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The rapid development of autonomous driving technology is widely regarded as a potential solution to current traffic congestion challenges and the future evolution of intelligent vehicles. Effective driving strategies for autonomous vehicles should balance traffic efficiency with safety and comfort. However, the complex driving environment at highway entrance ramp merging areas presents a significant challenge. This study constructed a typical highway ramp merging scenario and utilized deep reinforcement learning (DRL) to develop and regulate autonomous vehicles with diverse driving strategies. The SUMO platform was employed as a simulation tool to conduct a series of simulations, evaluating the efficacy of various driving strategies and different autonomous vehicle penetration rates. The quantitative results and findings indicated that DRL-regulated autonomous vehicles maintain optimal speed stability during ramp merging, ensuring safe and comfortable driving. Additionally, DRL-controlled autonomous vehicles did not compromise speed during lane changes, effectively balancing efficiency, safety, and comfort. Ultimately, this study provides a comprehensive analysis of the potential applications of autonomous driving technology in highway ramp merging zones under complex traffic scenarios, offering valuable insights for addressing these challenges effectively.

The report examines and analyzes the purpose of acceleration and deceleration lanes at two-level road interchanges. For the preparation of the current report are used the regulatory framework for the design of acceleration and deceleration lanes from Bulgaria, Austria, Germany and USA – California. The design elements of the acceleration and deceleration lanes are compared in detail. Widths and lengths of lanes, merging zones, angles and slopes. The conclusions of the report are given as guidelines for good practice in the design of road interchanges at one and two levels.

In this study, we construct a method for selecting a target space when changing lanes from the merging lane to the main lane, assuming a merging scene on a congested highway. The proposed method predicts the driving trajectory for a few seconds ahead based on the state variables of the ego vehicle and surrounding vehicles, which is used to evaluate the lane change target. We determine the reachable range of the ego vehicle and select a candidate group of inter-vehicle spaces that will serve as the target for the lane change. Next, the proposed method evaluates the candidate group using indicators such as the size of the inter-vehicle space, the distance from the ego vehicle, and the remaining distance of the merging lane, selecting the inter-vehicle space with the highest evaluation as the target for the lane change. Through simulation experiments where the diversity of driving characteristics of human drivers on the main lane is considered, we confirmed that the proposed method has sufficient safety and stability.

To improve lane‐changing efficiency and reduce safety risks for ramp vehicles in highway merging areas, this paper presents a method for predicting vehicle trajectories in these types of scenarios, and it is based on physics‐enhanced residual learning. Focusing on ramp lanes and adjacent mainline lanes, the model considers the influence of both the current and target lanes on the vehicle’s velocity during lane‐changing maneuvers. A hybrid prediction model is constructed by integrating a physics‐based model with a data‐driven approach. Specifically, an improved speed prediction model based on the Gipps general collision avoidance algorithm is introduced to calculate vehicle speed variations during lane‐changing maneuvers, and its parameters are calibrated using a genetic algorithm. The next trajectory point of the vehicle is predicted, and the corresponding residual is computed using the calibrated physical model. A long short‐term memory network is constructed to learn and predict the residuals. The final trajectory prediction is obtained by combining the physical model’s output with the predicted residuals. The experimental results based on real‐world traffic data show that the approach introduced in this study outperforms traditional neural network models significantly in terms of both accuracy and stability. The model achieves a higher determination coefficient and notably reduces both overall and longitudinal prediction errors. Additionally, ablation studies confirm that incorporating a Gipps‐based residual learning mechanism into the data‐driven model significantly enhances prediction performance, thereby validating the effectiveness of integrating physical information with residual learning. The proposed trajectory prediction model offers a novel and effective solution for improving trajectory prediction accuracy for ramp vehicles in highway merging areas.

Highway on-ramp merging locations are vulnerable to traffic collisions inflicting fatal or serious injuries to drivers. Although numerous studies have uncovered the major contributing factors to crashes at on-ramp merging areas, none of these studies have focused on fatal/severe-injury crashes. This paper aims to provide an in-depth and systematic investigation on critical contributing factors of the high-severity crashes at highway merging areas. As part of the analysis, support vector machines (SVM) and random forest (RF) models were developed for a 10-year data set of crashes at more than 250 merginglocations in Texas, United States, using 23 different crash attributes describing each incident to predict highseverity crashes. A sensitivity analysis was conducted to quantify the marginal effects of each contributing factor. The results indicate that there is an increased likelihood of fatal/ severe-injury crashes when the number of highway lanes is high, and the number of lanes on the frontage roads/connector roads is low (<4). Likewise, presence of heavy vehicles seems to affect the occurrence of fatal injury crashes at merging areas. Additionally, longer ramp lengths, presence of auxiliary lanes, and the proximity of exit ramps are found to increase the likelihood of high severity crashes. These findings, either new or consistent with previous studies are helpful in enriching the literature of on-ramp related highway safety studies.

