Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Evaluating impact of remote-access cyber-attack on lane changes for connected automated vehicles

Connected automated vehicles(CAVs)rely heavily on intelligent algorithms and remote sensors.If the control center or on-board sensors are under cyber-attack due to the security vulnerability of wireless communication,it can cause significant damage to CAVs or passengers.The primary objective of this study is to model cyberattacked traffic flow and evaluate the impacts of cyber-attack on the traffic system filled with CAVs in a connected environment.Based on the analysis on environmental perception system and possible cyber-attacks on sensors,a novel lane-changing model for CAVs is proposed and multiple traffic scenarios for cyber-attacks are designed.The impact of the proportion of cyber-attacked vehicles and the severity of the cyber-attack on the lanechanging process is then quantitatively analyzed.The evaluation indexes include spatio-temporal evolution of average speed,spatial distribution of selected lane-changing gaps,lane-changing rate distribution,lane-changing preparation search time,efficiency and safety.Finally,the numerical simulation results show that the freeway traffic near an off-ramp is more sensitive to the proportion of cyber-attacked vehicles than to the severity of the cyber-attack.Also,when the traffic system is under cyber-attack,more unsafe back gaps are chosen for lane-changing,especially in the center lane.Therefore,more lane-changing maneuvers are concentrated on approaching the off-ramp,causing severe congestions and potential rear-end collisions.In addition,as the number of cyber-attacked vehicles and the severity of cyber-attacks increase,the road capacity and safety level will rapidly decrease.The results of this study can provide a theoretical basis for accident avoidance and efficiency improvement for the design of CAVs and management of automated highway systems.

Vision-based long-distance lane perception and front vehicle location for full autonomous vehicles on highway roads

A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.

Direct adaptive control for lane keeping in intelligent vehicle systems

In this paper, with parametric uncertainties such as the mass of vehicle, the inertia of vehicle about vertical axis, and the tire cornering stiffness, we deal with the vehicle lateral control problem in intelligent vehicle systems. Based on the dynamical model of vehicle, by applying Lyapunov function method, the control problem for lane keeping in the presence of parametric uncertainty is studied, the direct adaptive algorithm to compensate for parametric variations is proposed and the terminal sliding mode variable structure control laws are designed with look-ahead references systems. The stability of the system is investigated from the zero dynamics analysis. Simulation results show that convergence rates of the lateral displacement error, yaw angle error and slid angle are fast.

LSTM-based lane change prediction using Waymo open motion dataset: The role of vehicle operating space

Lane change prediction is critical for crash avoidance but challenging as it requires the understanding of the instantaneous driving environment.With cutting-edge artificial intelligence and sensing technologies,autonomous vehicles(AVs)are expected to have exceptional perception systems to capture instantaneously their driving environments for predicting lane changes.By exploring the Waymo open motion dataset,this study proposes a framework to explore autonomous driving data and investigate lane change behaviors.In the framework,this study develops a Long Short-Term Memory(LSTM)model to predict lane changing behaviors.The concept of Vehicle Operating Space(VOS)is introduced to quantify a vehicle's instantaneous driving environment as an important indicator used to predict vehicle lane changes.To examine the robustness of the model,a series of sensitivity analysis are conducted by varying the feature selection,prediction horizon,and training data balancing ratios.The test results show that including VOS into modeling can speed up the loss decay in the training process and lead to higher accuracy and recall for predicting lane-change behaviors.This study offers an example along with a methodological framework for transportation researchers to use emerging autonomous driving data to investigate driving behaviors and traffic environments.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

