localization data from LiDAR point cloud and the IMU odometer information. The IMU provided essential motion parameters such as acceleration and angular velocity of the agricultural robot, and by combining with the LiDAR-derived localization information, the localization of the agricultural robot could be more accurately estimated. This fusion approach maximized the advantages of different sensors, compensated for their individual limitations, and improved the overall localization accuracy of the agricultural robot.Results and DiscussionsA series of experimental results in the Gazebo simulation environment of the robot operating system (ROS) and real operation scenarios showed that the fusion localization method proposed had significant advantages. In the simulation environment, the average localization errors of the proposed multi-sensor data fusion localization method were 1.7 and 1.8 cm, respectively, while in the experimental scenario, these errors were 3.3 and 3.3 cm, respectively, which were significantly better than the traditional 3D-NDT localization algorithm. These findings showed that the localization method proposed in this study could achieve high-precision localization in the complex agricultural environment, and provide reliable localization assistance for the autonomous functioning of agricultural robots.ConclusionsThe proposed localization method based on the fusion of LiDAR data and IMU information provided a novel localization solution for the autonomous operation of agricultural robots in areas with limited GNSS reception. Through the comprehensive utilization of multi-sensor information and adopting advanced data processing and fusion algorithms, the localization accuracy of agricultural robots could be significantly improved, which could provide a new reference for the intelligence and automation of agricultural production.

为解决车辆在避障时由于路径曲率不连续易发生原地转向的问题,该文对比探究智能车辆避障路径规划方法,开展了五次贝塞尔曲线、三次贝塞尔曲线、五次样条曲线和三次样条曲线避障路径研究.首先,确定控制点分别拟合出五次贝塞尔曲线、三次贝塞尔曲线、五次样条曲线和三次样条曲线的避障路径;其次,采用Matlab和Carsim联合仿真分析的方法对该四种避障路径的曲率进行对比分析;最后,基于无人驾驶平台,采用纯跟踪预瞄模型开展实车试验,以此探究四种避障路径的工程实用性.结果表明:采用样条曲线和贝塞尔曲线进行避障规划,车辆的横向超调量为0;相比于三次曲线避障方法,五次曲线避障方法所规划的路径曲率更连续;相比于样条曲线避障方法,贝塞尔曲线避障方法车辆的转向更稳定.

When the intelligent weeding robot was working in the lawn， it was easy to be affected by external disturbance and system uncertainty， which led to long tracking convergence time and poor tracking effect. Therefore， an adaptive fast integrating terminal sliding mode control algorithm for trajectory tracking was designed. Firstly， the dynamics model of the weeding robot was established by considering the dynamic characteristics of the driving wheel and uncertainties such as unmodeled errors， external interference and dynamic and static friction. Then， based on the established dynamic model， an adaptive fast integrating terminal sliding mode controller was designed. The proposed controller combined the advantages of fast terminal sliding mode， integral sliding mode and adaptive estimation technology to achieve the desired tracking performance and suppress control signal jitter. At the same time， without specifying the upper bound of the system uncertainty and external interference， the designed adaptive estimation can be used for real-time compensation to improve the robustness of the system. Finally， the effectiveness of the proposed method was verified by simulation and experiment. The experimental results showed that the designed controller can make the tracking error converge quickly in a limited time， and the absolute value of the lateral error was less than 0.0979m， the absolute value of the longitudinal error was less than 0.1026m， and the absolute value of the heading Angle error was less than 0.0578rad， which can ensure the robot to track the working path accurately and have strong robustness.

Ni-W/ZnO superhydrophobic composite coating was prepared on the surface of 45# steel by electrodeposition technology in combination with spraying method. The microstructure and main composition of the composite coating were characterized, and its hydrophobicity, mechanical stability and corrosion resistance were tested and analyzed. The results show that the formation of a micro-nano hierarchical structure on the composite coating surface, primarily composed of six elements: Ni, W, Zn, O, C and Si. A relatively uniform dispersion state is observed among the modified ZnO particles in the composite coating. The water droplet contact angle on the composite coating surface reaches 151.4 °, which demonstrates superhydrophobicity. Following 20 repetitions of tape pulling, falling sand impact and sandpaper friction, the contact angle is still more than 150 °, which indicates that the composite coating has the ability to stably maintain superhydrophobicity and possesses mechanical stability. In addition, the composite coating exhibits excellent corrosion resistance, with a corrosion current density of just 6.79×10 -7 A/cm~2 and polarization resistance of 3.25×10~4 Ω·cm~2, providing ideal corrosion protection for 45# steel in comparison to conventional Ni-W alloy coating.

