Traffic Scenarios Research Articles

Investigating pedestrians’ red light running intentions at urban intersections in different traffic Environments: A scenario-based analysis guided by theoretical frameworks

Pedestrian risky behaviors are one of the contributing factors to crashes involving pedestrians. Therefore, it is crucial to comprehend the mechanisms by which pedestrians interact with many influential components in the traffic environment. This study aimed to evaluate pedestrians’ red light running intentions and related factors under different traffic flow scenarios, including straight traffic flow, right-turning traffic flow, and left-turning traffic flow. A theoretical approach based on the theory of planned behavior (TPB) and the prototype willingness model (PWM) was employed. Data were collected from an online survey of 2250 participants in Tehran, Iran. Structural equation modeling (SEM) was used to identify the significant factors that explain intentions. All models successfully explained the behavioral intention for red light running violation; however, the findings revealed that the integrated model was the best-performing model to represent violation and, thus, was selected for interpreting the results and drawing relevant conclusions. Different traffic flow scenarios had varied effects on violation intentions for individual characteristics and model constructs. Previous crash experiences and driving-related background variables emerged to impact pedestrian violation intention across three scenarios. The findings also suggested that the rational constructs (attitude, perceived behavioral control, and facilitating conditions) had a more robust impact on violation intention compared to reactive constructs (prototype similarity, prototype favorability), with facilitating conditions being the strongest predictor of the model, followed by attitudes toward violation as a significant predictor of intention for red light violation. According to the results, the mechanism of risk-taking varies depending on the direction of the traffic flow. Higher risk was associated with the violation at the intersections with straight traffic flow compared to the intersections with turning traffic flow. Based on the findings of this study, several implications, including interventions focusing on individuals’ transportation safety attitudes, countermeasures to increase the risk perception of pedestrians toward turning vehicles, and countermeasures regarding the use of mobile phones while walking for the context of this study were proposed.

A Study on Surrogate Safety Measures at Unsignalized Intersection Blackspots in a Mixed Traffic Scenario

A Study on Surrogate Safety Measures at Unsignalized Intersection Blackspots in a Mixed Traffic Scenario

Does Prosocial Automation Increase Driver’s Well-being?

As societies transition to hybrid mobility systems, interactions between automated vehicles (AVs) and human users in public spaces become more complex, highlighting the critical role of prosocial behaviors. These behaviors are essential for the seamless operation of interdependent transportation networks, helping to address integration challenges of AVs with human-operated vehicles and enhancing well-being by creating more efficient, less stressful, and inclusive environments. This study explores the impact of receiving prosocial behaviors on the cognition, riding performance, and well-being of micromobility users in simulated urban traffic scenarios. Using a mixed-method design, the research contrasts two types of social interactions—prosocial and asocial—across three temporal conditions: relaxed, neutral, and urgent. The Structural Equation Modeling underscored the intricate relationships between prosocial behaviors, satisfaction, cognition, riding performance, and well-being. Incorporating social factors into driving systems could enhance safety and improve urban mobility solutions.

Coordination of distributed adaptive signal control and advisory speed optimization based on shockwave theory

AbstractThis paper presents a distributed adaptive signal control and advisory speed coordination method based on shockwave theory, which accommodates diverse traffic conditions. In order to assess signal control efficiency under various scenarios, an innovative evaluation index termed synthetic delay is introduced based on the analysis of traffic dynamics at intersections. Considering the formation and dissipation of queue, and flow fluctuation of incoming traffic, it automatically evaluates control delay and throughput with distinctive significances. The distributed adaptive control method calculates the optimal green time in real time to minimize total synthetic delay at intersections. Furthermore, the coordination of advisory speed with the signal control schemes is addressed to ensure smooth progressions for vehicles. The proposed method considers the saturation of traffic and upstream traffic flow changes, leading to adaptability to changing traffic scenarios and effective coordination of traffic control. Several simulations were conducted and compared with the proposed method with other control methods. The results demonstrate that the proposed methods reduce the control delay and increase intersection throughput remarkably under different traffic saturations, confirming their effectiveness.

