牵引力控制系统模糊PI控制方法研究

提出牵引力控制系统的油门模糊增量PI控制算法和驱动轮制动模糊PI控制算法。在建立汽车加速过程的发动机模型、制动器模型、轮胎模型、整车模型和路面自动识别系统的基础上，采用所提出的控制算法对典型附着路面上的牵引力控制过程进行了仿真模拟。结果表明路面自动识别系统能识别路面附着变化，有效抑制各种工况的驱动轮过度滑转，有较强的鲁棒性。     

无人驾驶汽车路径规划算法研究综述

无人驾驶汽车是目前汽车发展的一个大方向,无人驾驶的实现依靠于汽车的感知、决策和控制功能。路径规划属于决策中重要的一环。目前,无人驾驶汽车的路径规划算法存在受环境影响较大,无法适用于复杂的道路环境的问题,基于此文章对无人驾驶汽车轨迹规划算法进行归纳。其在广义上可分成全局路径规划和局部路径规划两种,文章对上述两种规划进行细分并介绍了各种路径规划方法的原理,分析了各个方法的优劣,为无人驾驶汽车路径规划算法的研究提供参考。     

Development of algorithms for commercial vehicle mass and road grade estimation

Estimation algorithms for road slope angle and vehicle mass are presented for commercial vehicles. It is well known that vehicle weight and road grade significantly affect the longitudinal motion of a commercial vehicle. However, it is very difficult to measure the weight and road slope angle in real time because of lack of sensor technology. In addition, the total weight of a commercial vehicles varies depending on the freight. In this study, the road grade and vehicle mass estimation algorithms are proposed using the RLS (Recursive Least Square) method and only the in-vehicle sensors. The proposed algorithms are verified in experiments using a commercial vehicle under various conditions.     

基于模式识别与ST-MRF相结合的车辆检测方法

车辆检测技术的主要难点是在于解决车辆之间的遮挡,以及由于光照变化引起的车辆与其阴影之间的遮挡问题,这些问题将直接影响检测的精度.针对这个问题,在原ST-MRF方法上研究了基于模式识别与ST-MRF相结合的车辆检测方法.模式识别技术分割相互遮挡的2辆车之间的边界,并识别相互遮挡车辆的边缘间隙以及边界信息,模式识别结果反馈...     

基于改进人工势场的自动驾驶汽车弯道超车动态路径规划

动态路径规划是自动驾驶汽车避障控制的关键技术。针对自动驾驶汽车弯道超车工况，建立基于改进人工势场(Artificial Potential Field, APF)的动态路径规划方法。为使基于APF的动态路径规划方法能运用于包含弯曲道路的复杂交通环境，将已在直道环境验证过的道路APF函数通过极坐标系与笛卡尔坐标系的相互转换，建立考虑道路曲率的弯曲道路APF函数。针对根据车辆质心位置判断车辆碰撞风险方法存在的缺陷，提出考虑车辆体积的碰撞风险预判方法，建立综合考虑车辆位置、速度和体积的障碍车辆APF函数。基于弯曲道路APF和改进障碍车辆APF，建立道路环境综合APF，引导车辆实现弯道超车。为避免目标函数中子目标相互干涉，提高弯道超车安全性，提出根据本车与障碍车辆相对位置关系自适应调整权重矩阵的方法。基于Carsim/Simulink联合仿真平台，分别在静态障碍车辆和动态障碍车辆2种工况下，验证自动驾驶汽车弯道超车动态路径规划的有效性。研究结果表明：所建立的弯曲道路APF能引导车辆转弯行驶，避免冲出车道；目标函数权重自适应调整方法能根据超车过程动态调整子目标的权重，规划出符合道路交通安全法规的路径，避免车辆超车时提前折返原车道，提高了超车安全性；考虑车辆体积的障碍车辆APF提高了车辆碰撞风险的预判精度，有效避免碰撞事故发生。     

不同光照条件下直线型车道标识识别方法研究

为了解决不同光照条件下车道标识的识别，提出一种基于双标准可调模板关联技术的车道标识识别方法。该方法首先根据光照的变化采用不同的预处理技术对图像进行处理，然后利用基于统计的方法得到车道的初始化标识线，最后在以该书初始化标识线为尽准建立的感兴趣区域内利用双标准可调模板关联技术对初始化标识线进行矫正从而得到可靠的车道标识线参数。     

