Highway Vehicle Research Articles

Design and Analysis of the Trajectory of an Overtaking Maneuver Performed by Autonomous Vehicles Operating with Advanced Driver-Assistance Systems (ADAS) and Driving on a Highway

Overtaking is a maneuver that consists of passing another vehicle traveling on the same trajectory, but at a slower speed. Overtaking is considered one of the most dangerous, delicate and complex maneuvers performed by a vehicle, as it requires a quick assessment of the distance and speed of the vehicle to be overtaken, and also the estimation of the available space for the maneuver. In particular, most drivers have difficulty overtaking a vehicle in the presence of vehicles coming on other trajectories. To solve these overtaking problems, this article proposes a method of performing safe, autonomous-vehicle maneuvers through the PreScan simulation program. In this environment, the overtaking-maneuver scenario (OMS) is composed of highway infrastructure, vehicles and sensors. The proposed OMS is based on the solution of minimizing the risks of collision in the presence of any oncoming vehicle during the overtaking maneuver. It is proven that the overtaking maneuver of an autonomous vehicle is safe to perform through the use of advanced driver-assistance systems (ADAS) such as adaptive cruise control (ACC) and technology-independent sensors (TIS) that detect the driving environment of the maneuver.

Highway Vehicle Route Reconstruction Using Sparse and Noisy Communication Base Station Fingerprints

The current application of highway toll system generally uses the Dijkstra algorithm to calculate the shortest path of vehicles from the entrance to the exit to charge. This means that managers have no way of knowing the exact route of vehicles. Also, different routes of highways are often funded and operated by different investors. To address this problem, this article presents a new algorithm to reconstruct trajectories from sparse and noisy fingerprint signals from communication base stations identifications (CBSIDs), with practical application in a high-speed toll collection system in Hubei Province, China. In this solution, we use an inexpensive device that collects signal fingerprint identification numbers from CBSIDs at a low sampling rate. These CBSIDs are then matched with a special CBSID-anchor radiomap, converting the sequence of CBSIDs into a sequence of candidate anchors (toll stations and intersections on highways). Finally, a route mapping algorithm is run to process these candidate anchors and to generate the complete driving route. In the experiment on both simulated and field routes, results show that the proposed algorithm can effectively reconstruct the driving routes of vehicles. The upgraded toll collection system meets the needs of efficient motorway investment, maintenance, and management.

Research on the Safety of the Left Hard Shoulder in a Multi-Lane Highway Based on Safety Performance Function

The left hard shoulder plays an important role in the event of an emergency on the inside of a multi-lane highway, but past studies have not been able to clarify the criteria for its installation or quantify the safety impact of its installation on the left side. In order to study the influence of the left hard shoulder on the safety of vehicles traveling on multi-lane highways, based on past studies that only studied the situation of four-lane highways, this paper firstly constructs a multi-lane highway simulation model under different numbers of lanes based on the VISSIM traffic simulation and uses Surrogate Safety Assessment Model (SSAM) to study the conflict characteristics of multi-lane highway vehicles under different numbers of lanes. Based on the above findings, this paper introduces the Safety Performance Function (SPF) to construct a multi-lane freeway accident prediction model, calibrates the model by adding the indexes affected by the left side hard shoulder to the basic prediction mode, and uses the historical accident data of the Badou-Shihu section of the Guangdong Northern Second Ring Highway as the basis to study the differences in accident rates of the investigated section before and after setting the left hard shoulder. The study showed that the average Time to Collision (TTC) increased by 57.2%, Maximum Deceleration (MaxD) increased by 19.2%, and Delta Speed (DeltaS) increased by 15.3% after setting hard shoulders on the left side of multi-lane freeways, and traffic conflicts on multi-lane freeways were significantly reduced, and safety was improved considerably. In addition, the rear-end conflict rate decreased by 0.17%, 0.75%, and 4.6% after setting hard shoulders on the left side of one-way three, four, and five lanes, respectively, indicating that hard shoulders on the left side are the most effective in improving the safety of one-way five-lane freeways. The accident prediction results show that within the reasonable setting range of the left hard shoulder width (0~4 m), the accident rate decreases by about 1.5% for every 0.5 m increase if only the influence of the left hard shoulder width is considered. Without considering other factors, increasing the width of the hard shoulder on the left side can reduce the number of accidents. This indicates a significant safety improvement for a one-way five-lane highway after setting the hard shoulder on the left side, and the conclusion is consistent with the simulation results. In this paper, based on past research, the research object is extended to one-way three-, four-, and five-lane highways. The findings of this paper can help the road authorities develop specifications for installing hard shoulders on the left side of multi-lane freeways and adopt strategies to improve the traffic safety level of multi-lane freeways. In addition, the models and methods used in this paper can also help build a framework for future intelligent networked vehicle avoidance systems and promote the development of intelligent networked technologies.

