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.

Pedestrian lane formation with following–overtaking model and measurement of system order

Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.

ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet

The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field.However,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines.The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines.To address the aforementioned challenges,we propose a novel deep learning approach for lane line detection.This method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane lines.In 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 Hz.The programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://github.com/Duane 711/Lane-line-detec tion-ST-LaneNet.

Design Strategy of Collector-Distributor Lanes in Urban Interchanges

Based on the functions and characteristics of the interchange collector-distributor lanes,with Harbin as an example,it is proposed in this paper that the local characteristics and traffic flow characteristics should be considered in the design of the interchange collector-distributor lanes,which includes the analysis of function,location,and many other aspects,in hopes to provide reference for the design of collector-distributor lanes in other regions of our country.

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.

An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision

Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.

A Lane Change Model Considering the Stability of Cooperative Adaptive Cruise Control Platoon Fleet

In this article,lane change models for mixed traffic flow under cooperative adaptive cruise control(CACC)platoon formation are established.The analysis begins by examining the impact of lane changes on traffic flow stability.The influences of various factors such as lane change locations,timing,and the current traffic state on stability are discussed.In this analysis,it is assumed that the lane change location and the entry position in the adjacent lane have already been selected,without considering the specific intention behind the lane change.The speeds of the involved vehicles are adjusted based on an existing lane change model,and various conditions are analyzed for traffic flow disturbances,including duration,shock amplitude,and driving delays.Numerical calculations are provided to illustrate these effects.Additionally,traffic flow stability is factored into the lane change decision-making process.By incorporating disturbances to the fleet into the lane change income model,both a lane change intention model and a lane change execution model are constructed.These models are then compared with a model that does not account for stability,leading to the corresponding conclusions.

A new traffic model with the consideration of coupling effect with two lanes

This paper introduces a new two-lane high-order continuum model by embedding the two delay time scales con- tinuum traffic model presented by Xue （2003 Phys. Rev. E 68 066123） into the multi-lane model proposed by Daganzo （1997 Transpn. Res. B 31 83） with the consideration of the coupling effect between the vehicles of two lanes in instantaneous traffic situation and lane-change effect. In the novel model, the coupling effect of two lanes and phenomena of lane change, which were not discussed in Daganzo＇s model and Xue＇s model, are taken into account. Numerical simulation shows that it is in accordance with real traffic flow. This obviously indicates that the new phenomenon and behaviour are analogous results as single lane presented by Xue, and the proposed model is more reasonable on two lanes. Furthermore, the generation rate between two lanes is also investigated. The results show that the formation and diffusion of traffic shock wave can be better simulated on two lanes.

LaneSegNet:一种高效的车道线检测方法

车道线检测在智能交通领域占有重要地位,其检测的准确度和速度对于辅助驾驶以及自动驾驶有重要影响.针对目前深度学习方法识别车道线精度差、速度慢的问题,提出了一种高效的车道线分割方法LaneSegNet.首先基于编码和解码网络原理构建主干网络Lane-Net,用于提取车道线特征信息并分割出车道线;然后使用多尺度空洞卷积特征融合网络,可以极大地扩充模型的感受野,提取全局特征信息;最后使用混合注意力网络获取丰富的车道线特征,并增强与当前任务相关的信息.实验结果表明:在TuSimple数据集上,该方法检测车道线的准确率为97.6%;在CULane数据集上,该方法在标准路面的检测准确率达到92.5%,多种路面综合检测准确率为75.2%.本文提出的LaneSegNet车道线检测方法分割精确度和推理速度优于其他对比模型,且具有更强的适应性和鲁棒性.

Impact of viscosity modifier on asphalt properties used for bus rapid transit lanes in Chengdu

