Unsignalized Intersections Research Articles

Toward edge-computing-enabled collision-free scheduling management for autonomous vehicles at unsignalized intersections

With the support of Vehicle-to-Everything (V2X) technology and computing power networks, the existing intersection traffic order is expected to benefit from efficiency improvements and energy savings by new schemes such as de-signalization. How to effectively manage autonomous vehicles for traffic control with high throughput at unsignalized intersections while ensuring safety has been a research hotspot. This paper proposes a collision-free autonomous vehicle scheduling framework based on edge-cloud computing power networks for unsignalized intersections where the lanes entering the intersections are undirectional and designs an efficient communication system and protocol. First, by analyzing the collision point occupation time, this paper formulates an absolute value programming problem. Second, this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time (EI-OET) algorithm based on edge-cloud computing power support. Then, the communication system and protocol are designed for the proposed scheduling scheme to realize efficient and low-latency vehicular communications. Finally, simulation experiments compare the proposed scheduling framework with directional and traditional traffic light scheduling mechanisms, and the experimental results demonstrate its high efficiency, low latency, and low complexity.

CALIBRATION OF MICROSCOPIC TRAFFIC SIMULATION OF URBAN ROAD NETWORK INCLUDING MINI-ROUNDABOUTS AND UNSIGNALIZED INTERSECTION USING OPEN-SOURCE SIMULATION TOOL

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.

Genetic Programming for Estimating Passenger Car Equivalent in Unsignalized Intersections

The study concentrates on two different Genetic Programming approaches for determining Passenger Car Equivalent(PCE) values and observing the impact on capacity estimation at urban unsignalized intersections. Considering heterogeneoustraffic conditions, a new PCE value is introduced to encompass sustainable modes of public transit vehicles, specifically Slow-moving three-wheelers (SM3W), commonly known as E-Rickshaws. Since PCE value is considered an important parameter forcapacity calculations, the present study considered 14 unsignalized intersections located in Ranchi city of India. An automaticplate recognition system is employed to have the count of vehicular traffic. The methodologies include Age layered populationstructure genetic programming (ALPSGP), and the Offspring selection genetic programming (OSGP) approach that incorporatesstatic and dynamic variables. Based on the significance test and ranking of the Genetic programming (GP) models, the OSGPmodel is recommended as the most appropriate model for heterogeneous traffic. Sensitivity analysis reported that laggingheadway (����) is the most contributing factor in PCE estimation. The PCE value of SM3W is found to be 0.81 and that could beincorporated as a new classification of vehicles in Indo-HCM. It is observed that evaluated capacity based on OSGP’s PCEvalues performed admirably in both normal and congested traffic situations.

Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study

Abstract Significant social, economic, and environmental costs are associated with traffic congestion. The effectiveness of intersections makes a substantial contribution to the effectiveness of entire road networks. The three-leg at-grade Al-Husainea intersection, located 20 km from the center of holy Karbala city in Iraq, is subjected to serious congestion, resulting in an increase in delay time, reduction in capacity, and bad level of service (LOS). Therefore, it is essential to use advanced software tools to ensure that the current intersection can be controlled, evaluated, and improved. So, the major goal of this study is to use SIDRA, an acronym for signalized and unsignalized intersection design and research aid, software to evaluate the LOS of the Al-Husainea intersection, in which the traffic is assessed using the current LOS. LOS, delay time, and degree of saturation were the criteria utilized to evaluate the traffic flow performance. SIDRA is also used to assess benefits as a result of suggested changes in the design of particular junctions. The first stage is to gather field data regarding traffic volumes utilizing a method of traffic volume gathering. From 7 am to 6 pm, the SIDRA program gathered data for a full 7 days. The results showed that the LOS for the Al-Husainea intersection in the Al-Husainea arm is F, with an average delay of 52 s per vehicle and a saturation level of 0.86 v/c. Finally, it was determined that the Al-Husainea intersection needs additional improvements based on the study and findings from the SIDRA program, and some remedies are suggested in this study to improve the intersection traffic flow.