Highway Vehicles’ Overtaking Classification

Abstract Highway users can apprehension to certain subjects with utilizing of Vehicular Ad-hoc Networks (VANET) applications if the rules for safe overtaking movement are violated to make the lane change maneuver between vehicles on the highway road. In our research, we suggest an algorithm for semi-automated vehicles S-AV compliant lane change to emphasize rules for safe overtaking between vehicles on the highway. The proposed algorithm technique classify the safe overtaking into major categories and critically analyzed them depending on various classes of lane change movements between vehicles interrelated to road condition based on different performance criteria; this technique will add awareness to drivers traveling on highway to increasing the comfort and safety of driving. Finally, we have conclude and suggest research issues associated to Vehicular Ad-hoc Networks to investigate and ensure the real-time decision of safe overtaking between vehicles on the highway, which is important research task to motivate researchers to connect the semi-automated vehicles with driver face emotion detection and increase driving safety.

How do the smart travel ban policy and intercity travel pattern affect COVID-19 trends? Lessons learned from Iran.

COVID-19, as the most significant epidemic of the century, infected 467 million people and took the lives of more than 6 million individuals as of March 19, 2022. Due to the rapid transmission of the disease and the lack of definitive treatment, countries have employed nonpharmaceutical interventions. This study aimed to investigate the effectiveness of the smart travel ban policy, which has been implemented for non-commercial vehicles in the intercity highways of Iran since November 21, 2020. The other goal was to suggest efficient COVID-19 forecasting tools and to examine the association of intercity travel patterns and COVID-19 trends in Iran. To this end, weekly confirmed cases and deaths due to COVID-19 and the intercity traffic flow reported by loop detectors were aggregated at the country’s level. The Box-Jenkins methodology was employed to evaluate the policy’s effectiveness, using the interrupted time series analysis. The results indicated that the autoregressive integrated moving average with explanatory variable (ARIMAX) model outperformed the univariate ARIMA model in predicting the disease trends based on the MAPE criterion. The weekly intercity traffic and its lagged variables were entered as covariates in both models of the disease cases and deaths. The results indicated that the weekly intercity traffic increases the new weekly COVID-19 cases and deaths with a time lag of two and five weeks, respectively. Besides, the interrupted time series analysis indicated that the smart travel ban policy had decreased intercity travel by around 29%. Nonetheless, it had no significant direct effect on COVID-19 trends. This study suggests that the travel ban policy would not be efficient lonely unless it is coupled with active measures and adherence to health protocols by the people.

A highway vehicle routing dataset during the 2019 Kincade Fire evacuation

As the threat of wildfire increases, it is imperative to enhance the understanding of household evacuation behavior and movements. Mobile GPS data provide a unique opportunity for studying evacuation routing behavior with high ecological validity, but there are little publicly available data. We generated a highway vehicle routing dataset derived from GPS trajectories generated by mobile devices (e.g., smartphones) in Sonoma County, California during the 2019 Kincade Fire that started on October 23, 2019. This dataset contains 21,160 highway vehicle routing records within Sonoma County from October 16, 2019 to November 13, 2019. The quality of the dataset is validated by checking trajectories and average travel speeds. The potential use of this dataset lies in analyzing and modeling evacuee route choice behavior, estimating traffic conditions during the evacuation, and validating wildfire evacuation simulation models.

Video-Based Vehicle Counting and Analysis using YOLOv5 and DeepSORT with Deployment on Jetson Nano

In recent years, the advancements in deep learning and high-performance edge-computing systems have increased tremendously and have become the center of attention when it comes to the analysis of video-based systems on edge by making use of computer vision techniques. Intelligent Transportation Systems (ITS) is one area where deep learning can be used for several tasks including highway-based vehicle counting systems whereby making use of computer vision techniques, an edge computing device and cameras installed in specific locations on the road, we are able to obtain very accurate vehicle counting results and replace the use of traditional and laborious hardware devices with modern low-cost solutions. This paper proposes and implements a modern, compact, and reliable vehicle counting system based on the most recent and popular object detection algorithm as of writing this paper, known as the YOLOv5, combined with a state-of-the-art object tracking algorithm known as DeepSORT. The YOLOv5 will be used in the following system to detect and classify four different classes of vehicles, whereas DeepSORT will be used to track those vehicles across different frames in the video sequence. Finally, a unique and efficient vehicle counting method will be implemented and used to count tracked vehicles across the highway scenes. A new highway vehicle dataset consisting of four vehicle classes, namely: car, motorcycle, bus, and truck, was collected, cleaned, and annotated with a total of 11,982 images published in the following study and used for the training of our robust vehicle detection model. From the results obtained over real-world highway surveillance videos, the following system was able to obtain an average vehicle detection mAP score of 96.1% and a vehicle counting accuracy of 95.39%, all while being able to be deployed on a compact Nvidia Jetson Nano edge-computing device with an average speed of 15 FPS which outperforms other previously proposed tools in terms of both accuracy and speed.

