Unsignalized Intersections Research Articles

Research on vehicle trajectory prediction methods in dense and heterogeneous urban traffic

ABSTRACT In autonomous driving, accurately predicting the trajectories of surrounding vehicles is essential, particularly in dense and heterogeneous urban traffic. We propose a graph-structured model with a category layer to efficiently forecast the target vehicle’s trajectory. The model enables flexible selection of interacting objects based on environmental interactions and extracts spatial-temporal features using a graph convolutional network. A categorical layer is introduced to account for the different influences of dynamic agents, while vehicle dynamics constraints ensure the feasibility of predicted trajectories. We developed a new heterogeneous and dense urban unsignalized intersection dataset (HID), capturing complex urban interactions, and conducted extensive experiments on HID, ApolloScape, and TRAF datasets. Results demonstrate that our model outperforms benchmark methods across diverse urban scenarios, and the integration of key modules significantly enhances prediction accuracy and performance.

Learning to control and coordinate mixed traffic through robot vehicles at complex and unsignalized intersections

Learning to control and coordinate mixed traffic through robot vehicles at complex and unsignalized intersections

Anticipated buffer time – An evasive surrogate safety indicator for risk assessment of unsignalized intersections under heterogeneous traffic and aggressive driving conditions

Risk assessment of unsignalized intersections is particularly challenging when confronted with a combination of factors such as heavy traffic, diverse vehicle types, lane indiscipline, aggressive driving, and evasive manoeuvres. Understanding how people drive in these situations is crucial for accurately assessing the risks at unsignalized intersections. This study introduces a novel surrogate safety indicator, i.e. Anticipated Buffer Time (ABT), designed to account for these various factors. Additionally, three new indicators derived from ABT are introduced, namely ABT Negation Ratio, ABT Extremity Ratio, and ABT Progression Ratio. A risk assessment measure, denoted as UnSigRisk Score, is formulated using these three indicators for unsignalized intersections. Three intersections in Ahmedabad, India, were selected for the study due to their manifestation of these challenging conditions. Spearman Rank Correlation Coefficient was estimated to find out how well can UnSigRisk Score measure is able to quantify evasive behaviour. The results indicate that this score proficiently measures evasive behaviour, exhibiting coefficients exceeding 0.6 in all cases—significantly outperforming the current evasive indicators, Yaw Rate Ratio and Jerk. The proposed risk assessment score could serve as a practical tool for transportation authorities, enabling them to identify the most vulnerable intersections and allocate resources for targeted safety interventions wisely. The study unequivocally demonstrates that the use of ABT paves the way for a thorough examination of safety at unsignalized intersections, regardless of driving behaviour and traffic conditions.

Predicting pedestrian-vehicle interaction severity at unsignalized intersections

Objectives This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptrons (MLP). This approach leverages advanced feature analysis capabilities, offering a more direct and interpretable method than traditional models. Methods High-resolution optical cameras recorded detailed pedestrian and vehicle movements at study sites, with data processed to extract trajectories and convert them into real-world coordinates via precise georeferencing. Trained observers categorized interactions into safe passage, critical event, and conflict based on movement patterns, speeds, and accelerations. Fleiss Kappa statistic measured inter-rater agreement to ensure evaluator consistency. This study introduces a novel deep-learning model combining Transformer-based time series data capabilities with the classification strengths of a Multilayer Perceptron (MLP). Unlike traditional models, this approach focuses on feature analysis for greater interpretability. The model, trained on dynamic input variables from trajectory data, employs attention mechanisms to evaluate the significance of each input variable, offering deeper insights into factors influencing interaction severity. Results The model demonstrated high performance across different severity categories: safe interactions achieved a precision of 0.78, recall of 0.91, and F1-score of 0.84. In more severe categories like critical events and conflicts, precision and recall were even higher. Overall accuracy stood at 0.87, with both macro and weighted averages for precision, recall, and F1-score also at 0.87. The variable importance analysis, using attention scores from the proposed transformer model, identified ‘Vehicle Speed’ as the most significant input variable positively influencing severity. Conversely, ‘Approaching Angle’ and ‘Vehicle Distance from Conflict Point’ negatively impacted severity. Other significant factors included ‘Type of Vehicle’, ‘Pedestrian Speed’, and ‘Pedestrian Yaw Rate’, highlighting the complex interplay of behavioral and environmental factors in pedestrian-vehicle interactions. Conclusions This study introduces a deep-learning model that effectively predicts the severity of pedestrian-vehicle interactions at crosswalks, utilizing a Transformer-MLP hybrid architecture with high precision and recall across severity categories. Key factors influencing severity were identified, paving the way for further enhancements in real-time analysis and broader safety assessments in urban settings.