A Novel Ego Lanes Detection Method for Autonomous Vehicles

Autonomous vehicles are currently regarded as an interesting topic in the AI field.For such vehicles,the lane where they are traveling should be detected.Most lane detection methods identify the whole road area with all the lanes built on it.In addition to having a low accuracy rate and slow processing time,these methods require costly hardware and training datasets,and they fail under critical conditions.In this study,a novel detection algo-rithm for a lane where a car is currently traveling is proposed by combining simple traditional image processing with lightweight machine learning(ML)methods.First,a preparation phase removes all unwanted information to preserve the topographical representations of virtual edges within a one-pixel width around expected lanes.Then,a simple feature extraction phase obtains only the intersection point position and angle degree of each candidate edge.Subsequently,a proposed scheme that comprises consecutive lightweight ML models is applied to detect the correct lane by using the extracted features.This scheme is based on the density-based spatial clustering of applications with noise,random forest trees,a neural network,and rule-based methods.To increase accuracy and reduce processing time,each model supports the next one during detection.When a model detects a lane,the subsequent models are skipped.The models are trained on the Karlsruhe Institute of Technology and Toyota Technological Institute datasets.Results show that the proposed method is faster and achieves higher accuracy than state-of-the-art methods.This method is simple,can handle degradation conditions,and requires low-cost hardware and training datasets.

Investigation and Optimization of Priority for Non-motor Vehicles in the Old Urban Area of Nanchang

Under the background of"people-oriented"thought and"green transportation",the idea of"priority for non-motor vehicles"came into being,which can improve the riding environment of non-motor vehicle riders to a certain extent.According to the current situation of priority for non-motor vehicles in the old urban area of Nanchang,through field investigation,questionnaire investigation and interview,this study summarized the existing problems,and put forward optimization suggestions for these problems,in order to provide reference for areas with similar conditions.

In-Vehicle Stereo Vision Systems with Improved Ant Colony Optimization Based Lane Detection: A Solution to Accidents Involving Large Goods Vehicles Due to Blind Spots

This paper presents an in-vehicle stereo vision system as a solution to accidents involving large good vehicle due to blind spots using Nigeria as a case study. In this paper, a stereo-vision system was attached to the front of Large Good Vehicles (LGVs) with a view to presenting live feeds of vehicles close to the LGV vehicles and their distance away. The captured road images using the stereo vision system were optimized for effectiveness and optimal vehicle maneuvering using a modified metaheuristics algorithm called the simulated annealing Ant Colony Optimization (saACO) algorithm. The concept of simulated annealing is strategies used to automatically select the control parameters of the ACO algorithm. This helps to stabilize the performance of the ACO algorithm irrespective of the quality of the lane images captured in the in-vehicle vision system. The system is capable of notifying drivers through lane detection techniques of blind spots. This technique enables the driver to be more aware of what surrounds the vehicle and make decisions early. In order to test the system, the stereo-vision device was mounted on a Large good vehicle, driven in Zaria (a city in Kaduna state in Nigeria), and data were in the record. Out of 180 events, 42.22% of potential accident events were caused by Passenger Cars, while 27.22%, 18.33% and 12.22% were caused by two-wheelers, Large Good Vehicles and road users, respectively. In the same vein, the in-vehicle lane detection system shows a good performance of the saACO-based lane detection system and gives a better performance in comparison with the standard ACO method.

Connected and Autonomous Vehicles (CAVs) Challenges with Non-motorized Amenities Environments

With the deployment of Connected and Automated Vehicles in the coming decades,road transportation will experience a significant upheaval.CAVs(Connected and Autonomous Vehicles)have been a main emphasis of Transportation and the automotive sector,and the future of transportation system analysis is widely anticipated.The examination and future devel­opment of CAVs technology has been the subject of numerous researches.However,as three essential kinds of road users,pedestrians,bicyclists,and motorcyclists have experienced little to no handling.We explored the influ­ence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.

Speed prediction models for car and sports utility vehicleat locations along four-lane median divided horizontal curves