The Shared Experience Actor–Critic (SEAC) Approach for Allocating Radio Resources and Mitigating Resource Collisions in 5G-NR-V2X Mode 2 Under Aperiodic Traffic Conditions

5G New Radio (NR)-V2X, standardized by 3GPP Release 16, includes a distributed resource allocation Mode, known as Mode 2, that allows vehicles to autonomously select transmission resources using either sensing-based semi-persistent scheduling (SB-SPS) or dynamic scheduling (DS). In unmanaged 5G-NR-V2X scenarios, SB-SPS loses effectiveness with aperiodic and variable data. DS, while better for aperiodic traffic, faces challenges due to random selection, particularly in high traffic density scenarios, leading to increased collisions. To address these limitations, this study models the Cellular V2X network as a decentralized multi-agent networked Markov decision process (MDP), where each vehicle agent uses the Shared Experience Actor–Critic (SEAC) technique to optimize performance. The superiority of SEAC over SB-SPS and DS is demonstrated through simulations, showing that the SEAC with an N-step approach achieves an average improvement of approximately 18–20% in enhancing reliability, reducing collisions, and improving resource utilization under high vehicular density scenarios with aperiodic traffic patterns.

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Abstract The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.

Domain adaptation for semantic segmentation of road scenes via two-stage alignment of traffic elements

Unsupervised domain adaptation has been used to reduce the domain shift, which would improve the performance of semantic segmentation on unlabeled real-world data. However, existing methodologies fall short in effectively addressing the domain shift issue prevalent in traffic scenarios, leading to less than satisfactory segmentation results. In this paper, we propose a novel domain adaptation method for semantic segmentation via unsupervised alignment of traffic elements. Firstly, we introduce a two-stage self-training framework that leverages a blended set of training samples to enhance the training process. In the first stage, we leverage generated mixup training samples as inputs within our two-stage self-training framework and have developed corresponding loss functions for both the source and target domains to direct the training process. Then, the alignment modules for dynamic and static traffic elements are designed to achieve accurate matching between the source and the target domain images. The cosine similarity maximization is applied to the alignment of dynamic traffic elements, while the prototype learning is utilized for the static traffic elements. Additionally, we present a new technique for reducing noise in pseudo labels by constructing thresholds that adjust to each class. Meanwhile, we formulate the associated target domain loss function for vacant pseudo label pixels. The experimental results demonstrate that the proposed method is superior to the existing methods on five different domain adaptation tasks, which is more applicable to semantic segmentation of road scenes.

Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges

This paper aims to extend a previously developed probabilistic model for simulating extreme response scenarios to include congested traffic flow on long-span bridges, addressing the challenge of accurately modeling traffic loads under changing conditions. While the model was initially designed for free-flow traffic, this study demonstrates how it can be adapted for congested conditions, with the objective of improving the accuracy of traffic load models. To overcome the limitation of traditional Weigh-in-Motion (WIM) systems in capturing congested traffic, congested flow characteristics were inferred from available free-flow data. The cellular automata (CA) method was applied to generate realistic congested traffic scenarios, which were used as input for the probabilistic model. Key simulation parameters, such as cell length and vehicle weight distribution, were adjusted to reflect congested conditions. The results validate the model’s flexibility, showing how, with the adaptation of some parameters, it can simulate both free-flow and congested traffic patterns effectively. This research provides a basis for improving traffic load models used in the design and assessment of long-span bridges, addressing the current limitations in existing codes and standards.