Expressway Information Collection Processing and Control Technology Based on RFID Internet of Things

In order to solve the problems of highway traffic congestion and frequent traffic accidents, a technology method for highway information collection, processing, and control based on RFID Internet of Things is proposed. The highway information collection and processing control technology designed in this paper is based on radio frequency identification technology to realize the collection of highway vehicle information, analyze, and process the collected information, and finally get the road condition information. Through the extraction and analysis of the traffic flow information in the video surveillance, the background difference method is used to extract the relevant vehicle factors in the video, including the extraction of traffic flow information, vehicle model extraction, and vehicle speed extraction. Traffic detection provides convenience. The experimental data shows that from Monday to Friday on weekdays, the road congestion rates are 2.72%, 1.94%, 2.77%, 2.27%, and 3.47% respectively; the average vehicle speed on weekdays is 89.96 km/h, and the test results are fast and accurate. The proposed method integrates road information collection and road information service, realizes traffic detection when there are vehicles, and realizes detection and perception of large-scale road traffic systems. Travelers provide real-time traffic information and navigation services.

A scheduling strategy for a new energy highway integrated network with clean green energy synergy

Purpose Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO2 emission reduction of highways. Design/methodology/approach To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO2 emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility. Findings The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO2 emissions. Originality/value This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.

강원 영동지역 관광경기가 지역경제에 미치는 영향과 시사점

This study develops a tourism business cycle index of Yeongdong area, and Naver search term trends by city and county in Yeongdong area, Gangwon-do using high-frequency data such as credit card usage amount, Yeongdong area highway tollgate vehicle traffic, navigation Yeongdong area destination searches, KT mobile communication data based on visitors to Yeongdong area. Calculate the index. A statistically high correlation was estimated between the calculated tourism economy index and the real economy of the local tourism industry. This means that the real-time tourism condition is properly reflected. In addition, as a result of large-scale structural VAR analysis, it was found that the shock of the tourism industry in Yeongdong increased employment and credit card consumption in the tourism industry, and had a positive effect on total employment. We expect that the results of this study will be used in real-time monitoring of tourism in the Yeongdong region and policy development to promote tourism and expand its real effects.

First aid program for high way car drivers about road injuries

Road injuries are a major public health problem that increases every day causing more deaths and disabilities each year. Aim of this study is to: assess Knowledge, practice of high way car drivers and their life style related to road injuries. Design: A quasi-experimental design. Setting: The study was conducted on Assuit governorate at south Assuit of high way car drivers. Sampling: Purposive sample of 140 high way car drivers. Tools: Two tools were used; Tool (1). Part I: Sample sociolect-demographic data for highway cars; Part II: Questions to assess driver knowledge of road injuries; Part III: Lifestyle assessment for highway vehicle drivers. Tool (2) Observational Check List also included questions to assess drivers' backgrounds. Results: Almost all high way car drivers studied had UN satisfactory level of knowledge related to road injuries before the program conduction. However, there was a significant improvement in Knowledge, Practice and Life Style for high-way car drivers after the program. The total of knowledge, practice and life style were highly statistically significant (p-value <0.001). Conclusion: The implemented program for high way car drivers related to road injuries have a good impact on high way car drivers` knowledge, practice and life style.

Collision Warning System Using Naïve Bayes Classifier

Motor vehicle crashes can lead to traumatic experiences. High-impact collisions usually cause severe injuries or fatalities. A collision warning system that analyzes driving behaviors and warns drivers of impending crashes can prevent road collisions and save lives, so increasing traffic safety. An application of the Naïve Bayes classifier model to determine the potential for rear-end collisions between highway vehicles is presented. The Naïve Bayes classifier is a supervised machine-learning model based on Bayes’s theorem. Two vehicles are utilized, with one vehicle following the other. The parameters studied are speed, distance, acceleration, and deceleration. Training examples involving over 100 potential collision scenarios have been evaluated. Simulation results show that the model successfully responds to and correctly predicts potential collisions.

