A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

Traffic light detection and recognition in intersections based on intelligent vehicle

To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.

Optimization of suspension system of heavy off-road vehicle for stability enhancement using integrated anti-roll bar and coiling spring mechanism

Short suspension system has an indispensable effect on vehicle handling and ride,so,optimization of vehicle suspension system is one of the most effective methods,which could considerably enhance the vehicle stability and controllability.Motion control,stability maintenance and ride comfort improvement are fundamental issues in design of suspension system of off-road vehicles.In this work,a dependent suspension system mostly used in off-road vehicles is modeled using Trucksim software.Then,geometric parameters of suspension system are optimized using integrated anti-roll bar and coiling spring in a way that ride comfort,handling and stability of vehicle are improved.The simulation results of suspension system and variations of geometric parameters due to road roughness and different steering angles are presented in Trucksim and effects of optimization of suspension system during various driving maneuvers in both optimized and un-optimized conditions are compared.The simulation results indicate that the type of suspension system and geometric parameters have significant effect on vehicle performance.

General Torsional Stiffness Matching of Off-road Vehicle

Increasing frame torsional stiffness of off-road vehicle will lead to the decrease of body torsional deformation, but the increase of torsional loads of frame and suspension system and the decrease of wheel adhesive weight. In severe case, a certain wheel will be out of contact with road surface. Appropriate matching of body, frame and suspension torsional stiffnesses is a difficult problem for off-road vehicle design. In this paper, these theoretically analytic models of the entire vehicle, body, frame and suspension torsional stiffness are constructed based on the geometry and mechanism of a light off-road vehicle＇s body, frame and suspension. The body and frame torsional stiffnesses can be calculated by applying body CAE method, meanwhile the suspension＇s rolling angle stiffness can be obtained by the bench test of the suspension＇s elastic elements. Through fixing the entire vehicle, using sole timber to raise wheels to simulate the road impact on a certain wheel, the entire vehicle torsional stiffness can be calculated on the geometric relation and loads of testing. Finally some appropriate matching principles of the body, frame and suspension torsional stiffness are summarized according to the test and analysis results. The conclusion can reveal the significance of the suspension torsional stiffness on off-road vehicle＇s torsion-absorbing capability. The results could serve as a reference for the design of other off-road vehicles.

Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water surfaces.For monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save time.It is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface vehicles.For this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental conditions.Although the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon maneuvering.In this study,a three-layer convolutional neural network is applied to classify types of surface vehicles.The aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification purposes.Hence,we have proposed a descriptor that converts the 3D point cloud data into 2D image data.To use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each object.We developed voxel-based clustering for the point cloud clustering.Furthermore,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the network.We intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the simulator.Furthermore,we explore the feasibility of real-time object classification within this framework.

Effect of the off-road terrains on the ride comfort of construction vehicles

In order to evaluate the impact of off-road terrains on the ride comfort of construction vehicles,a nonlinear dynamic model of the construction vehicles interacting with the terrain deformations is established based on Matlab/Simulink software.The weighted root mean square(RMS)acceleration responses and the power spectral density(PSD)acceleration responses of the driver s seat heave,the pitch and roll angle of the cab in the low-frequency region are chosen as objective functions under different operation conditions of the vehicle.The results show that the impact of off-road terrains on the driver s ride comfort and health is clear under various conditions of deformable terrains and range of vehicle velocities.In particular,the driver s ride comfort is greatly affected by a soil terrain while the comfortable shake of the driver is strongly affected by a sand terrain.In addition,when the vehicle travels on a poor soil terrain in the frequency range below 4 Hz,more resonance peaks of acceleration PSD responses occurred than that on a rigid road of ISO 2631-1 level C.Thus,the driver s health is significantly affected by the deformable terrain in a low-frequency range.