A Computationally Efficient Bi-Level Coordination Framework for CAVs at Unsignalized Intersections

A Computationally Efficient Bi-Level Coordination Framework for CAVs at Unsignalized Intersections

Coordination for Connected and Autonomous Vehicles at Unsignalized Intersections: An Iterative Learning-Based Collision-Free Motion Planning Method

Coordination for Connected and Autonomous Vehicles at Unsignalized Intersections: An Iterative Learning-Based Collision-Free Motion Planning Method

Evalusai Kinerja Simpang Tak Bersinyal (Study Kasus Simpang Jalan Sukomulyo dan Jalan Kyai H. Syafii Kec. Manyar Kab. Gresik)

The intersection of Jalan Sukomulyo and Jalan Kyai H. Syafii is one of the 3 unsignalized intersections with quite high traffic levels. So it is necessary to conduct research on the performance of the intersection. This type of research was conducted using field observation methods. The primary data collection is done by making direct observations at the Sukomulyo road intersection and Kyai H. Syafii road. The LHR survey was carried out for one week by directly recording vehicles passing through the planning location at 15-minute intervals. When the data have been obtained, it analyzed based on the Directorate General of Highways MKJI 1997. The results of the analysis of the performance of the intersection at the Sukomulyo road intersection and Kyai H. Syafii road show quite good results even though many vehicles pass it. Morning traffic volume is 4220.8 pcu/hour, afternoon is 2071.6 pcu/hour, in the afternoon is 3741.3 pcu/hour. The morning intersection capacity is 6552.74 pcu/hour, the afternoon is 4805.38 pcu/hour, in the afternoon is 5500.06 pcu/hour. The degree of saturation in the morning is 0.64, afternoon is 0.43 and afternoon is 0.68. Morning delay is 7.79 seconds/hour, afternoon is 3.98 seconds/hour, in the afternoon is 8.57 seconds/hour. Probability of queuing in the morning with an upper limit of 35.22% and lower limit of 16.98%, Probability of queuing in the afternoon with an upper limit of 20.44% and a lower limit of 8.5%, Probability of queuing in the afternoon with an upper limit of 38.78% and a lower limit of 18.98% .

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

IMPLEMENTATION OF THE TRAFFIC CONFLICT TECHNIQUE METHOD AT PONTIANAK'S UNSIGNALIZED INTERSECTION

The high number of traffic accidents is a problem for the government in realizing traffic security, safety, and smoothness. Intersections are the most common point of conflict, especially unsignalized intersections. The research location of the intersection of Jl. Budi Utomo - Jl. 28 October-Jl. Parit Nenas, Siantan Hulu Village, North Pontianak District, and Pontianak City intersect environmental-type commercial roads with many vehicles and education centers. The research objectives are to identify traffic conflicts, analyze the level of traffic conflicts, and provide alternatives to overcome problems.Traffic Conflict Technique is an observation method to analyze traffic conflict that illustrates the seriousness of the conflict. The research data used CCTV recording for two days, Saturday and Tuesday. The Traffic Conflict Technique method uses the Time to Accident and Conflicting Speed values to determine the seriousness of the conflict.Based on the analysis of the Traffic Conflict Technique method, the research location with 34 vehicle conflicts has a severe level of conflict that could potentially cause accident risk. The level of serious conflict is 27 vehicle conflicts, with a percentage of 79.41%. Vehicle speed in the class 15 km / h - 19 km / h is the most severe conflict speed, with 12 vehicle conflicts and a percentage of 44.44%. Accelerating vehicle behavior is the most severe conflict, with 25 vehicle conflicts and a percentage of 83.33% of accidents that have the potential to occur. Alternatives to reduce the causes of accidents are adding traffic signaling devices and traffic signs to improve safety.

Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique

Autonomous driving in urban areas is challenging because it requires understanding vehicle movements, traffic rules, map topologies and unknown environments in the highly complex driving environment, and thus typical urban traffic scenarios include various potentially hazardous situations. Therefore, training self-driving cars by using traditional deep learning models not only requires the labelling of numerous datasets but also takes a large amount of time. Because of this, it is important to find better alternatives for effectively training self-driving cars to handle vehicle behavior and complex road shapes in dynamic environments and to follow line guidance information. In this paper, we propose a method for training a self-driving car in simulated urban traffic scenarios to be able to judge the road conditions on its own for crossing an unsignalized intersection. In order to identify the behavior of traffic flow at the intersection, we use the CARLA (CAR Learning to Act) self-driving car simulator to build the intersection environment and simulate the process of traffic operation. Moreover, we attempt to use the DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient) learning algorithms of the DRL (Deep Reinforcement Learning) technology to train models based on the CNN (Convolutional Neural Network) architecture. Specifically, the observation image of the semantic segmentation camera installed on the self-driving car and the vehicle speed are used as the model input. Moreover, we design an appropriate reward mechanism for performing training according to the current situation of the self-driving car judged from sensing data of the obstacle sensor, collision sensor and lane invasion detector. Doing so can improve the convergence speed of the model to achieve the purpose of the self-driving car autonomously judging the driving paths so as to accomplish accurate and stable autonomous driving control.

Understanding distracted pedestrians’ risky behaviour: The role of walking and visual characteristics through a field study

Use of mobile phones while crossing the road has become a growing safety concern among pedestrians. Though recently, some studies have attempted to understand risky behaviour of distracted pedestrians by analyzing impacts of distraction on pedestrians’ walking and visual characteristics, the impacts were measured individually. Moreover, most of the existing findings are based on lab-based experiments, whereas the real-world conditions are quite dynamic in nature. Hence, to overcome these gaps, the current study aims to understand to what extent different potential factors (such as distraction, walking characteristics, visual behaviour, etc.) affect the risk of unsafe pedestrian-vehicle interactions in real-world settings. For this purpose, a field experiment was designed to understand the impact of distraction on pedestrians’ risky behaviour using an eye-tracker at an unsignalized intersection in Delhi. The risk assessment was done using Post-Encroachment Time (PET) measure. The results indicate that the use of mobile phone while crossing the road increased the chances of unsafe (PET < 1.5 s) pedestrian-vehicle interactions by approximately eight folds. Surprisingly, pedestrians who crossed the intersection at high speed had higher chances of unsafe interactions, whereas their increased crossing initiation time reduced the probability of unsafe interactions. Moreover, increased pupil diameter and fixating more on vehicles approaching from the right side resulted in a reduced probability of unsafe interactions. However, the high frequency of saccades and higher fixations on the crosswalk's right side negatively affected unsafe interactions. This study highlights that it is important to consider visual characteristics along with demographic and walking and traffic characteristics as they can provide in-depth insights into pedestrians’ road crossing behaviour that are required to design and implement effective countermeasures and policies.

Effects of physical functional characteristics of older drivers on spatial cognition at unsignalized intersections

This study analyzed the relationship between gaze behavior and physical functions of elderly drivers at unsignalized intersections, which have spatial characteristics that make elderly drivers more likely to be on the at-fault side of a collision. Twenty-nine elderly and 14 non-elderly drivers were asked to view 360-degree videos from five different routes of two unsignalized intersections under conditions similar to actual driving. The results were as follows. (1) The eye movement distance (EMD) was shorter for elderly drivers in most of the driving sections, with the section "At slowdown" on non-priority roads being the section where a significant difference was likely to occur. (2) For both elderly and non-elderly drivers, the amount of EMD was significantly related to visual and general physical functions such as flexibility, equilibrium, and agility.