Do cyclists disregard ‘priority-to-the-right’ more often than motorists?

The integration of autonomous driving into mixed traffic environments poses unique challenges, especially at unsignalized intersections where communication and cooperation with human road users is necessary. This study addresses a critical research gap by investigating the adherence to the “priority-to-the-right” rule at an urban unsignalized intersection, comparing behavioral patterns of cyclists and motorists. Utilizing stationary mounted cameras, a 12-day traffic observation of an urban T-intersection in Braunschweig, Germany, was conducted. The class and the trajectory of road users as well as their scenarios were identified. In 202 cases, cars appearing from the right (ego, with priority) encountered cars or bikes coming from the left (foe, without priority). The study analyzed the impact of variables, ego’s direction, foe’s class and lateral position and their arrival time on their passing order through descriptive statistics and logistic regression. The findings reveal that cyclists disregard the “priority-to-the-right” rule more often than motorists. Additionally, road users who have the priority are more likely to yield when turning right, arriving at the intersection later, and encountering an opposing road user who is close to the center of the road. This study highlights the importance of implicit communication in traffic and provides essential benchmarks for developing more human-like autonomous driving systems, capable of interpreting and responding to nuanced road user interactions at unsignalized intersections.

Analisa Pengaruh Hambatan Samping terhadap Kinerja Ruas Jalan pada Jalan Raya Kedu, Reco Kabupaten Wonosobo

According to the 1997 Indonesian Road Capacity Manual (MKJI), a road section is defined as the length of the road between and not influenced by major signalized or unsignalized intersections and has the same characteristics along the entire length of the road. Side obstacles are one of the factors causing traffic jams. Traffic jams are caused by an imbalance between the increase in vehicle ownership and the growth in available road infrastructure, as well as the effective capacity of existing road sections being smaller than the planned road capacity due to roadside obstacles. Traffic engineering is needed to monitor and maintain the continuity of transportation. According to the Institute of Transportation Engineers, USA, Traffic Engineering is a stage of transportation engineering which involves the design, geometric planning and traffic operations of all kinds of roads, road networks, terminals, surrounding land and relationships with other types of transportation.

Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation

Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation

Reasoning graph-based reinforcement learning to cooperate mixed connected and autonomous traffic at unsignalized intersections

Cooperation at unsignalized intersections in mixed traffic environments, where Connected and Autonomous Vehicles (CAVs) and Manually Driving Vehicles (MVs) coexist, holds promise for improving safety, efficiency, and energy savings. However, the mixed traffic at unsignalized intersections present huge challenges like MVs’ uncertainties, the chain reaction and diverse interactions. Following the thought of the situation-aware cooperation, this paper proposes a Reasoning Graph-based Reinforcement Learning (RGRL) method, which integrates a Graph Neural Network (GNN) based policy and an environment providing mixed traffic with uncertain behaviors. Firstly, it graphicly represents the observed scenario as a situation using the interaction graph with connected but uncertain (bi-directional) edges. The situation reasoning process is formulated as a Reasoning Graph-based Markov Decision Process which infers the vehicle sequence stage by stage so as to sequentially depict the entire situation. Then, a GNN-based policy is constructed, which uses Graph Convolution Networks (GCN) to capture the interrelated chain reactions and Graph Attentions Networks (GAT) to measure the attention of diverse interactions. Furthermore, an environment block is developed for training the policy, which provides trajectory generators for both CAVs and MVs. A reward function that considers social compliance, collision avoidance, efficiency and energy savings is also provided in this block. Finally, three Reinforcement Learning methods, D3QN, PPO and SAC, are implemented for comparative tests to explore the applicability and strength of the framework. The test results demonstrate that the D3QN outperformed the other two methods with a larger converged reward while maintaining a similar converged speed. Compared to multi-agent RL (MARL), the RGRL approach showed superior performance statistically, reduced the number of severe conflicts by 77.78–94.12 %. The RGRL reduced average and maximum travel times by 13.62–16.02 %, and fuel-consumption by 3.38–6.98 % in medium or high Market Penetration Rates (MPRs). Hardware-in-the-loop (HIL) and Vehicle-in-the-loop (VehIL) experiments were conducted to validate the model effectiveness.