A following model considering multiple vehicles from the driver's front and rear perspectives

In the current traffic environment dominated by manual driving, existing models of drivers' rear-view perceptions are inadequate. Existing car-following models that incorporate rear-view information are primarily focused on the Internet of Vehicles (IoV) and automated driving environments. However, they fail to realistically reflect the visual processing mechanisms of human drivers, limiting their effectiveness in realistic traffic scenarios. Therefore, we propose a new following model, the multi-vehicle influence from front and rear perspectives (MVFR), that considers the influence of multiple vehicles. The MVFR model combines information from both front and rear vehicles, integrating views from the front, side front and rear. It provides an in-depth analysis of the effects of relative state differences between a vehicle and its surrounding vehicles on speed, including the effects of perspectives in both the lateral and longitudinal directions. Linear stability analysis and numerical simulation demonstrate that considering the perspectives of rear-following vehicles and lateral offset angles can improve traffic flow stability to a certain extent. Furthermore, properly considering the lateral offset distance and the number of vehicles ahead also positively affects traffic flow stability. This study reveals that observing following vehicles and considering information from multiple front vehicles enhances system stability, especially when there is no or minimal lateral offset. In contrast, focusing on fewer front vehicles is more effective for traffic flow stability when there is a large lateral offset. Experimental results using the CKQ4up dataset show that the MVFR model achieves higher accuracy than the conventional FVD model, the front-view-only improved FVD model (MFVD-RV), and the MFRHVAD-RV and MFRHVAD-AV models. Compared with models relying solely on front-view or non-visual perception, the MVFR model demonstrates a better fit, validating the advantages of this full-view perception model in manual driving environments. This innovation addresses the shortcomings of existing research, thereby enhancing the reliability of models under manual driving conditions on highways.

A hierarchical deep reinforcement learning method for solving urban route planning problems under large-scale customers and real-time traffic conditions

As urbanization and economic growth advance, large-scale customers and real-time traffic conditions have become crucial factors in urban route planning. Deep reinforcement learning is considered the most effective method for solving urban route planning problems involving large-scale customers and real-time traffic conditions. Due to memory usage limitations, existing deep reinforcement learning methods cannot identify candidate customers or determine optimal travel routes in large-scale and real-time environments. To tackle these problems, this study introduces a hierarchical deep reinforcement learning method utilizing an improved transformer model (HDRLITF) based on the divide-and-conquer concept. Graph attention networks and gate mechanisms are integrated into the transformer model to capture dynamic features and improve the model’s performance. A two-stage training method, based on the actor-critic algorithm, is proposed to determine the optimal policy function. To evaluate the HDRLITF method, experiments were conducted using datasets from the cities of Shenzhen and Chengdu in China. The experimental results suggest that the HDRLITF method can effectively interact with real-time traffic environments and obtain high-quality solutions compared to other deep reinforcement learning methods. The robustness and reliability of HDRLITF were further validated across multiple traffic scenarios and indicators.

A precise registration method for large-scale urban point clouds based on phased and spatial geometric features

Abstract The dense high-rise buildings and multipath effects in urban areas significantly reduce the positioning signal accuracy of laser scanning systems, leading to layering and offset issues in the collected point cloud data on the same road. In order to acquire comprehensive and consistent three-dimensional information on the objects, thereby providing field inspection data for large-scale road traffic network scenarios, in this paper, an improved point cloud registration method is proposed to divide the registration process into two stages: elevation registration and plane registration. Elevation registration takes the ground point cloud as the registration primitive, reduces the number of point clouds through curvature down-sampling, and constrains the feature point sequence with a fixed range to provide a good initial pose for fine registration. The plane registration first inherits the elevation registration parameters, combining the dynamic distance parameters of spherical region step based on the median, using robust multi-scale loss functions to address residual points, effective adjacent point pairs are selected to obtain the spatial transformation matrix, and realizes accurate registration. Experimental results with multiple sets of urban point cloud data show that the root mean square error of point cloud registration can be controlled within 0.06 m, achieving a relatively superior registration accuracy, it can provide detailed prior data for measurement information analysis.

LFIR-YOLO: Lightweight Model for Infrared Vehicle and Pedestrian Detection

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices.