Sites with varying geometric features were analyzed to develop the 85 th percentile speed prediction models for car and sports utility vehicle(SUV) at 50 m prior to the point of curvature(PC), PC, midpoint of a curve(MC), point of tangent(PT) and 50 m beyond PT on four-lane median divided rural highways. The car and SUV speed data were combined in the analysis as they were found to be normally distributed and not significantly different. Independent parameters representing geometric features and speed at the preceding section were logically selected in stepwise regression analyses to develop the models. Speeds at various locations were found to be dependent on some combinations of curve length, curvature and speed in the immediately preceding section of the highway. Curve length had a significant effect on the speed at locations 50 m prior to PC, PC and MC. The effect of curvature on speed was observed only at MC. The curve geometry did not have a significant effect on speed from PT onwards. The speed at 50 m prior to PC and curvature is the most significant parameter that affects the speed at PC and MC, respectively. Before entering a horizontal curve, drivers possibly perceive the curve based on its length. Longer curve encourages drivers to maintain higher speed in the preceding tangent section. Further, drivers start experiencing the effect of curvature only after entering the curve and adjust speed accordingly. Practitioners can use these findings in designing consistent horizontal curve for vehicle speed harmony.

Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds

The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation （CA） of the four-wheel-distributed trac- tion/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control （MC） problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive con- trol （NMPC） algorithm is introduced to handle the non- linear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control （TSMC） based path fol- lower. As revealed through off-line simulations, the hier- archical methodology brings nearly 1700% improvement in computational efficiency without loss of control per- formance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane- change （DLC） test results show that by using the optimalpredictive controller, the root-mean-square （RMS） values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration （OPA） driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9~. This research proposes a novel motion controller, which provides the full drive-by-wire vehicle with better lane-keeping and colli- sion-avoidance capabilities during autonomous driving.

Designing Right-Turn Vehicle Box as a Supplemental Treatment to Eliminate Conflicts with Pedestrians and Bicycles

Field observations illustrated that, right-turn vehicles stopped at various positions when proceeding within the right-turn lanes, while some of them trespassed on the crosswalks with multiple stops. In this case, pedestrians and bikes (ped/bike) are encountered unsmooth and hazardous crossings when right-turn vehicles encroaching their lanes. Meanwhile, this also causes conflicts between right-turn and through vehicles at the crossing street. To better protect ped/bike at crossings with right-turn vehicles, this paper proposes a concept of “right-turn vehicle box” (RTVB) as a supplemental treatment within right-turn lanes. Sight distance, geometric conditions, and behaviors of vehicles and ped/bike are key factors to consider so as to set up the criteria and to design the suitable treatment. A case study was conducted at an intersection pair in Houston, USA to shape the idea of RTVB, together with driving simulator tests under relevant scenarios. The preliminary crosscheck examination shows that the right-turn vehicle box could possibly provide ped/ bike with smoother and safer crossings. In the interim, the safety and efficiency of right-turn operations were also improved. To further validate the effects, implementation studies should be conducted before the RTVB can make its debut in practice. Future works will focus on the complete warrants and design details of this treatment. Moreover, the concept of “vehicle box” could also be transplanted to other places where turning movement(s) needs assistance or improvements.

Analysis of the Effect of Trailer Tire Size on the Articulated Vehicle’s Stability

This research paper aims to identify the effect of tire size on the handling characteristics of a trailer attached to a vehicle. In various stability tests, different models with different tires from the market were tested. A successful outcome of this research would generate an efficient tire selection process and improve the handling of a trailer attached to a vehicle while maximizing fuel efficiency. In this study, different accurate tire models using the magic formula were developed in vehicle dynamics modelling and simulation software. These models were then simulated on on-road conditions to predict vehicle and trailer behaviour under different conditions within the software. Two distinct tests were conducted, the J-Turn test and the Double Lane change test. The results of these tests were used to evaluate the handling characteristics and decide on a better tire size for the trailer attached to the vehicle.

Factors Influence Hanoians’ Choice of Non-Motorized Transport Mode to Access Bus Stations

Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized transport, citing mobility and safety concerns, exacerbated by insufficient pedestrian infrastructure. This study examines the motivations behind this reliance on motorized vehicles, particularly motorcycles, in Hanoi. Findings reveal safety and convenience as primary factors driving motorized transport use, especially for accessing bus stations. Economic incentives could promote non-motorized travel and public transport adoption. Policy implications highlight the importance of addressing economic factors and improving access infrastructure to manage motorized vehicle reliance and foster sustainable urban mobility in Hanoi.