发展自动驾驶汽车的挑战和前景展望

自动驾驶汽车不仅是对道路交通具有革命性影响的工具,更是新时代人类进步的一大表现。不过,自动驾驶汽车的独特性、复杂性也意味着其在完善过程中会遇到各种挑战和问题。对此,本文通过对国内外自动驾驶汽车发展现状的分析,探寻其发展中面临的挑战,展望其未来,希望能推动该领域的研究发展,为推动自动驾驶汽车发展提供参考。     

不利天气高速公路路况车载采集系统研究

分析了不利天气下道路安全状况评估参数及其影响因素，确定了车载路况采集系统的功能，设计了由道路能见度检测、道路环境温湿度检测、路面状况识别、路面微观形貌检测、路况数据定位与路况数据无线传输6个子系统以及检测车、电源系统等组成的路况检测系统，分析了系统设计中的一些关键技术。     

汽车驾驶人姿态监测系统研究综述（双语出版）

随着自动驾驶技术的发展,驾驶人将会参与更多的与驾驶无关的活动,从而呈现出新的姿态,这些新姿态是优化传统被动安全系统的重要切入点。而且在未来相当长的时间内,自动驾驶车辆的行驶依然依赖于人和系统的密切配合。对驾驶人姿态的观察,则可以为判断驾驶人是否有能力及时接管车辆提供帮助,从而确保安全、合理的人机交互过程。通过对大量相关文献的系统性梳理,综述了汽车驾驶人姿态监测技术的智能化发展趋势,从传感器种类以及相应的姿态监测算法出发,分析了目前不同监测系统的优缺点。研究发现,尽管传感器技术和姿态识别算法取得了明显进步,然而廉价稳定且能够在实际驾驶条件下对驾驶人姿态准确感知的监测系统依然缺乏。总体而言,目前的监测系统大多只是集中于对驾驶人局部身体部位的感知,缺乏实际驾驶条件下的性能分析,并且对驾驶人状态的实时感知和预测能力仍有待完善。最后,针对目前监测系统所面临的问题,对未来可能的研究方向进行展望,并提出主动式立体视觉系统和压力传感器阵列相融合的驾驶人姿态监测方式。研究成果将为驾驶人姿态监测系统的研究提供参考和借鉴,从而有助于道路交通安全水平的进一步提升,同时也可为人机交互界面的设计带来启发。     

Friction coefficient measurement for autonomous winter road maintenance

Real-time measurement of tyre–road friction coefficient is extremely valuable for winter road maintenance operations, since knowledge of tyre–road friction coefficient can be used to optimise application of deicing chemicals to the roadway. In this paper, a wheel-based tyre–road friction coefficient measurement system is developed for snowploughs. Unlike a traditional Norse meter, this system is based on measurement of lateral tyre forces, has minimal moving parts and does not use a brake actuator. Hence, it is reliable and inexpensive. A key challenge is quickly detecting changes in the estimated tyre–road friction coefficient while rejecting the high levels of vibratory noise in the measured force signal. Novel filtering and signal processing algorithms are developed to address this challenge, including a biased quadratic mean filter and an accelerometer-based vibration removal filter. Detailed experimental results are presented on the performance of the friction estimation system on different types of road surfaces. It is also shown that disturbances due to lateral and longitudinal vehicle manoeuvres on the estimated friction coefficient can be removed by using accelerometer-based filtering.     

基站式道路气象站布设原理和方法

通过分析国内道路气象站的布设情况,针对"密布式"道路气象站浪费资金和能源等缺点,提出基站式道路气象站.基站式道路气象站是一种低成本的道路气象站布设方法,它能优化道路气象站布设,为合理布设(位置、数量与配置)道路气象站提供定量参考.本文针对国内尚未应用"基站式"道路气象站布设方案的情况,对基站式道路气象站的布设原理和方法做了详细的介绍,对于国内迅速发展的道路气象信息服务,节约资金和能源都有着重要的启发和借鉴意义.     

Identification of linear handling models for road vehicles

This study reports the identification of linear handling models for road vehicles starting from structural identifiability analysis, continuing with the experiments to acquire data on a vehicle equipped with a sensor set and data acquisition system, and ending with the estimation of parameters using the collected data. The model structure originates from the well-known linear bicycle model that is frequently used in handling analysis of road vehicles. Physical parameters of the bicycle model structure are selected as the unknown parameter set that is to be identified. Global identifiability of the model structure is analysed, in detail, and concluded according to various available sensor sets. Physical parameters of the bicycle model structure are estimated using prediction error estimation method. Genetic algorithms are used in the optimisation phase of the identification algorithm to overcome the difficulty in the selection of initial values for parameter estimates. Validation analysis of the identified model is also presented. The identified model is shown to track the system response successfully.     