Light weight analysis of a skeleton vehicle frame using BS960 super-high-strength steel

Static strength finite element analysis was conducted to decrease the weight of a skeleton vehicle＇s frame. Results indicated that the maximum stress occurs on the front beam ＇s variable section area. Dynamic sensitivity analysis elucidated the relationship between the maximum stress and the thickness of a particular beam,e. g.,top,middle,and bottom beam. Displacement was analyzed by the key part that influenced the maximum stress. Finally,the new plan using BS960 super-high-strength beam steel and the preferred beam thickness was compared with the original plan. New combinations of beam thickness were introduced on the basis of different purposes; the maximum responding light w eight ratio was 21%.

Deep Learning-Based Control System Design for Emergency Vehicles through Intersections

This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable emergency vehicles to pass through intersections efficiently and safely. The research aims to develop a deep learning model that utilizes intersection violation monitoring cameras to identify emergency vehicles in real time. This system adjusts traffic signals to ensure the rapid passage of emergency vehicles while simultaneously optimizing the overall efficiency of the traffic system. In this study, OpenCV is used in combination with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to jointly complete complex image processing and analysis tasks, to realize the purpose of fast travel of emergency vehicles. At the end of this study, the principle of the You Only Look Once (YOLO) algorithm can be used to design a website and a mobile phone application (app) to enable private vehicles with emergency needs to realize emergency passage through the application, which is also of great significance to improve the overall level of urban traffic management, reduce traffic congestion and promote the development of related technologies.

Recognition algorithm for turn light of front vehicle

Intelligent vehicle needs the turn light information of front vehicles to make decisions in autonomous navigation. A recognition algorithm was designed to get information of turn light. Approximated center segmentation method was designed to divide the front vehicle image into two parts by using geometry information. The number of remained pixels of vehicle image which was filtered by the morphologic feaatres was got by adaptive threshold method, and it was applied to recognizing the lights flashing. The experimental results show that the algorithm can not only distinguish the two turn lights of vehicle but also recognize the information of them. The algorithm is quiet effective, robust and satisfactory in real-time performance.

Characteristic of Line-of-Sight in Infrastructure-to-Vehicle Visible Light Communication Using MIMO Technique

Visible Light Communication(VLC)technology is aggressive research for the next generation of communication.Currently,Radio Frequency(RF)communication has crowed spectrum.An Intelligent Transportation System(ITS)has been improved in the communication network for Vehicle-to-Vehicle(V2 V),Vehicle-to-Infrastructure(V2I),and Infrastructure-to-Vehicle(I2V)by using the visible light spectrum instead of the RF spectrum.This article studies the characterization of Line-of-Sight(LOS)optical performance in an Outdoor Wireless Visible Light Communication(OWVLC)system employing a Multiple-Input Multiple-Output(MIMO)technique for I2V communications in ITS regulations.We design the new configuration of the OWVLC-I2V system,which is an alternative approach to communication for I2V system at nighttime.The results show the Channel Impulse Response(CIR)of the LOS links in visible light communication for I2V system in ITS by investigating the receiver on the vehicle moving along the coverage communication area.Furthermore,the OWVLC-I2V system using the MIMO technique depicts the performance of throughput and Bit Error Rate(BER)vs.vehicle speed while the vehicle passes a street light.

The Dynamic Simulation of Rotary Inertia on Light vehicle-Slope I

According to formula we can simulate their driven force and acceleration on the slope.The mechanical formula is used to obtain force and theoretical dynamics in the slope.The driven force decreases when rotation increases.When power increases the acceleration increases.it reduces when its weight raises.It is found that the a will decrease as slope becomes high from 5 to 11°to 22°,which fit the formula too.Meantime as the radius is high from 0.3m to 0.4m to 0.47m a will be low.The needed force will increase as the slope decline becomes big at the same power.