An adversarial diverse deep ensemble approach for surrogate‐based traffic signal optimization

AbstractSurrogate‐based traffic signal optimization (TSO) is a computationally efficient alternative to simulation‐based TSO. By replacing the simulation‐based objective function, a surrogate model can quickly identify solutions by searching for extreme points on its response surface. As a popular surrogate model, the ensemble of multiple diverse deep learning models can approximate complicated systems with a strong generalizability. However, existing ensemble methods barely focus on strengthening the prediction of extreme points, which we found can be realized by further diversifying base learners in the ensemble. The study proposes an adversarial diverse ensemble (ADE) method for online TSO with limited computational resources, comprising two stages: In the offline stage, base extractors are diversified with unlabeled data by a designed adversarial diversity training algorithm; in the online stage, base predictors are trained in parallel with limited labeled data, and the ensemble then serves as the surrogate model to search for solutions iteratively for TSO. First, it is demonstrated that the prediction accuracy on extreme points, and associated solution quality, can be constantly improved with base learners’ diversity enhanced by ADE. Case studies of TSO conducted on a four‐intersection arterial further demonstrate the superior solution quality and computational efficiency of the ADE surrogate model in a wide range of traffic scenarios. Moreover, a large‐scale online TSO experiment under dynamic traffic demand proves ADE's effectiveness in practical applications.

Overtaking risk assessment based on extreme value theory and intelligent network information

In order to evaluate the overtaking risk level of intelligent connected vehicles when providing different connected information, and to make up for the neglect of driver factors in traditional risk assessment and the inadequate evaluation of complex traffic scenarios by a single traffic conflict indicator, this paper introduces the Block maxima (BM) and Peak over threshold (POT) methods to fit the extreme value distribution for two conflict scenarios involved in overtaking events (following vehicle conflict and frontal vehicle conflict), so as to evaluate the risk of following vehicle accidents and frontal collision accidents during overtaking. In each conflict scenario, a non-stationary extreme value model considering driver factors and a binary extreme value model considering different traffic conflict indicators are constructed, and the model is verified by the overtaking test data of intelligent connected vehicles in a two-way two-lane vehicle. Overtaking events were extracted from the original test data and conflict indicators were calculated: including the collision time GAP with the front vehicle at the beginning of the overtaking event, the collision time TTC_t1 with the oncoming vehicle, the deceleration DRAC, and the collision time TTC with the oncoming vehicle and the headway time THW with the front vehicle at the end of the overtaking event. The probability of the event with a negative time conflict indicator or DRAC greater than MADR was used to characterize the degree of collision risk. The results show that in the following vehicle conflict scenario, the binary extreme value model constructed with different conflict indicators has different error results, among which the binary extreme value model constructed with THW&DRAC has the most accurate evaluation result (standard error MAE = 0.00028); in the front vehicle conflict scenario, the binary extreme value model constructed with TTC&DRAC has the most accurate evaluation result ( MAE = 0.006 ). In different conflict scenarios, the non- stationary extreme value model considering the driver factor significantly improves the risk assessment accuracy compared with the model without considering the driver factor (small AIC and BIC values). In addition, different intelligent network information (real-time distance, overtaking advice, speed advice) brings different overtaking risks, and when the intelligent network information is speed advice, the vehicle's overtaking risk is the lowest. Therefore, the non- stationary extreme value model and binary extreme value model considering driver factors proposed in this paper can effectively evaluate driving risks through traffic conflict indicators. Secondly, the experimental data of intelligent networked vehicles show that the extreme value theory model proposed in this paper can accurately evaluate the overtaking risk level of intelligent networked vehicles when providing different network information.