In order to find the effect of different viscosity modifier dosages on asphalt binder＇s performance in bus rapid transit lanes in the city of Chengdu, three different viscosity modifiers were analyzed： TAFPACK-super （TPS）, high-viscosity additive （HVA） and road-science- technology （RST）, and four different asphalt binders were investigated through laboratory experiments. The percent- ages of the viscosity modifiers used were： TPS （0%, 8%, 10%, 12%, 14% and 16%） and RST and HVA （8% and 12%） depending on the type of asphalt binder. Technical indicators of modifier asphalt were tested through con- ventional and unconventional binder tests. It has been found out that only a percentage greater than or equal to 14% TPS is reasonable to achieve the requirement set by 20,000 Pa. s for the 60℃ dynamic viscosity on local #70 grade asphalt. The results indicate that conventional bin- ders did not meet the requirements of the 60℃ dynamic viscosity when 12% of TPS or HVA modifiers were used. In addition, the B-type styrene-butadienne-styrene （SBS） modified asphalt binder has better viscosity balance than the A-type SBS modified when 8% of each of the three different kinds of viscosity modifiers is used. Therefore, the B-type modified SBS thus appears to be a suitable choice in asphalt mixtures for bus rapid transit lane with the 60℃ dynamic viscosity.

Capacity of Dual-Right-Turn Lanes at Signalized Intersections under Mixed Traffic Conditions

To explore the potential capacity of dual-fight-turn lanes at signalized intersections under mixed traffic conditions, we defined two conflict zones between right turn vehicles and through bicycle corresponding to different right turn flows from dual-right-turn lanes. Relationships between the arrival rate of bicycle group at each conflict zone and the saturation flow rate of right turn movement were investigated. A model based on gap acceptance theory was adopted to estimate the capacity of dual-right-turn lanes under mixed traffic conditions. An analysis was carried out using the collected data from three four-leg signalized intersections in Beijing, China, where the dual-right-turn lanes were used. In addition, we also discussed the patterns of bicycle lane in the urban area of Beijing, and classified it based on its characteristics in use. It is concluded that the two lanes of dual-fight-turn lanes produce different capacities under mixed traffic conditions, and the analysis on scenarios of dual-right-turn movement traversing bicycle traffic plays a key role in explaining the different capacity performance of the two right turn lanes. Error analysis of the model indicated that the model was rational.

Optimal allocation model of signalized intersections with dynamic use of exit lanes for left turn

In order to improve the operational efficiency of heavy left-turn demand intersections,an optimal allocation model of an intersection with dynamic use of exit lanes for left turns(EFL)is proposed.The constraints of setting EFL are analyzed,including the number and length of reverse variable lanes,flow direction constraints,and signal constraints,etc.The constraints and control variables are combined in a unified framework for simultaneous optimization.The objective functions are defined as the average delay and left-turn capacity of an intersection.The model is solved by a non-dominated genetic algorithm(NSGA-Ⅱ).The results show that after the optimal allocation of EFL,the average vehicle delays of the intersection can be reduced by 14.9%and left-turn capacity can be increased by 19.3%.The effectiveness of the optimal allocation model of EFL is demonstrated.

Chaotic time series prediction for surrounding rock's deformation of deep mine lanes in soft rock

Based on the measured displacements,the change laws of the effect of distance in phase space on the deformation of mine lane were analyzed and the chaotic time series model to predict the surrounding rocks deformation of deep mine lane in soft rock by nonlinear theory and methods was established.The chaotic attractor dimension(D) and the largest Lyapunov index(Emax) were put forward to determine whether the deformation process of mine lane is chaotic and the degree of chaos.The analysis of examples indicates that when D>2 and Emax>0,the surrounding rock's deformation of deep mine lane in soft rock is the chaotic process and the laws of the deformation can still be well demonstrated by the method of the reconstructive state space.Comparing with the prediction of linear time series and grey prediction,the chaotic time series prediction has higher accuracy and the prediction results can provide theoretical basis for reasonable support of mine lane in soft rock.The time of the second support in Maluping Mine of Guizhou,China,is determined to arrange at about 40 d after the initial support according to the prediction results.

Road Traffic Accidents (RTAs) Trends on Kathmandu-Bhaktapur Road after Addition of Lanes

Road traffic accidents are the outcome of the factors associated with the traffic system namely road users, road environment and vehicles. Despite good road surface, the Kathmandu-Bhaktapur road has high accident records. The traffic police record shows that 1530 accidents have occurred from July 2009 to June 2012. The area of study of this research is the Kathmandu-Bhaktapur road which starts at Tinkune Kathmandu and ends at Suryabinayak with a length of 9.1 kilometer, a section of Araniko highway heading towards China. The road is the first ever six lane road constructed in Nepal. The objectives of this research work are to identify locations with high accident numbers, to investigate possible causes of accidents, and to propose countermeasures for traffic safety along the Kathmandu-Bhaktapur road.