Study of traffic volumes

"The area of transportation planning deals with transportation facilities (usually streets, roads, walkways, bike lanes, and public transit lines)." It is vital to assess the present state of traffic volume and anticipate the future state of traffic volume in this field traffic volume survey. The purpose of traffic volume studies is to determine the number, movement, and categories of highway vehicles in a given region. An attempt was made to grasp traffic patterns throughout various time periods using data collection. The characteristics of the traffic flow at that crossroads also have an impact on traffic control. As a consequence, the study's findings are valuable in controlling traffic at the intersection and giving some corrective measures to improve traffic safety in the region. Based on the findings, corrective actions such as road widening or enhanced public transit may be recommended.

Fine-grained highway autonomous vehicle lane-changing trajectory prediction based on a heuristic attention-aided encoder-decoder model

Accurate prediction of lane-changing (LC) trajectories can improve traffic efficiency and reduce the probability of accidents, as well as provide driving strategies for intelligent connected vehicles (ICVs) and connected autonomous vehicles (CAVs). Aiming at solving the problems of low prediction accuracy, difficulty in long-term prediction, and an inability of a fine-grained level description of conventional models, a prediction model based on an attention-aided encoder-decoder structure and deep neural network (DNN) is proposed. This study analyzes the LC process and proposes an LC segmentation and sampling method for dividing LC into four stages. The optimal attention-aided encoder-decoder model is tested by trajectory data in the four LC stages and then a heuristic network model is designed. In addition, the proposed heuristic network is connected with the DNN for predicting vehicle kinematics data while predicting the vehicle trajectory. Finally, the heuristic network and DNN are tested in cascade to form a joint cascade prediction model that can perform a fine-grained LC description based on the prediction results. The experimental results show that the proposed cascade prediction model has high accuracy and long-term prediction capability of a vehicle’s trajectory, velocity, acceleration, and steering angle and is also capable of fine-grained LC description. The proposed prediction model can provide a useful theoretical basis for further research on ICVs and CAVs.

Predicting airborne chloride deposition in marine bridge structures using an artificial neural network model

Chloride-induced corrosion of reinforcement is the most frequent durability problem in marine reinforced concrete (RC) structures. In particular, marine structures are intrinsically exposed to chloride ingress due to airborne chloride deposition. However, monitoring airborne chloride deposition in marine structures is difficult, since conventional on-site measurement is time-consuming and very hazardous. This study presents a prediction model for airborne chloride deposition in coastal bridges based on an artificial neural network (ANN) using local marine meteorological data. Two data sets were prepared for training: chloride deposition data in the (1) presence and (2) absence of scattered deicing salts. The proposed ANN model successfully predicted airborne chloride deposition at three different sampling sites in a coastal bridge, despite the complex relationship between airborne chloride deposition and meteorological parameters. The sampling site, such as one near a vehicle highway, was a more important factor for chloride deposition than the bridge height.

Research on Vehicle Lane Change Warning Method Based on Deep Learning Image Processing.

In order to improve vehicle driving safety in a low-cost manner, we used a monocular camera to study a lane-changing warning algorithm for highway vehicles based on deep learning image processing technology. We improved the mask region-based convolutional neural network for vehicle target detection. Suitable anchor frame ratios were obtained by means of K-means++ method clustering for 66,389 vehicle targets with the width/height ratio, which is one more set of anchor frames than the original setting, so as to ensure that the generation accuracy of candidate frames can be improved without sacrificing more network performance. Using the vehicle target annotation set, we trained the vehicle targets. Through the analysis of indicators for mean average precision, a new set of anchor frames was added to improve the accuracy of vehicle target detection. Based on the improved vehicle detection network and an end-to-end lane detection network in series, we proposed an algorithm for the detection of highway vehicle lane-changing behavior with the first-person perspective by summing the inter-frame change rates in the vehicle lane-changing data pool. After the identification and verification of the marked lane-changing picture sequences, a lane-changing detection accuracy rate of 94.5% was achieved.