冰雪道路交通安全管理措施及其成效分析

针对目前我国寒冷地区冬季冰雪天气条件下道路交通安全管理被动、消极,部门间协调性差,紧急救援效率低的局面,对比分析了国内外冰雪天气条件下的道路交通安全特征,通过总结国内外冰雪天气条件下的道路交通安全处理策略,得到了3类有效降低冰雪天气对道路交通安全影响的管理措施:信息提供、交通控制和冰雪处理,并从道路维护管理者、紧急事件管理者、道路交通管理者和道路交通参与者4个方面就3类管理措施各自所能取得的安全成效进行了分析,初步建立了寒冷地区冰雪道路的交通安全管理体系,期望能对我国寒冷地区的冰雪道路交通安全管理有所帮助。     

Hierarchical map representation using vector maps and geometrical maps for self-localization

Map-based self-localization estimates the pose of the self-driving vehicle in an environment, becoming an essential part of autonomous driving tasks. Generally, maps used in self-localization have detailed geometric information on an environment in formats such as point cloud maps and Gaussian mixture model (GMM) maps. As other maps are widely developed for autonomous driving, vector maps store more object-focused information, such as buildings and road facilities, for navigation and scene understanding in autonomous driving tasks. However, it is not compatible with self-localization due to the lack of detailed geometric information. The two different map formats of vector maps and maps for self-localization complicate the management, preventing the development of the area where a self-driving vehicle can drive stably. This paper proposes a unified map format with a hierarchical structure that enables both vector maps and self-localization maps (i.e., GMM maps) to be managed more easily. Because proposed maps can be treated as vector maps at the high-level layer, various tasks related to navigation and scene understanding in autonomous driving can utilize. A GMM map is stored at the low-level layer associated with a vector map component, enabling accurate self-localization in an environment. The proposed map format is compatible with vector maps widely developed by mapping companies on the surface and facilitates future map management. The experimental results of self-localization in urban areas showed that the proposed map gives the competitive self-localization accuracy compared with the GMM map even with fewer cells that link to vector components. The proposed maps enable self-localization with sufficient accuracy for safe autonomous driving operations.     

考虑路网拓扑时变的交通拥堵自适应预测方法研究

对路网交通系统中的交通拥堵进行预测，有利于交通管理和避免交通风险。然而，由于交通管制、道路施工、恶劣天气、自然灾害等原因，路网交通系统的拓扑结构时常发生变化，使得依赖于固定路网拓扑的拥堵预测方法效果不佳。针对这一问题，提出一种双重自适应图卷积循环网络结构(DAGCRN)来处理路网拓扑结构变化情况下的交通拥堵预测问题，该方法运用自适应辅助邻接矩阵对预定义的路网静态图结构进行适应性学习以动态优化原有连接间信息的传递，运用自适应嵌入邻接矩阵对预定义路网静态图结构进行路网隐藏信息的捕捉以确保路网拓扑结构的动态完整性，并采用门控循环单元提取路网交通流的时间特征信息。研究结果表明,DAGCRN具备以下特点：①能够有效捕捉和定位路网拓扑结构发生的变化，并能够在拓扑结构变化时仍然保证拥堵预测的精确率；②相比较一些常见预测模型有更高的预测准确率，尤其是长期预测方面和克服路网结构变化方面更具优势；③进一步的双重自适应功能消融试验，证实了含有自适应辅助邻接矩阵和自适应嵌入邻接矩阵的双重自适应图卷积结构对于路网拓扑结构变化有很强的自适应能力，缺少2个或任一个自适应模块，都会引起模型预测性能的大幅下降。     

基于以打造文旅开发为主的风景道路设计要点浅析

随着旅游业的快速发展、自驾游时代的到来以及交通强国建设战略的大力实施，交通与旅游融合发展成为时代趋势。道路单一的交通功能已无法满足以旅游为导向的城市发展需求，打造一条集交通、文化、景观、生态、游憩于一体的综合性高品质风景道路，使之成为不同于传统围墙式的线性旅游风景区显得尤为重要。基于开封开柳路拓宽改造实例浅析风景道路设计要点，重点论述了风景道路的功能定位、断面规模、呈现形式以及关键节点的近远期结合等，以期为以打造文旅开发为主的风景道路提供设计思路。     