Motivated by design challenges related to aerospace propulsive jets, an experimental investigation has been conducted of the high Mach number jet plume flow field from a round convergent nozzle at under-expanded shock-containing conditions. Hot jets up to a total temperature ratio of 3 were considered. Laser doppler anemometry (LDA) measurements in the jet near field (first 15 nozzle exit diameters) captured the turbulent mixing process in detail, enabling the separate effects of compressibility and static temperature ratio (t) on the development of the velocity and turbulence profiles to be identified. Compressibility dominated in the initial shear layer region, whereas temperature effects controlled the downstream jet merging zone. Analysis of shear layer development demonstrated that, at all temperature ratios, a similar, but significantly stronger, damping effect was observed as in planar shear layers (correlated well by convective Mach number Mc). Consideration of the interaction of compressibility and temperature ratio – which reduce/enhance turbulent mixing respectively – provided for the first time a rational explanation of the observation that increasing jet temperature influenced flow development only up to a static temperature ratio t ~ 1.5, after which further increase has little effect. Measurements of the potential core length (Lp) were analysed to produce an empirical correlation that also illustrated the diminishing effects of heat addition at all jet Mach numbers. The data provide the improved understanding and empirical design techniques essential for developing technologies for jet noise and infra red (IR) signature reduction and represent an important validation test case for computational fluid dynamics (CFD) modelling.

Vehicle trajectory data can reveal naturalistic driving behaviour trends. However, owing to measurement and processing errors, the trajectory data extracted from videos often contain obvious noise. In merging zones, vehicles tend to accelerate and decelerate frequently, leading to poor denoising performance of the linear Kalman filter (KF). To address this issue, this study proposes a new denoising method based on the adaptive Kalman filter, which automatically switches between KF and Unscented KF to accommodate car-following and merging behaviours, respectively. A merging behaviour detection method was designed based on the PELT method and normalized innovation squared (NIS). The F1 score of 92.9% shows the accuracy of behaviour detection. According to our results, the proposed method minimizes the range of jerk compared with other methods, reducing it from −4927.78 to 4960.72 of raw data to −44.92 to 47.14, indicating a significant improvement in denoising and trajectory smoothing. The goal of this study is to achieve high-precision trajectory data under complex real traffic scenarios.

To reduce the risk of traffic conflicts in merging area, driver’s behavior pattern was analyzed to provide a theoretical basis for traffic control and conflict risk warning. The unmanned aerial vehicle (UAV) was used to collect the videos in two different types of merging zones: freeway interchange and service area. A vehicle tracking detection model based on YOLOv5 (the fifth version of You Only Look Once) and Deep SORT was constructed to extract traffic flow, speed, vehicle type, and driving trajectory. Acceleration/deceleration distribution and vehicle lane-changing behavior were analyzed. The influence of different vehicle models on vehicle speed and lane-changing behavior was summarized. Based on this data, the mean and standard deviation of velocity, acceleration, and variable acceleration were selected as the characteristic variables for driving style clustering. To avoid redundant information between features, principal component dimensionality reduction was performed, and the dimensionality reduction data was used for K-means and K-means++ clustering to obtain three driving styles. The results show that there are obvious differences in the driving behaviors of vehicles in different types of merging areas, and the characteristics of different areas should be fully considered when conducting traffic conflict warnings.

Due to the complex traffic characteristics in highway merging areas, drivers tend to exhibit high-risk driving behaviours. To address the characteristics of driving behaviour in highway merging areas, we have developed a real-time identification model for risky drivers by combining a driver risk level labelling method with load balancing-ensemble learning (LB-EL). In this paper, we explore four types of manoeuvre indicator indexes (MIIs)—acute direction, stomp pedal, dangerous following and dangerous lane changing—that can describe the negative behaviours of both individual vehicles and vehicle platoons in highway merging areas. To quantize the label driver risk level, we use the interquartile range (IQR) method and Criteria Importance Though Intercriteria Correlation (CRITIC) while evaluating the reliability of the MII using spatial analysis. Furthermore, we balance the dataset using three load balancing (LB) algorithms and create nine ensemble strategies by pairing adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM) with the three LB algorithms. Finally, we validate the proposed model using trajectory data extracted from unmanned aerial vehicle (UAV) videos. The results indicate that the distribution laws of risky driving behaviours in the acute direction and stomp pedal show a high degree of similarity and good matching with the distribution laws of traffic conflict points in existing research. Moreover, the synthetic minority over-sampling technique-light gradient boosting machine (SMOTE-LGBM) ensemble model achieves the best performance, reaching an accuracy rate of 93.4%, and a recall rate of 92.1%, which demonstrates the validity of our proposed model. This model can be widely applied to recognize risky drivers in video-based surveillance systems.

Dynamic speed guidance for vehicles in on-ramp merging zones is instrumental in alleviating traffic congestion on urban expressways. To enhance compliance with recommended speeds, the development of a dynamic speed-guidance mechanism that accounts for heterogeneity in human driving styles is pivotal. Utilizing intelligent connected technologies that provide real-time vehicular data in these merging locales, this study proposes such a guidance system. Initially, we integrate a multi-agent consensus algorithm into a multi-vehicle framework operating on both the mainline and the ramp, thereby facilitating harmonized speed and spacing strategies. Subsequently, we conduct an analysis of the behavioral traits inherent to drivers of varied styles to refine speed planning in a more efficient and reliable manner. Lastly, we investigate a closed-loop feedback approach for speed guidance that incorporates the driver's execution rate, thereby enabling dynamic recalibration of advised speeds and ensuring fluid vehicular integration into the mainline. Empirical results substantiate that a dynamic speed guidance system incorporating driving styles offers effective support for human drivers in seamless mainline merging.