针对自主代客泊车系统的路径规划问题,提出了一种基于有向图搜索和几何曲线的自主泊车路径规划算法.提出了一种基于广义维诺图的有向混合A*全局路径规划算法,准确有效地生成一条从停车场入口到达泊车起始点的无碰撞路径.采用改进的C型垂直停车路径规划算法生成局部泊车路径.通过Matlab和CarSim联合仿真及实车试验证明了该算法的有效性和实用性.试验结果表明:与Hybrid A*算法相比,提出的全局路径规划算法在生成可行路径时,所需时间更短,避障性更好.该自主代客泊车路径规划算法,路径跟踪效果较好,能够有效可靠地实现自主泊车目标.

以提高45#钢法兰的耐腐蚀性能为目标,使用含有硝酸镨的锌系磷化液对法兰进行磷化处理,并研究了硝酸镨的质量浓度对磷化处理后的法兰宏观形貌以及锌系磷化膜的微观形貌和耐腐蚀性能的影响.结果表明:硝酸镨对磷化处理后的法兰宏观形貌基本无影响,但在不同硝酸镨质量浓度下获得的磷化膜微观形貌存在差异,导致磷化膜的耐腐蚀性能发生改变.随着硝酸镨浓度从0 g/L增加到4.0×10-2 g/L,磷化膜的平整度和致密度逐步改善,耐腐蚀性能明显提高.随着硝酸镨质量浓度从4.0×10-2g/L增加到6.0×10-2g/L,磷化膜的平整度和致密度转而变差,导致耐腐蚀性能下降.硝酸镨质量浓度为4.0×10-2 g/L时获得的磷化膜结晶更致密,其电荷转移电阻、频率为0.01 Hz时的阻抗模值以及液滴变色时间均最大,分别达到2150Ω ·cm2、2744Ω ·cm2、150 s,该磷化膜表现出良好的耐腐蚀性能.

Path-planning is a widely studied subject in the field of unmanned robots, and it is also one of the basic operations required to realize collision avoidance for autonomous driving vehicles. With information about obstacles location given by onboard sensors and road geometry information provided by highly automated driving map [28], a path planner calculates a feasible collisionfree trajectory to the destination in the complex traffic environment while satisfying the criteria set. The path planning problem for collision avoidance can be described using the following set of specifications.

The main purpose of path-tracking control for automated vehicles is to control the vehicle’s lateral and longitudinal motion along the reference path which is regulated by the path planner. In past decades, a great deal of researches have been done on path-tracking algorithms, and some of them have been applied to automated vehicles in recent years. Commonly, the pathtracking algorithms can be classified in to two categories, the classical control algorithms already introduced in Chapter 3 and the model-based control that will be discussed in this chapter.

According to the inherent nature of the fluid that can naturally avoid obstacles, a path planning method for active collision avoidance of autonomous vehicles is presented based on the virtual flow field. Firstly, the mathematical model of the virtual flow field on the road is established by using the theory of hydrodynamics. Then a fifth degree polynomial curve is adopted to construct the virtual hazard area of the obstacle vehicle to prevent the fluid into this area, and it can be easily resized by adjusting the parameters of the lateral and longitudinal safety distance. Finally, Computational Fluid Dynamics (CFD) simulations are performed to quantitative predict the dynamic behavior of the ego vehicle on the straight or curved road and the desired path for active collision avoidance can be determined based on the calculation result of the flow field. The simulation results show that the proposed path planning method takes into account the dynamic characteristics and kinematic constraints of the vehicle, and ensures that the vehicle doesn't collide with the dynamic and static obstacles on the road.

为提高分布式驱动电动智能汽车在自主循迹过程中关键参数的估计精度并降低模型不确定性对控制系统鲁棒性的影响,本文中提出了一种基于观测器的自适应滑模路径跟踪控制策略.首先,针对难以直接精确测量的车辆纵、侧向速度,建立了5输入3输出3状态的状态估计系统,并采用最小模型误差准则以降低估计过程轮胎的非线性特性带来的观测模型误差.接着,基于运动学模型,计算出了路径跟踪期望横摆角速度响应,并采用自适应滑模算法实现主动转向控制.考虑线控转向系统的潜在失效风险,引入径向基神经网络对系统不确定性进行在线估计.同时,设计了直接横摆稳定控制器并采用最优转矩分配策略,进一步提高车辆的稳定性.最后,对车辆状态估计和路径跟踪进行了Carsim/Matlab联合仿真,结果表明:基于最小模型误差准则的观测器能取得较可靠的估计结果,路径跟踪控制器能保证车辆具有较好的跟踪精度和鲁棒性.