交通场景下的大型车辆右转停车检测方法研究

随着大型车辆右转必停的新交规逐步实施,针对现有的监管方式存在效率较低的问题,提出了一种大型车辆右转停车检测方法。对YOLOv7-tiny检测模型进行改进,在ELAN模块中引入注意力机制,以提升大型车辆的检测性能。设计了一种新的大型车辆右转车道定位方法,通过自适应探测车道,提高了其在不同监控视角下的定位准确性。对大型车辆的运动轨迹进行序列数据转换,并采用结合加权平均的中值滤波算法,有效降低了原始数据的噪声。在滑动窗口损失函数中引入权重因子,进一步提升了检测方法的鲁棒性。实验结果表明,改进后的大型车辆检测模型在自制数据集和公开数据集上均取得了性能提升,大型车辆右转车道定位方法具有更强的泛化能力,并且检测到的停车时间覆盖率达到了97.4%。

基于模糊MPC的自主车辆队列转向换道协同控制系统设计

传统的自主车辆队列控制方法主要依赖于简单的规则和传感器信息,在复杂场景下往往难以达到理想的稳定性和实时性。为提高自主车辆队列在不同工况下执行转向换道时的行驶稳定性和实时性,提出基于模糊模型预测控制(Model-based Predictive Control,MPC)的自主车辆队列转向换道协同控制系统设计。该系统的硬件结构包括电源管理模块、电机驱动模块、传感器采集模块以及通信模块等。基于硬件结构建立自主车辆队列数学模型,获取自主车辆队列的行驶特性。在MPC控制器中引入模糊控制策略完成系统的软件设计,实现自主车辆队列转向换道的协同控制。实验结果表明,所提方法控制精度和实时性高,且能够有效提高车辆的行驶稳定性。

城市道路人行道与非机动车道分离设计研究

随着城市化进程加快,道路交通压力日益增大,人行道与非机动车道的分离设计成为改善城市交通环境的重要手段。通过文献分析、实地调研和案例研究,探讨了人行道与非机动车道分离的必要性、设计原则和具体方法。研究表明,合理的分离设计能有效提高道路通行效率,保障行人和非机动车使用者的安全。建议采用物理隔离、高差设计等方式实现有效分离,并结合绿化带、休憩设施等提升整体环境品质。研究成果可为城市道路规划与改造提供参考。

深部大断面软岩底抽巷快速掘进关键技术研究

针对煤矿巷道岩巷掘进效率低下的问题,以赵固二矿14050工作面下顺槽底抽巷为研究对象,开展了深部大断面软岩底抽巷快速掘进技术的研究。分析了当前底抽巷掘进技术存在的效率低、安全隐患大、作业人员多及劳动强度大等问题,并明确了工艺优化方向。随后,基于EBZ318(H)悬臂式掘进机和CMMD4-35煤矿用单轨液压锚杆钻车等先进设备,提出了“掘-支-锚”快速掘进成套装备方案和优化施工工艺,实现了掘进与支护工序的平行作业,显著提高了巷道掘进效率。本研究成果为类似地质条件下的煤矿巷道快速掘进提供了可借鉴的技术方案。

Real-Time Lane Detection for Driver Assistance System

Traffic problem is more serious, as the number of vehicles is growing. Most of the road accidents were caused by carelessness of drivers. To reduce the number of traffic accidents and improve the safety and efficiency of traffic for many years around the world and company studies have been conducted on intelligent transport systems (ITS). Intelligent vehicle, (IV) the system is part of a system which is designed to assist drivers in the perception of any dangerous situations before, to avoid accidents after sensing and understanding the environment around itself. In this paper, it proposes architecture for driver assistance system based on image processing technology. To predict possible Lane departure, camera is mounted on the windshield of the car to determine the layout of roads and determines the position of the vehicle on line Lane. The resulting sequence of images is analyzed and processed by the proposed system, which automatically detects the Lane lines. The results showed of the proposed system to work well in a variety of settings, In addition computer response system is inexpensive and almost real time.