Autonomous driving is one of the promising technologies to tackle traffic accident and congestion problems nowadays. Even though an autonomous vehicle is operated without humans, it is necessary to reflect the driving characteristics of a human driver. This can increase user acceptance to autonomous driving system, which in turn will improve driving safety because of human occupants' trust in it. In this paper, a combined trajectory planning and tracking algorithm is proposed for the vehicle control. Firstly, traffic environments and driving styles are modeled with the Artificial Potential Field (APF) approach. Secondly, those APF values are integrated into the Model Predictive Control (MPC) design process, which can optimize the trajectories and control outputs. In this way, we add people's driving habits and styles into the controller, so that the controlled vehicle can move under the effects of the traffic environments and human's driving styles. At last, autonomous driving, which reflects two types of human drivers' driving styles (a cautious driving style and an aggressive one), is tested by the simulation experiments in two scenarios (car-following and lane-changing). Furthermore, the result demonstrates that the proposed algorithm can reflect driving styles. Accordingly, this novel controller can be utilized in the autonomous vehicle control field.

OpenDRIVE格式是高精度地图数据的存储格式标准，但从原始数据采集到OpenDRIVE高精度地图生成的过程涉及环节多、过程复杂，构建难度大。为此，设计了从shp数据到OpenDRIVE格式的高精地图构建方案，重点解决了轨迹坐标计算、曲线拟合、节点属性值计算、仿真验证等关键问题，并通过真实数据的转换验证了方法的可行性，有效提高了OpenDRIVE高精度地图生成的自动化程度。

在车辆智能化驾驶的过程中,车道线检测是环境感知的重要技术内容之一。以车道线检测技术为基础实现的车道偏离预警和车道保持等高级辅助驾驶系统,能够有效减少事故碰撞的风险。在自动驾驶车辆中,车道线检测技术作为辅助手段,为其指明行车区域。本文对车道线检测技术的研究现状进行了总结,探讨了车道线在图像中所表达出来的特点及检测难点。顾及车道线检测实时性、准确性和鲁棒性的要求,结合相机成像模型,提出了基于灭点方向的车道直线检测方法,及直线和贝塞尔曲线组合的车道线模型估计方法。论文具体研究内容包括以下几个方面:1)以灭点方向与车道线区域互相迭代的方式完成车道直线的检测。结合相机成像模型,根据局部统计特征保留较为完整的车道线结构,并剔除明显干扰特征。基于灭点的导向作用,通过直方图统计设计车道线可信区域的提取方法;并在可信区域基础上,重新对灭点方向进行估计。迭代这一过程直至稳定。通过代价函数完成车道中心直线的筛选。2)建立直线和二次贝塞尔曲线组合的车道线模型。基于已知车道中心直线完成对二次贝塞尔曲线的两个节点的确定,并用粒子滤波直接完成对贝塞尔曲线最后一个节点的估计。在曲线估计过程中,为保证曲线的稳定性,用与灭点的距离表示最后一个节点以关联两侧车道线,在一次粒子滤波过程中完成估计,同时也保证了实时性。根据车道线统计分布特征对粒子权重的计算进行设计。3)为了保证实时性,在涉及全幅图像的操作中,如特征提取、噪声滤除和似然度计算等等,针对行扫描线的特点,设计使用邻域均值及统计为基础进行处理,能够通过递推方式实现,减少计算时间;并以Bresenham直线表示方式构建不同长宽的直线坐标映射空间,提高直方图统计过程的效率。4)最后,在公开数据集和武汉大学的实车数据集上进行了算法实验。实验结果表明,本文算法能够满足不同场景下的车道线检测需求,具有良好的实时性、准确性和鲁棒性。

移动机器人的障碍物快速检测是其导航、避障、轨迹跟踪的关键技术。目前多数传感器存在距离盲区以及异常尺度等问题,且现有算法大多计算复杂,难以满足实时性需求。为了改善这些问题,本文提出了一种基于环视逆投影差分的移动机器人障碍

路径规划是为机器人生成可行驶路径以实现循迹的过程。因此,机器人的位置应该位于或靠近规划的行驶路径。从而,路径规划可为机器人定位产生重要的约束。该文提出一种规划路径约束的位置概率图(PILPM)模型,该模型通过概率来表征机器人在

Path planning is a step to generate a feasible path for a robot to track along. Locations of the robot are supposed to lie on or at least nearby the planned path, which can thus generate important constraints for robot localization. In this paper, a model, called Path-Induced Location Probability Map (PI-LPM), to exploit such constraint on robot localization is proposed. The proposed PI-LPM model is a Probability Density Function (PDF) over the entire map with the probability to describe the likelihood that the robot is located. The PDF is generated from all the points representing the path by applying the Kernel Density Estimation (KDE) method with each point as a sampling point. Based on the PI-LPM model, a Robot Localization from Planned Path Constraints (RL-PPC) method to enhance robot localization is proposed. In this method, particle filter is applied to fuse the develop PI-LPM model and existing localization methods, where the probability from PI-LPM is an important factor to assign weights to the particles. The proposed method is validated with both simulation and real data. In the experiment, the proposed PI-LPM model is integrated into both GPS and LiDAR based localization systems. Experimental results demonstrate that the RL-PPC method can effectively improve the over-all performance of robot localization. © 2022 Science Press. All rights reserved.

为提高智能车定位精度,提出了一种融合激光雷达与双层地图模型的智能车定位方法。该双层地图模型在车道图层基础上,增加基于激光点云的稀疏特征图层。稀疏特征地图由车辆位姿、2D强度特征和3D特征3部分组成,可为智能车定位提供精确的位

A two-branch convolutional neural network (CNN) architecture for feature extraction in person re-identification (re-ID) based on video surveillance is proposed. Highly discriminative person features are obtained by extracting both global and local features. Moreover, an adaptive triplet loss function based on the original triplet loss function is proposed and is used in the network training process, resulting in a significantly improved learning efficiency. The experimental results on open datasets demonstrate the effectiveness of the proposed method.

This paper proposes a novel fuzzy comprehensive evaluation model based on the 2-tuple linguistic model and grey target decision method (2TL-GTD) for evaluating the comprehensive application effect of the connected vehicle system. Since the evaluation indicator system of the research problem belongs to multi-source heterogeneous data, the original data is firstly converted into a unified 2-tuple form using linguistic 2-tuple model, which can avoid information distortion or loss. Second, the deviation maximization method is used to objectively determine the weight of each indicator. Then, the grey target decision-making method is used to deal with the uncertain information in the data and select the optimal solution according to the bullseye degree, which allows a comprehensive evaluation of the application effectiveness of the connected vehicle system. Taking the tunnel scenario as an example to verify the effectiveness of 2LT-GTD, it is found that in the tunnel scenario, the connected vehicle system can remind the driver to prepare in advance, effectively reducing traffic accidents and pollutant emissions. Based on the results, it can provide technical improvement directions for relevant technical departments to achieve the coordinated development of safe driving, efficient driving and ecological driving.