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.

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.

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.

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 slip angle are fast.

Research on TDM approach of sharing BRT-lane supported by vehicle-infrastructure integration

In order to increase the availability of the part-time idle bus rapid transit lane(BRT-lane),a time division multiplexing(TDM) method to share BRT-lane with the vehicles besides BRT buses is proposed based on vehicle-road collaboration. The TDM control strategy is established under the circumstance of vehicle-infrastructure integration(VII). The algorithm is given to forecast the segmented BRT travel time. According to the real time traffic information,a comprehensive model is given to estimate the vehicles' lane-changing time from/to the BRTlane to/from its neighbor lane and determine the timing sequence for vehicles collaboration. Finally,the experiment demonstrates that the predicted value of the travel time and lane-changing time is much close to the true value. The control strategy of the vehicles collaboration could promise the non-BRT vehicles to share BRT-lane without disturbing BRT's priority.

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.

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.

基于时域卷积网络与注意力机制的车辆换道轨迹预测模型

精准的车辆轨迹预测模型可以为自动驾驶车辆提供其周围车辆的准确运动状态信息,进而判断本车与周围车辆短期内是否有发生冲突的可能性。本文提出一种基于时域卷积网络与注意力机制(Temporal Convolutional Networks with Attention mechanism,TCN-Attention)的车辆换道轨迹预测模型。该模型以时域卷积网络作为当前输入的特征提取器,利用时间与空间注意力机制使模型在不同时间和空间位置之间建立动态关联,更准确地捕捉车辆之间的动态时空相关性,实现准确预测车辆换道轨迹。与传统单一车辆轨迹特征输入不同,本文通过对输入特征进行多维扩充与融合,进一步提高了轨迹预测准确率。此外,本文提出一种换道执行起止时刻定义方法更准确地确定数据集中的换道起止时刻。实验表明,本文所提模型能以高准确率预测变换车道轨迹,在整体效果上优于其他深度学习模型,与ConvLSTM (Convolution Long Short-Term Memory)相比,TCN-Attention的平均绝对误差(Mean Absolute Error,E_(MAE))降低了69.8%,均方根误差(Root Mean Square Error,E_(RMSE))降低了49.15%,平均绝对百分比误差(Mean Absolute Percentage Error,E_(MAPE))降低了14.24%。

计及安全性与舒适性的智能车辆换道轨迹规划研究

针对当前智能车辆在换道过程中的安全性以及乘员舒适性问题,本文提出一种基于风险场评估轨迹的二次筛选方法。首先,在Frenet坐标系下,将车辆运动解耦为横向和纵向两个维度,基于五次多项式生成所有横向d-t曲线簇和纵向s-t曲线簇;其次,由车辆的动力学特性和三圆碰撞模型设计轨迹初筛评价函数,选取合格轨迹作为候选轨迹;最后,参考人工势场理论的思想,引入行车过程中风险场的概念,根据换道效率,换道风险值和横、纵向冲击度建立总损失函数评价候选轨迹以进行二次筛选,选取最佳轨迹并完成可视化。为检验算法的可行性,通过搭建双车道的道路环境,设计障碍车不同速度和加速度的多场景进行弯道换道的仿真验证。研究结果表明,本文所提算法能够满足换道的安全性和舒适性需求。同时,在正常换道场景下,乘员在换道过程的97.5%时间处于舒适状态,在紧急避障场景下也能达到平衡安全性与舒适性的目标。

基于驾驶场景与决策规则的智能汽车换道决策

复杂交通环境中,换道决策直接影响智能汽车自主换道效果,然而在换道决策过程中依旧存在着预测正确率低以及决策安全的问题。因此,针对这一问题,提出了基于驾驶场景和决策规则的换道决策模型。考虑换道后的交通行驶状况对换道决策的影响,引入换道后的期望速度和换道前后与前车的距离作为新的特征变量,基于特征变量与换道决策的相关性建立了换道决策规则。建立了模拟真实驾驶环境的换道场景数据集,扩充了NGSIM换道场景数据集,并对其进行了有效性验证。针对换道决策的多参数和非线性问题,提出了基于贝叶斯优化核函数的支持向量机模型,在换道场景数据集上进行测试验证。结果表明:新引入的决策特征变量对换道行为有积极作用,换道场景数据集能够模拟真实的换道场景,可进一步应用到换道决策和轨迹规划的研究中,支持向量机模型对换道行为的预测正确率达95.40%,高于其他机器学习分类器,提高了换道行为的安全性。

混合流下非机动车道基本需求宽度的设计

为弥补在非机动车道设计方面对电动自行车考虑不足的情况,有必要对由人力/电动自行车组成的饱和混合流下非机动车道基本需求宽度进行研究。以非机动车饱和混合流为研究对象,从非机动车的物理运动宽度组成出发,通过构建多重线性回归模型分析了非机动车物理运动宽度的影响因素,对分类自变量进行组合筛选;建立了不同组合类型下的非机动车速度与运行摆幅、路侧安全净距线性模型,得到性别为女性、年龄为老年和车型为电动自行车为最不利组合的结论;根据非机动车道的85%位运营速度确定最不利组合下的非机动车物理运动宽度,进而确定非机动车道基本需求宽度;针对非机动车道宽度合理性验证的准确性问题,从稳定性角度提出了运用速度熵来验证基本需求宽度合理性的方法,并以多条非机动车道路段为例进行了实测验证。研究结果表明:在隔离栏和划线下,非机动车单车道基本需求宽度分别为1.65、1.50 m;在绿化带、隔离栏和划线下,非机动车两车道基本需求宽度分别为2.90、2.80、2.65 m。在同一车道数和隔离方式下,车道宽度越宽,交通流速度熵越高;随着非机动车道宽度增加,以基本需求宽度为拐点,非机动车道的交通流速度熵变化幅度增大,稳定性更差。

基于合作博弈的网联车辆合流序列优化策略

为提升道路合流区交通通行效率,提出了一种基于合作博弈的网联车辆合流序列优化决策算法。首先,构建包含油耗、舒适度的多优化目标联合成本函数,通过分析多人合作博弈问题推导出合流区全局最小成本策略;然后,将多车合作博弈分解为多个两车合作博弈,预测不同策略的最小合流成本,利用成本矩阵法求得两车博弈的帕累托有效解,确定最优合流序列;最后,在三车道合并至一车道合流场景下,MATLAB仿真结果表明,上述优化合流序列能有效降低油耗,缩短出行时间,提高乘客的舒适性。