Theoretical and Experimental Study on The Speed-up of Freight Train with Mixed Marshaling of Light and Heavy Vehicle

To study the influence of the speed-up of a freight train with mixed marshaling of light and heavy vehicles on the dynamic behavior,a dynamic model of the freight train was established based on the modular method of cyclic variables,and the dynamic behavior of the freight train was simulated and analyzed under different marshaling patterns,speeds and line conditions.On-site speed-up test with different marshaling freight trains was carried out,and the stability and ride-index of the train before and after the speed-up were compared and analyzed.The feasibility of increasing the speed of freight trains with mixed marshaling of light and heavy cars was demonstrated theoretically and experimentally.The results show that the theory is in good agreement with the test,which can effectively reflect the dynamic behavior of the vehicle.The dynamic behavior of the freight train in the study meets the requirements of increasing speed to 90 km/h.This paper provides a theoretical basis and method for railway freight transportation and the speed-up of freight vehicles.

Vehicle and terrain interaction based on Adams-Matlab co-simulation

A kind of construction truck model is built in Adams based on multi-body dynamic theory. The rigid and elastic wheels of tire-soil contact models are proposed based on the Bekker pressure model and the Jonasi shear soil model, and they are described in the form of S-function to enhance the calculation efficiency and simulation accuracy. Finally, the interaction of truck and soil is simulated by Adams-Maflab co-simulation to study the influence of soft terrain on the ride comfort of vehicles. The co-simulation results reveal that the terrain properties have a great influence on the ride comfort of vehicles as well as driving speed, road roughness and cargo weight. This co-simulation model is convenient for adding the factor of terrain deformation to the analysis of vehicle ride comfort. It can also be used to optimize suspension system parameters especially for off-road vehicles.

基于LightGBM的电动汽车行驶工况下电池剩余使用寿命预测

行驶工况下电动汽车锂离子电池剩余使用寿命(RUL)衰退情况复杂,准确的RUL预测可为电池的定期维护和安全稳定运行提供指导,避免安全隐患。为此,该文提出一种适用于行驶工况下电动汽车电池的RUL预测方法。首先,针对行驶工况,提出一种基于轻量型梯度提升机(LightGBM)的RUL预测模型,利用元学习超参数优化方法对其进行超参数调优;其次,搭建行驶工况下电池全生命周期容量测试系统,模拟行驶工况下电池所受振动应力、充放电应力环境和测试电池容量衰退情况;然后,基于动态时间规整对容量衰退的相似性分析结果,使用生成对抗网络(GAN)生成新的容量序列;最后,通过实验数据验证所提模型和生成容量序列的有效性。

某混动商用车油耗优化分析

商用车新能源化发展趋势明显,纯电和插电式混合动力汽车(PHEV)车型不断增多,混合动力汽车(HEV)商用车车型较少,且HEV商用车油耗研究较少。文章基于高效发动机和1挡后驱混合动力变速器,开发了适合轻型商用车的混合动力系统,并将该混合动力系统搭载于一款3.5 t的轻型商用车,通过试验的方法,在整车转毂试验台架对该轻型商用车油耗性能进行分析并优化。结果表明,通过整车控制策略与发动机油耗的优化,混合动力轻型商用车整车油耗由原来的8.9 L/100 km降低到8 L/100 km,整车油耗降低10%。通过优化,该车型达到开发目标,整车控制策略与发动机油耗是轻型混合动力商用车油耗的重要影响因素。