Mapping Highway Mobile Carbon Source Emissions Using Traffic Flow Big Data: A Case Study of Guangdong Province, China

The continuously growing transportation sector has become the second largest, yet increasing, industrial emissions source of CO2, posing serious challenges to global environmental security. Among the various transport modes, road transportation yields the highest cumulative level of CO2 emissions. However, these emissions have not been sufficiently investigated in previous studies, especially with respect to analyses from the perspective of vehicle emission sources. This can make source management and emissions reduction difficult. To address these methodological issues, this study aims to build a highway traffic carbon emissions monitoring and spatial analysis system, employing the mobile carbon sources concept, and establish a carbon emissions model encompassing all types of passenger and freight vehicles based on interstation O-D traffic flow data recorded by the toll collection network, to calculate vehicle carbon emissions and create a mobile carbon source emissions map. Empirical analyses in Guangdong Province revealed that, compared with conventional studies, the mobile carbon source emission mapping approach can accurately identify vehicle types with higher emissions while assisting with source management. Of the average total daily carbon emissions from all types of vehicles that use highways (15,311 t), 57.10% originated from freight vehicles (8,743 t) while passenger vehicles contributed 42.90%. By specific vehicle type, emissions mainly originated from small and medium-sized vehicles, including Class I passenger vehicles (i.e., cars) and Class I and III freight vehicles. Further, the proposed method could locate road sections characterized by high carbon emissions. High-emission sections in Guangdong Province were mainly spatially autocorrelated, with peak aggregations on national highways; near economically developed and densely populated areas; and adjacent to surrounding airports, ports, and overpass roads. This study improves the scientific and spatial analytical accuracy for carbon emissions measurements of highway vehicles, thus informing source management and sustainable development, as well as providing technical support for attaining carbon neutrality in China.

A domain‐adaptive method with cycle perceptual consistency adversarial networks for vehicle target detection in foggy weather

Foggy weather can cause such problems as blurred image information and the loss of image details, which may pose great challenges to road traffic target detection based on images and videos. In this study, we propose a domain-adaptive road vehicle target detection method to implement domain adaptation for the real foggy scene. We firstly constructed a highway vehicle detection dataset with foggy images (HVFD), which contains normal weather images and foggy images and provides a complete data support for vehicle detection based on computer vision. Secondly, by improving CycleGAN we designed an improved generative confrontation network (CPGAN), which realised the style transfer between foggy images and normal weather images. Finally, we formulated a YOLOv4 target detection framework according to the domain adaptation based on the pre-trained YOLOv4 fog vehicle detection model. The experimental results show that the method we put forward can effectively improve vehicle detection performance and reduce the work of manually labelling a large number of foggy image tags, which has a strong generalisation ability for computer vision-based applications in low-visibility weather.

Operational speed strategy opportunities for autonomous trucking on highways

This study examines the potential for cost and carbon emission reduction for autonomous trucks to adopt lower peak speeds than today's human driven trucks, where the hourly cost of the driver remains a significant trade-off. Adopting lower speeds presents an indirect savings opportunity in addition to savings associated with the removal of the driver. Benefits and trade-offs for varying target speed of autonomous trucks on highways were investigated with a parameterised analysis of transport and cargo cost components. A validated fuel consumption model was used to compare fuel consumption for different speed strategies across idealised highway drive-cycles and vehicle scenarios. A volume-limited tractor-trailer (50% cargo mass capacity) could reduce fuel consumption and carbon emissions by 26% when reducing target speed from 90 km/h to 70 km/h. When the trade-offs of driver, vehicle and cargo time are considered for the same 20 km/h target speed reduction, combined costs increase by 3% for the human driver scenario, but decrease by 4% for an autonomous truck (assuming medium cargo time value). Sensitivity to vehicle type, loading, drive-cycle and cargo value was examined. A UK supermarket chain case study was investigated and key differences between a UK and US context were compared. Mandatory rest breaks and Hours-of-Service restrictions were identified as additional barriers for human driven vehicles to adopt slower speed strategies.

An Integrated Decision-Making Framework for Highway Autonomous Driving Using Combined Learning and Rule-Based Algorithm

In order to solve the manual labelling, long-tail effect and driving conservatism of the existing decision-making algorithm. This paper proposed an integrated decision-making framework (IDF) for highway autonomous vehicles. Firstly, states of the highway traffic are extracted by the velocity, time headway (TH) and the probabilistic lane distribution of the surrounding vehicles. With the extracted traffic state, the reinforcement learning (RL) is adopted to learn the optimal state-action pair for specific scenario. Analogously, by mapping millions of traffic scenarios, huge amounts of state-action pairs can be stored in the experience pool. Then the imitation learning (IL) is further employed to memorize the experience pool by deep neural networks. The learning result shows that the accuracy of the decision network can reach 94.17%. Besides, for some imperfect decisions of the network, the rule-based method is taken to rectify by judging the long-term reward. Finally, the IDF is simulated in G25 highway and has promising results, which can always drive the vehicle to the state with high efficiency while ensuring safety.