Automated driving recognition technologies for adverse weather conditions

During automated driving in urban areas, decisions must be made while recognizing the surrounding environment using sensors such as camera, Light Detection and Ranging (LiDAR), millimeter-wave radar (MWR), and the global navigation satellite system (GNSS). The ability to drive under various environmental conditions is an important issue for automated driving on any road. In order to introduce the automated vehicles into the markets, the ability to evaluate various traffic conditions and navigate safely presents serious challenges. Another important challenge is the development of a robust recognition system can account for adverse weather conditions. Sun glare, rain, fog, and snow are adverse weather conditions that can occur in the driving environment. This paper summarizes research focused on automated driving technologies and discuss challenges to identifying adverse weather and other situations that make driving difficult, thus complicating the introduction of automated vehicles to the market.     

Analysis of high dynamic car manoeuvres using two types of lever-arm correction

As navigation algorithms using Kalman filters, fuzzy or adaptive algorithms, interacting multiple model (IMM) algorithms and other possible solutions combining data from several sensors, have been progressively used in the last decade, there has been little advance in developing a robust and accurate device available for car manufacturers. The most solutions fail in long-term reliability and/or use too generalized linearization models. This is why in this paper we have examined some high dynamic manoeuvres which are usually a part of automotive tests. Some major issues during these manoeuvres were identified and a modified Kalman filter solution is presented. The problem of positioning of an inertial device within a vehicle is addressed and a transformation of measured data to the centre of gravity (COG) or rotation point (RP) of the vehicle is introduced. We also propose a few methods to identify the start and the stop of a brake test and show distance difference between conventional and modified Kalman algorithm during driving in circles. Finally, a direct and indirect lever-arm correction is introduced and real road tests are made to present an improvement in outputs using one-device sensor setup.     

A New Method for Accurately Estimating the Weight of Moving Vehicles Using Piezoelectric Sensors and Adaptive-footprint Tire Model

Summary This paper presents new methods for estimating the axle weight of a moving vehicle, using two piezoelectric sensors and adaptive-footprint tire model. It is more difficult to weigh vehicles in motion accurately than to weigh standing vehicles. The difficulties in weighing moving vehicles result from sensor limitations as well as dynamic loading effects induced by vehicle/pavement interactions. For example, two identical vehicles with the same weight will generate sensor signals that differ in the shape and the peak value, depending the tire pressure, vehicle speed, road roughness, and sensor characteristics. This paper develops a method that is much less sensitive to these variable factors in determining the axle weight of a moving vehicle. In the developed method, first the piezoelectric sensor signal is reconstructed using the inverse dynamics of a high-pass filter representing the piezoelectric sensor. Then, the reconstructed signal, is normalized, using the nominal road/tire contact length obtained using an adaptive-footprint tire model, and then integrated. Experiments are performed with 3 vehicles of known weight ranging from 1,400 kg to 28,040 kg. The developed method is compared to two other algorithms. Results show that the developed method is most consistent and accurate.     

A New Method for Accurately Estimating the Weight of Moving Vehicles Using Piezoelectric Sensors and Adaptive-footprint Tire Model

Summary This paper presents new methods for estimating the axle weight of a moving vehicle, using two piezoelectric sensors and adaptive-footprint tire model. It is more difficult to weigh vehicles in motion accurately than to weigh standing vehicles. The difficulties in weighing moving vehicles result from sensor limitations as well as dynamic loading effects induced by vehicle/pavement interactions. For example, two identical vehicles with the same weight will generate sensor signals that differ in the shape and the peak value, depending the tire pressure, vehicle speed, road roughness, and sensor characteristics. This paper develops a method that is much less sensitive to these variable factors in determining the axle weight of a moving vehicle. In the developed method, first the piezoelectric sensor signal is reconstructed using the inverse dynamics of a high-pass filter representing the piezoelectric sensor. Then, the reconstructed signal, is normalized, using the nominal road/tire contact length obtained using an adaptive-footprint tire model, and then integrated. Experiments are performed with 3 vehicles of known weight ranging from 1,400 kg to 28,040 kg. The developed method is compared to two other algorithms. Results show that the developed method is most consistent and accurate.