Hierarchical Model of Pedestrians' Crossing Decision-making at Unsignalized Intersection

Hierarchical Model of Pedestrians' Crossing Decision-making at Unsignalized Intersection

Advanced Decision Making and Motion Planning Framework for Autonomous Navigation in Unsignalized Intersections

Advanced Decision Making and Motion Planning Framework for Autonomous Navigation in Unsignalized Intersections

A Hierarchical Multi-Vehicle Coordinated Motion Planning Method Based on Interactive Spatio-Temporal Corridors

Multi-vehicle coordinated motion planning has always been challenged to safely and efficiently resolve conflicts under non-holonomic dynamic constraints. Constructing spatial-temporal corridors for multi-vehicle can decouple the high-dimensional conflicts and further reduce the difficulty of obtaining feasible trajectories. Therefore, this paper proposes a novel hierarchical multi-vehicle coordinated motion planning method based on interactive spatio-temporal corridors (ISTCs). In the first layer, based on the initial guidance trajectories, Mixed Integer Quadratic Programming is designed to construct ISTCs capable of resolving conflicts in generic multi-vehicle scenarios. And then in the second layer, Non-Linear Programming is settled to generate in-corridor trajectories that satisfy the vehicle dynamics. By introducing ISTCs, the multi-vehicle coordinated motion planning problem is able to be decoupled into single-vehicle trajectory optimization problems, which greatly decentralizes the computational pressure and has great potential for real-world applications. Besides, the proposed method searches for feasible solutions in the 3-D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(x,y,t)$</tex-math></inline-formula> configuration space, preserving more possibilities than the traditional velocity-path decoupling method. Simulated experiments in unsignalized intersection and challenging dense scenarios have been conducted to verify the feasibility and adaptability of the proposed framework.

A Dynamic Multi-Risk Management based on Cooperative Optimization Architecture for CAVs at Unsignalized Intersection

Compared to signalized intersections, unsignalized intersections possess greater potential and risks in solving traffic congestion. While using the Multi-Risk Management strategy based on Cooperative Optimization and the Predicted Inter-Distance Profile (MRMCO-PIDP), a Dynamic MRMCO (D-MRMCO) coordination optimization architecture for Connected Autonomous Vehicles (CAVs) is studied in this paper. The primary objective of this work is to propose an MRMCO-PIDP approach that is suitable for dynamic environments and to address the issue of local minima solutions associated with the penalty function. To overcome the challenges associated with local minima, we incorporated the Metropolis Criterion to cross the possible local optimal solution. Additionally, a new intersection area model has been established, allowing CAVs to make different risk-level decision-making strategies based on their specific positions within the intersection.

Multi-Agent Constrained Policy Optimization for Conflict-Free Management of Connected Autonomous Vehicles at Unsignalized Intersections

Multi-Agent Constrained Policy Optimization for Conflict-Free Management of Connected Autonomous Vehicles at Unsignalized Intersections

Towards Safe Decision-Making for Autonomous Vehicles at Unsignalized Intersections

Towards Safe Decision-Making for Autonomous Vehicles at Unsignalized Intersections

Experience in calibrating the VISSIM microscopic simulation model for an unsignalized intersection in Pekanbaru, Indonesia

Traffic activity is unique and occurs throughout the region. All moving traffic movements cause a build up of vehicles when there are conflicts between vehicles and result in congestion, one of which is in the intersection area located on Jalan SM Amin, Pekanbaru. Traffic modelling can assist traffic management through accurate simulation results with field conditions based on driving behavior value VISSIM is a simulation tool used to simulate dynamic traffic flow conditions. The research has aim to modelling and calibrating driver behavior value based on traffic flow conditions at unsignalised intersections in Pekanbaru. This research uses simulation modelling with VISSIM as the modeler and GEH and MAPE as validation tools to choose which right model. Simulation process will used calibration process based on driving behavior value for the right model by trial and error method.. The result showed that simulation model based on default parameter of driving Behavior cannot represent for real traffic. Then changes in the combination of Driving Behaviour values affect the accuracy of model conditions with field conditions. The accuracy is measured using the reference volume of vehicles exiting the unsignalled intersection. Therefore, this research could be can assist in smoothing traffic flow model in Pekanbaru which more easily by using live traffic flow simulation that can be utilized for traffic intersection management scenarios.

Reservation-Prioritization-Based Mixed-Traffic Cooperative Control at Unsignalized Intersections

Reservation-Prioritization-Based Mixed-Traffic Cooperative Control at Unsignalized Intersections