VN-MADDPG: A Variable-Noise-Based Multi-Agent Reinforcement Learning Algorithm for Autonomous Vehicles at Unsignalized Intersections

The decision-making performance of autonomous vehicles tends to be unstable at unsignalized intersections, making it difficult for them to make optimal decisions. We propose a decision-making model based on the Variable-Noise Multi-Agent Deep Deterministic Policy Gradient (VN-MADDPG) algorithm to address these issues. The variable-noise mechanism reduces noise dynamically, enabling the agent to utilize the learned policy more effectively to complete tasks. This significantly improves the stability of the decision-making model in making optimal decisions. The importance sampling module addresses the inconsistency between outdated experience in the replay buffer and current environmental features. This enhances the model’s learning efficiency and improves the robustness of the decision-making model. Experimental results on the CARLA simulation platform show that the success rate of decision making at unsignalized intersections by autonomous vehicles has significantly increased, and the pass time has been reduced. The decision-making model based on the VN-MADDPG algorithm demonstrates stable and excellent decision-making performance.

Constraint-Guided Behavior Transformer for Centralized Coordination of Connected and Automated Vehicles at Intersections.

The centralized coordination of Connected and Automated Vehicles (CAVs) at unsignalized intersections aims to enhance traffic efficiency, driving safety, and passenger comfort. Autonomous Intersection Management (AIM) systems introduce a novel approach for centralized coordination. However, existing rule-based and optimization methods often face the challenges of poor generalization and low computational efficiency when dealing with complex traffic environments and highly dynamic traffic conditions. Additionally, current Reinforcement Learning (RL)-based methods encounter difficulties around policy inference and safety. To address these issues, this study proposes Constraint-Guided Behavior Transformer for Safe Reinforcement Learning (CoBT-SRL), which uses transformers as the policy network to achieve efficient decision-making for vehicle driving behaviors. This method leverages the ability of transformers to capture long-range dependencies and improve data sample efficiency by using historical states, actions, and reward and cost returns to predict future actions. Furthermore, to enhance policy exploration performance, a sequence-level entropy regularizer is introduced to encourage policy exploration while ensuring the safety of policy updates. Simulation results indicate that CoBT-SRL exhibits stable training progress and converges effectively. CoBT-SRL outperforms other RL methods and vehicle intersection coordination schemes (VICS) based on optimal control in terms of traffic efficiency, driving safety, and passenger comfort.

Impact of Traffic Characteristic at Unsignalized Intersection in Mixed Traffic Condition using Logistic Regression Method (LRM) and Artificial Neural Network (ANN)

Impact of Traffic Characteristic at Unsignalized Intersection in Mixed Traffic Condition using Logistic Regression Method (LRM) and Artificial Neural Network (ANN)

Automatic driving decision-making on left-turn behavior at unsignalized intersections

Automatic driving decision-making on left-turn behavior at unsignalized intersections

Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach

Non-Cooperative and Cooperative Driving Strategies at Unsignalized Intersections: A Robust Differential Game Approach

A Pedestrian Trajectory Prediction Model for Right-Turn Unsignalized Intersections Based on Game Theory

A Pedestrian Trajectory Prediction Model for Right-Turn Unsignalized Intersections Based on Game Theory

Comparison of E-Scooter and Bike Users’ Behavior in Mixed Traffic

This study aims to investigate the different behaviors with respect to safety measures related to the interaction of e-scooters and bikes with cars in mixed traffic. E-scooters are relatively new vulnerable road users, and their behavior is still not fully understood. For this purpose, an observational study was carried out at an unsignalized at-grade intersection in the city of Catania, Italy. A total of 128 interactions between cars and e-scooters and 89 interactions between cars and bikes were detected. Specifically, two surrogate measures of safety were used, the time to collision (TTC) and post encroachment time (PET), which relate to the “crossing” and the “following” interactions between cars and bikes/e-scooters. The results show that 50% of the “crossings” involving bikes were close interactions with low TTCs representing high risk (TTC < 1.5); meanwhile, for the “crossing” interactions between cars and e-scooters, the same threshold of TTC relates to percentiles of more than 80%. In addition, more than 60% of interactions between cars and e-scooters were characterized by PET values representing a potentially high risk (PET < 1.0 s). The results provide a useful starting point for the elaboration and adaptation of new regulations for mixed traffic conditions including e-scooters that are currently being introduced in several countries with different rules. It should be noted that e-scooters are an intrinsically different transport mode from a bicycle, mainly because their interactions in mixed traffic show that they are prone to a higher risk of closer interactions.