针对智能车辆的路径跟踪控制问题,基于虚拟质量-弹簧-阻尼(MSD)模型以及驾驶员预瞄理论,设计了一种路径跟踪控制方法.首先,利用脉冲响应法对虚拟MSD模型的运动响应进行求解,得到不同工况下车辆的状态响应,并获得前轮转向角的控制函数;随后,结合路径跟踪的稳定性和快速性要求,建立系统性能函数,得到虚拟MSD模型最优参数;最后,在典型的双移线工况及实际道路环境下对控制器进行了验证.结果表明,与线性二次型最优控制器相比,设计的预瞄式MSD控制器在不同车速下均具有良好的路径跟踪精度和行驶稳定性,且具有明显的实时性优势.

There were nearly 2 million police-reported front-to-rear crashes in 2017, representing 32% of all crashes [85]. Front crash prevention systems, which warn drivers, brake autonomously, or perform both functions when a frontal collision is imminent, have been estimated to potentially prevent or mitigate up to 70% of front-to-rear collisions and 20% of all police-reported crashes if installed on all passenger vehicles [86]. In addition, in some special cases, such as when a moose suddenly crosses the road in front of the vehicle, front-crash prevention systems can reduce the severity of a crash by lowering the speed of the host vehicle, as shown in Fig. 6.1.

为了提高智能汽车在恶劣天气下的环境感知能力,提出一种针对雾天环境下车辆和行人的检测方法.将AOD-Net去雾算法与SSD目标检测算法相结合,实现了城市交通雾天环境下的车辆和行人检测.利用去雾处理后的雾天图片和原始带雾图片分别进行目标检测模型训练,并在不同雾浓度等级的交通环境下进行车辆和行人检测,结果显示:AOD-Net与SSD网络相结合得到的检测mAP值可达75.8％,比SSD算法的mAP值高4.1％,表明AOD-Net与SSD网络相结合的算法能更加有效地检测带雾图片中的车辆和行人.

针对基于特征点法的视觉里程计实时性和鲁棒性较差的问题,提出一种基于半直接法的双目视觉惯性里程计算法.在跟踪线程中将惯性测量数据作为先验,并使用逆光流法跟踪均匀化的特征关键点,以提高特征匹配的速度和鲁棒性,构建高精度的初始化地图,为后续的运动估计提供良好的初值.使用简化的双目视觉模型构造重投影误差,结合IM U误差项构建联合优化模型,并在滑动窗口中进行非线性优化求解.实验结果显示,该算法在数据集上的定位精度达到主流算法的水平,与VINS-Fusion算法相比,此算法拥有更低的CPU负载和更高的运行帧率.

为进一步优化汽车自动紧急制动系统的避撞算法,设计了考虑制动过程驾乘舒适性的制动减速度控制策略.通过设置制动减速度变化缓冲区对制动减速度及其变化率进行限制,得到满足舒适性条件的减速度控制曲线.基于AEB典型测试工况对设计的减速度控制曲线进行具体分析,提出了新的制动安全距离模型.最后,建立车辆纵向动力学模型并通过CarSim与Matlab联合仿真对提出的避撞算法进行仿真验证.仿真结果显示:提出的避撞算法能够在实现有效避撞的同时满足舒适性条件.

In this chapter, a path-planning method that generates a trajectory to mitigate the crash as much as possible is proposed for autonomous vehicles in emergency situations where an accident is unavoidable. When the avoidance of a collision is impossible for path-planning system, then the artificial potential field is filled into the controller objective to achieve the lowest possible severity. The MPC algorithm is adopted here for path-planning, and the vehicle dynamic is also treated as an optimal control problem. On account of the analysis above, the MPC can optimize the command following, obstacles avoidance, vehicle dynamics, road regulation, and mitigate the inevitable collision based on the predicted values. Simulations in this chapter have proved that the proposed MPC algorithm has the ability to avoid obstacles and mitigate the collision if the accident is inevitable.