基于改进YOLOv8的无人机可见光小目标检测方法研究

目的 :为解决目前无人机可见光系统检测小目标时准确率和实时性低的问题,提出一种基于改进YOLOv8的可见光小目标检测方法。方法:选取由主干网络(Backbone)、颈部模块(Neck)和头部模块(Head)组成的YOLOv8网络作为基础框架构建AGC-YOLO模型。首先,在Backbone部分融入卷积注意力模块(convolutional block attention module,CBAM),提高模型的特征表达能力;其次,将部分传统卷积模块替换为可改变核卷积模块AKconv,减少网络参数量;最后,在Neck部分采用Gold-YOLO模块,提高对不同尺寸目标的检测能力。选用VisDrone2019数据集分别进行消融实验和对比实验,通过平均精度均值(mean average precision,mAP)、每秒传输帧数(frames per second,FPS)、每秒10亿次的浮点运算数(giga floating-point operations per second,GFLOPs)和参数量(parameters)评估AGC-YOLO模型对小目标检测的效果。结果:AGC-YOLO模型的FPS为31.90,GFLOPs和Parameters分别为9.20和11.30 M,达到无人机实时性的检测速度要求(FPS不低于30)。虽然AGC-YOLO模型的GFLOPs和Parameters比轻量化模型YOLOv8n、Ghost-YOLO和YOLO-BiFPN有所增加,但是mAP50(mAP50表示在交并比为0.5时的mAP)分别提高了15%、6%和5%。结论:提出的方法在提高检测速度、减少参数量、保障检测精度方面表现良好,在无人机可见光小目标检测方面具有良好的应用前景。

基于XGBoost与LightGBM集成的电动汽车充电负荷预测模型

随着电动汽车规模化发展,充电站负荷对电网造成一定影响,为保障电网平稳运行,提出一种基于极端梯度提升(eXtreme Gradient Boosting,XGBoost)与轻量级梯度提升机(Light Gradient Boosting Machine,LightGBM)融合的电动汽车充电负荷预测模型。该方法运用Stacking集成学习的策略:首先根据时间特征与历史负荷数据采用XGBoost与LightGBM算法构建负荷预测的基学习器,然后采用岭回归(Ridge Regression,RR)算法将基学习器的输出结果进行融合之后输出负荷预测值。为了对比多种不同的负荷预测模型,采用上海市嘉定区的充电站订单数据进行试验,结果表明,该方法所构建的负荷预测模型相比单一算法模型具有更高的预测准确度,对电网平稳运行有一定理论及实用价值。

Filtering algorithm of line structured light for long-distance obstacle detection

Since unmanned ground vehicles often encounter concave and convex obstacles in wild ground, a filtering algorithm using line structured light to detect these long distance obstacles is proposed. For the line structured light image, a ranked-order based adaptively extremum median （RAEM） filter algorithm on salt and pepper noise is presented. In the algorithm, firstly effective points and noise points in a filtering window are differentiated; then the gray values of noise points are replaced by the medium of gray values of the effective pixels, with the efficient points＇ gray values unchanged; in the end this algorithm is proved to be efficient by experiments. Experimental resuits demonstrate that the image blur, resulting into proposed algorithm can remove noise points effectively and minimize the protecting the edge information as much as possible.

Automotive Lighting Systems Based on Luminance/Intensity Grids: A Proposal Based on Real-Time Monitoring and Control for Safer Driving

The requirements for automotive lighting systems,especially the light patterns ensuring driver perception,are based on criteria related to the headlamps,rather than the light perceived by drivers and road users.Consequently,important factors such as pavement reflectance,driver age,or time of night,are largely ignored.Other factors such as presence of other vehicles,vehicle speed and weather conditions are considered by the Adaptive Driving Beam(ADB)and Adaptive Front-lighting System(AFS)respectively,though with no information regarding the visual perception of drivers and other road users.Evidently,it is simpler to simulate and measure the light emitted by the lamps than the light reflected by the pavement or emitted by other vehicles.However the current technology in cameras and light sensors,communication protocols,and control of Light Emitting Diodes(LED),combined with decision-making techniques applied to large amounts of data,can open a new era in the operation of headlamps and thus ensure the visual needs of drivers in real time and under actual road conditions.The solution lies in an interaction road-sensor-headlamp,which is not based on the light emitted by headlamps,but rather on the light perceived by the drivers.This study thus proposes a dual grid based on luminance and luminous intensity,which would manage the headlamps by optimizing driver perception and the safety of all road users.