Factors affecting crash severity in Roundabouts: A comprehensive analysis in the Jordanian context

Roundabouts are widely embraced for their perceived safety advantages over other types of unsignalized intersections. However, there has been an observed increase in crash rates at roundabouts over time in Jordan. This paper delves into modeling traffic crash severity at roundabouts, considering various factors such as weather, lighting, vehicle characteristics, geometric features, and driver age and gender. To comprehensively analyze roundabout crashes in Jordan, we constructed rule-based classifiers and Random Forest models after balancing the dataset. Rule-based models offer interpretability, albeit with some simplicity trade-off, while Random Forest models provide deeper analysis but require additional explanation. Presenting both outputs to subject matter experts and policymakers facilitates a holistic understanding of factors contributing to roundabout crashes in Jordan. Subsequently, CN2 results revealed that injury severity crashes are influenced by the time of the day, driver age, day of the week, speed, and number of vehicles involved. On the other hand, property damage-only crashes are affected by the number of lanes, time of the day, type of driver fault, lighting conditions, speed, and day of the week. The RF model analysis unveiled crucial factors influencing crash severity in roundabouts, notably the varying impact of driver age, time of day, the number of vehicles involved, seasonality, and vehicle speed. This proposed approach is promising, comprehensive, and not only enhances the understanding of roundabout crashes but also informs the development of effective, localized safety interventions.

A pro-active approach for assessment of vehicle-vehicle interaction at unsignalized intersection in mixed traffic conditions

A pro-active approach for assessment of vehicle-vehicle interaction at unsignalized intersection in mixed traffic conditions

MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA

Roundabouts, as an unsignalized intersection, have an effective preventative measure designed to control straight-line crashes. Efficient traffic flow in cities depends upon appropriate capacity estimation of roundabouts. This study attempts to develop models for roundabout entry capacity by applying Artificial Neural Network (ANN) analysis for mixed traffic flow conditions. Data was gathered from 27 roundabouts spread across India. The influence area for gap acceptance (INAGA) concept was used as a graphical method to identify critical gap (Tc) of entry flow at roundabouts. This study indicated that the Bayesian Regularisation Neural Network (BRNN) based model has the best R2 and RMSE of 0.97 and 167.8. The connection weight approach and Garson algorithm evaluate the significance of each explanatory variable and identify follow-up time (Tf) as a critical parameter with values of 11.10 and 21.15%, respectively.

A Study on the Estimation of Change in Traffic States at Unsignalized Intersections based on the Mixed Rate of Autonomous Vehicle Using Microscopic Traffic Simulation

A Study on the Estimation of Change in Traffic States at Unsignalized Intersections based on the Mixed Rate of Autonomous Vehicle Using Microscopic Traffic Simulation

Performance Evaluation of Ekantakuna Intersection

Majority of the intersections in Kathmandu valley are unsignalized and hence traffic management becomes one of the emerging issues for traffic police and traffic designers. The number of vehicles in Kathmandu Valley is increasing at the rate of 12% annually (Department of Transport Management, 2018). The manual traffic management is very tedious and hectic nowadays. The consequence leads to traffic congestion and sometimes crashes too. Hence, the performance evaluation of such unsignalized intersection is essential and considering the real time situations, scenario analysis should be done. The research was carried out in Ekantakuna Intersection. The data's were collected in Ekantakuna Intersection through video-graphic surveying during weekdays: Monday to Friday both morning and evening during peak hours (9 to 11 AM in morning and 4 to 6 PM in evening).The model was developed in PTV VISSIM ( Micro-simulation software ) and thus the developed model was calibrated and validated using traffic volume. The relationship between field volume (actual volume) and VISSIM volume (Simulated Volume) was found with the help of R2 Value (Coefficient of Determination) which is found to be 0.9993 which indicates that variance of VISSIM output explains 99.9 percentage of variance in field data's. Pedestrian movement was not given priority and hence pedestrian simulation is not considered. Total three scenario analysis were performed in base case scenario. Among them the best scenario analysis was found to be scenario 1 which indicates the restriction of vehicles from service lanes to main lanes and allowing the movement of vehicles from Balkhu(service) to Satdobato(service) and Jawalakhel lanes, and Satdobato(service) to Balkhu(service ) and Bhaisepati lane.