Empirical Studies on Merging Characteristics in Temporary Highway Work Zones with Different Traffic States and Layouts

Vehicles conduct mandatory lane changes to merge into the open lanes in temporary highway work zones, and the merging characteristics have been widely studied over the past decades. Unlike research on other traffic facilities, the data and scenarios of highway work zones are often lacking and monotonous. This study presented a vehicle trajectory dataset in highway work zones. The dataset contains ten work zone sites and three different layouts. After transforming raw videos into high-resolution trajectories, we selected eight areas and divided them into three scenarios to investigate the merging characteristics in highway work zones with different traffic states and layouts. In work zones with varying traffic conditions, we found opposite merging locations and lane speed distribution trends. In work zones with varying layouts, we found different merging location distributions, lane speed distributions, and impacts on the following vehicle’s speed. The relationship between the gap and merge speed was consistent with previous studies.

Large scale multi-GPU based parallel traffic simulation for accelerated traffic assignment and propagation

Traffic simulation is a critical tool for congestion analysis, travel time estimation, and route optimization in urban planning, benefiting navigation apps, transportation network companies, and state agencies. Traditionally, traffic micro-simulation frameworks are based on road segments and can only support a limited number of main roads. Efficient traffic simulation on a regional scale remains a significant challenge due to the complexity of urban mobility and the large scale of spatiotemporal data. This paper introduces a Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), which leverages graphical processing unit (GPU) parallel computing to address these challenges. LPSim utilizes a multi-GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time. Using the parallel processing capabilities of GPUs, LPSim can perform tens of millions of individual vehicle dynamics simulations simultaneously, significantly outperforming traditional CPU-based approaches. The framework is designed to be scalable and can easily accommodate the increasing complexity of traffic simulations. We present the theory behind GPU-based traffic simulation, the architecture of single- and multi-GPU based simulations, and the graph partition strategies that enhance computation resource load balance. Our experimental results demonstrate the effectiveness of LPSim in simulating large-scale traffic scenarios. LPSim is capable of completing simulations of 2.82 million trips in just 6.28 min on a single GPU machine equipped with 5120 CUDA cores (Tesla V100-SXM2). Furthermore, utilizing a Google Cloud instance with two NVIDIA V100 GPUs, which collectively offer 10240 CUDA cores, LPSim successfully simulates 9.01 million trips within 21.16 min. We further tested our simulator with the same demand on dual NVIDIA A100-PCIE-40GB GPUs, which finished the simulation in 0.0398 h, approximately 113 times faster than the same simulation scenario running on an Intel(R) Xeon(R) Gold 6326 CPU @ 2.90 GHz, which takes 4.49 h to complete. This performance not only demonstrates its speed and scalability advantages over traditional simulation techniques but also highlights LPSim’s unique position as the first traffic simulation framework that is scalable for both single- and multiple-GPU configurations. Consequently, LPSim provides an invaluable tool for individuals and extensive research teams alike, enabling the acquisition of large-scale traffic simulation results in a time-efficient manner. LPSim code is available at: https://github.com/Xuan-1998/LPSim

A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

Recent years have witnessed significant advancements in machine perception, particularly in the context of self-driving vehicles. The accurate detection and interpretation of road signs by these vehicles are crucial for enhancing safety, intelligence, and efficiency on the roads. Consequently, there is a growing body of research dedicated to improving traffic sign recognition technologies, a key component of intelligent transportation systems. Annual statistics highlight numerous accidents attributable to factors such as excessive speed, variable lighting conditions, and the misinterpretation of traffic signs. In response to these challenges, a novel approach for the rapid and reliable recognition of traffic signs by moving vehicles has been developed. This approach leverages a custom dataset encompassing twelve object categories and seven subcategories, reflective of road sign diversities encountered in India. A specialized algorithm, TrafficSignNet, was devised to specifically identify signs related to speed, turning, zones, and bumps. This algorithm was trained on a comprehensive dataset comprising 4,962 images, with its performance evaluated using 705 images from real traffic scenarios. The evaluation demonstrates that the model achieves remarkable accuracy across various lighting conditions, processing up to 12 frames per second. This processing rate is compatible with the high-definition standards of contemporary vehicle cameras, which is 1280 × 720 pixels. The model's effectiveness is quantified through accuracy, precision, recall, and F1 score, with respective values of 0.985, 0.978, 0.964, and 0.971, showcasing its potential to significantly contribute to the advancement of smart transportation systems.