Analysis and Enlightenment on Continuation of Spatial Image of Traditional Streets and Lanes in Historical Urban Area:A Case Study of Spatial Design of Traditional Streets and Lanes in Xin’gan South Road of Ganzhou City

Traditional block of Xin’gan South Road in Ganzhou City is taken as the research object.Using urban design techniques,spatial structure,spatial interface,scale,ratio and spatial node of traditional streets and lanes are controlled and processed,and spatial image is analyzed.Moreover,the strategic transformation proposal is put forward.It promotes the renewal of traditional streets and lanes in urban renewal,and realizes the historical continuation of spatial image of traditional streets and lanes,thereby protecting the historical features of the ancient city.

Semiautomatic detection of lanes and bands in DNA gel electrophoresis images

The following article has been retracted due to the investigation of complaints received against it. The Editorial Board found that the same contents have been published in another journal at the same time. The scientific community takes a very strong view on this matter, and the Journal of Biomedical Science and Engineering treats all unethical behavior such as plagiarism seriously. This paper published in Vol.6 No.1 76-84, 2013 has been removed from this site. Title: Semiautomatic detection of lanes and bands in DNA gel electrophoresis images Authors: Ashraf K. Helmy, Ghada S.

Threshold values of traffic flow for the provision of exclusive bus lanes

A simulation network model was established using VISSIM software and verified by the T- test. The model took into consideration the road conditions, pedestrian crossing, traffic composi- tion, bus stops and traffic signal. The operating characteristics of buses and cars under different flow conditions were studied using the simulation model, and the speed-flow models of buses and cars were established based on the simulation results. Finally, the threshold values of traffic flow for the provision of exclusive bus lanes was determined with the target of optimal travel benefits （per capi- ta） , which would provide a basis for the planning and design of exclusive bus lanes on urban roads.

Research on the Construction Priority Model of Bus Lanes

Priority of public transport is one of the important means to enhance the attraction of ground public transportation, and also an important content in the construction of a public transport city. In recent years, Beijing has witnessed large-scale construction of Bus Lane. Based on the current situation that the construction of Bus Lane lacks scientific decision-making methods, this study establishes the time sequence evaluation indicator system for the construction of Bus Lane by analyzing the influencing factors, builds a ranking model of the importance degree of the construction of Bus Lane, and conducts case verification.

Enhancing Urban Mobility: Exploring the Potential of Exclusive Motorcycle Lane Using VISSIM

The proliferation of Mobility on Demand (MOD) services has ushered in a surge of ridesharing platforms, catalyzing the emergence of micro mobility solutions like motorcycle sharing. Consequently, motorcycles have witnessed unprecedented growth over recent decades. This proliferation, while offering convenience, has introduced challenges such as diminished road capacity, and compromised safety. This study advocates for the implementation of exclusive motorcycle lanes to mitigate the ensuing disorderliness using VISSIM microsimulation platform. Empirical data from a key corridor in Dhaka is harnessed to calibrate and simulate network performance scenarios—pre- and post-implementation of dedicated motorcycle lanes. The outcomes of our simulation experiments exhibit the implementation of dedicated motorcycle lanes leads to a reduction in vehicular throughput but improvement the flow of motorcycles. In addition, Surrogate Safety Measures (SSMs) demonstrate the safety improvements through implementation of the treatment.

Lane Line Detection Based on Improved PINet

Accurate perception of lane line information is one of the basic requirements of unmanned driving technology, which is related to the localization of the vehicle and the determination of the forward direction. In this paper, multi-level constraints are added to the lane line detection model PINet, which is used to improve the perception of lane lines. Predicted lane lines in the network are predicted to have real and imaginary attributes, which are used to enhance the perception of features around the lane lines, with pixel-level constraints on the lane lines;images are converted to bird’s-eye views, where the parallelism between lane lines is reconstructed, with lane line-level constraints on the predicted lane lines;and vanishing points are used to focus on the image hierarchy, with image-level constraints on the lane lines. The model proposed in this paper meets both accuracy (96.44%) and real-time (30 + FPS) requirements, has been tested on the highway on the ground, and has performed stably.