Video Content Analysis for Detection of Road Traffic Congestion Pattern: An Optimized YOLOv8 Based Approach

This paper presents a new approach to Video Content Analysis (VCA) with a specific focus on detecting and analyzing road traffic congestion patterns. The implemented methodology deploys the YOLOv8 (You Only Look Once) object detection framework, which has been optimized to accurately and efficiently identify road traffic objects within video streams. The primary goal is to improve traditional VCA systems by capturing and understanding complex road congestion scenarios. This research develops a customized YOLO model optimized specifically for road traffic analysis. The optimization process addresses the challenges inherent in real-world traffic scenarios including variations in lighting conditions, diverse vehicle types and dynamic traffic flow patterns. The developed model seeks to enable robust and real-time detection of congestion-related events such as heavy congestion and normal congestion thereby contributing to improved traffic management and overall road safety. Moreover, the paper explores the integration of advanced techniques for congestion pattern analysis including vehicle detection, tracking, and counting. The combination of these features facilitates a comprehensive understanding of traffic dynamics and congestion evolution over time. The proposed approach is trained and evaluated on benchmark datasets and real-world video footage to assess its performance. Experimental results demonstrate the effectiveness of the optimized YOLOv8-based approach in accurately detecting and analyzing road traffic congestion patterns. The system exhibits promising capabilities in handling diverse and challenging scenarios thereby serving as a valuable tool for intelligent transportation systems, urban planning and traffic management. This research significantly contributes to the advancement of video-based traffic analysis, providing a robust solution for monitoring and mitigating congestion-related challenges on road networks.

Probabilistic model for high-level intention estimation and trajectory prediction in urban environments

To enable successful automated driving, precise behavior prediction of surrounding vehicles is indispensable in urban traffic scenarios. Furthermore, given that a vehicle’s behavior is influenced by the movements of other road users, it becomes crucial to estimate their intentions to anticipate precise future motion. However, the elevated complexity resulting from interdependencies among traffic participants and the uncertainty arising from the object recognition errors present additional challenges. Despite extensive research on inferring intentions, many studies have concentrated on estimating intentions from interactions, resulting in a lack of practicality in urban traffic environments due to low computational efficiency and low robustness against recognition failure of strongly interacting road users. In this paper, we introduce a practical stochastic model for intention estimation and trajectory prediction of surrounding vehicles in automated driving under urban traffic environments. The trajectory is forecasted based on hierarchically computed and probabilistically estimated intentions, which represent an interpretation of vehicle behavior, utilizing only the kinematic state of the focal vehicle and HD maps to ensure real-time performance and enhance robustness. The evaluated results demonstrate that the proposed model surpasses straightforward methods in terms of accuracy while maintaining computational efficiency and exhibits robustness against the recognition failure of traffic participants which strongly influence the focal vehicle.

Research on the strategy of cruise control system for urban traffic jam assistant

This paper proposes a hierarchical cruise control strategy that can adapt to urban traffic scenarios. Firstly, the perception layer defines a target selection strategy by using a novel trapezoidal dangerous area. Then, the upper controller contains speed follow mode and distance follow mode. An improved PI mode with integral separation and saturation terms is developed to achieve the desired speed following control, a safe distance mode via variable time headway is developed to facilitate the safe and smooth distance following control. The lower controller is designed based on feedforward and PI feedback. Finally, the experimental results demonstrate that the strategy can effectively select suitable target vehicles in complex urban scenarios. With the improved safe distance model and the third-order Bessel actuator response delay model, the strategy exhibits adaptability and stability. This study offers valuable suggestions and guidance for designing and applying the cruise control strategy for urban traffic scenarios.