Non-signalized Intersections Research Articles

Assessing the impact of driving behaviors and traffic conflicts on vehicle emissions at non-signalized intersections using a trajectory-based computational framework

Vehicle emissions can rise due to traffic conflicts and aggressive driving behaviors, such as frequent acceleration and deceleration. This issue is particularly pronounced at non-signalized intersections with a high proportion of non-motorized vehicles. In this study, we propose a framework that integrates a microscopic vehicle emission model with trajectory data. By utilizing trajectory data collected from a non-signalized intersection in Shanghai, we analyzed vehicle emissions linked to driving behaviors and traffic conflicts. Our findings reveal that pre-braking at the entrance of non-signalized intersections can significantly reduce vehicle emissions, lowering them by nearly 80 % for straight maneuvers. However, this reduction is less substantial for turning maneuvers. Additionally, conflicts involving more than two types of targets lead to a significant increase in vehicle emissions. On average, stop-and-go emissions are 1.13 % higher than those resulting from traffic conflicts. Interestingly, when non-motorized vehicles constitute more than 80 % of the traffic volume, stop-and-go emissions fall below those generated by traffic conflicts. The results of this study provide valuable insights for optimizing eco-driving strategies and advancing towards a low-carbon transportation system.

A Study on the Impact of AI Edge Computing Technology on Reducing Traffic Accidents at Non-signalized Intersections on Residential Road

A Study on the Impact of AI Edge Computing Technology on Reducing Traffic Accidents at Non-signalized Intersections on Residential Road

Selection-sort-based cooperative driving strategy for CAVs at non-signalized intersections

Cooperative driving has recently been proposed to increase the efficiency and safety at intersections. The major challenge of multi-vehicle cooperative driving is to improve traffic performance and meanwhile guarantee instantaneous optimization response. This paper proposes a selection-sort-based cooperative driving strategy at non-signalized intersection in the environment of connected and automated vehicles (CAVs), which is made up of passing order adjustment and vehicle trajectory planning. In order to assign a better passing order to approaching vehicles and reduce the complexity of cooperative driving, a passing order adjustment algorithm is designed. The effectiveness of the algorithm is analyzed theoretically. According to the passing order, the movement rules are constructed to help vehicles pass through the intersections safely. Numerical simulations verify the effectiveness of the proposed strategy under different traffic scenarios. Compared with the periodic traffic signal and first-in-first-out strategy, the results show that the proposed strategy can significantly improve traffic efficiency with less computation time.

Heuristic-Based Multi-Agent Deep Reinforcement Learning Approach for Coordinating Connected and Automated Vehicles at Non-Signalized Intersection

Heuristic-Based Multi-Agent Deep Reinforcement Learning Approach for Coordinating Connected and Automated Vehicles at Non-Signalized Intersection

A Simulation Study for Evaluation of Autonomous Vehicle Safety Guidance Strategies at Non-signalized Intersection

A Simulation Study for Evaluation of Autonomous Vehicle Safety Guidance Strategies at Non-signalized Intersection

Autonomous-Vehicle Intersection Control Method Based on an Interlocking Block

Non-signalized intersections have only ever been suitable for low traffic flow; however, with the development of autonomous driving technology and new control methods, the operation efficiency of this kind of intersection may be improved. In view of the shortcomings of existing non-signalized intersection control methods in multilane situations and inspired by railway trains, an interlocking-block intersection control model is proposed. In this study, vehicles between parallel lanes are combined into a few combos, and the combo shape can be determined according to a pairing model and the interlocking angle range, and the gaps between the front and rear vehicles are simulated as blocks in a railway system, which are added into the intersection control model as virtual blocked cars (VBCs) for optimization. In setting the optimization objectives, the connotation and realization of fairness are discussed. Experimental results show that compared with signalized intersections, roundabouts, and non-signalized intersections without control, the interlocking-block intersection control model greatly reduces vehicle delay. Compared with an existing model, the calculation speed in a multilane situation has been greatly improved, while the vehicle delay is similar.

Distributed Scheduling at Non-Signalized Intersections With Mixed Cooperative and Non-Cooperative Vehicles

Intersection management with mixed cooperative and non-cooperative vehicles is crucial in next-generation transportation systems. For fully non-cooperative systems, a minimax scheduling framework was established, while it is inefficient in mixed systems as the benefit of cooperation is not exploited. This paper focuses on the efficient scheduling in mixed systems and proposes a two-stage decision framework that makes full use of the cooperation. First, a long-horizon self-organization policy is developed to optimize the passing order of cooperative vehicles in a distributed manner, which is proved convergent when inbound roads are sufficiently long. Then a short-horizon trajectory planning policy is proposed to improve the efficiency when an ego-vehicle faces both cooperative and non-cooperative vehicles, and its safety and efficiency are theoretically validated. Moreover, simulation results verify that the proposed policies can effectively reduce the scheduling cost and improve the throughput for cooperative vehicles.

Trajectory-based conflict investigations involving two-wheelers and cars at non-signalized intersections with computer vision

Unsafe acts at non-signalized intersections have become a primary contributor to traffic accidents and fatalities. Whereas many studies have focused on non-signalized intersections in the past decade, capturing and recording road users’ micro-behavior and risk in the mixed traffic flow remain challenging. A large number of two-wheelers (e.g., bicycles and e-bikes) appear at non-signalized intersections, in which the conflict behaviors are highly unpredictable. In this study, conflicts involving two-wheelers and cars at non-signalized intersections were investigated based on trajectory data collected with a YOLOv3-based framework automatically. A novel conflict identification algorithm was developed to gather and process microscopic trajectory data. To detect conflict behaviors involving two-wheelers and cars, near-crash identification was employed with a post-encroachment time indicator that also contributes to demonstrating the effect of vehicle order on conflict severity from an unprecedented perspective. The proposed framework was applied to a case study at a university campus in Shanghai. To explore the relationship between contributing factors and conflict severity, a significance test and ordered probability models were implemented using 10,304 conflicts collected from video data. The statistical analysis disclosed that conflicts involving e-bikes accounted for the highest proportion, and the order of vehicles (e.g., pre-encroachment vehicle and post-encroachment vehicle) has different effects on conflict severity. The analytical results with risk assessment can contribute to developing intersection-specific countermeasures for traffic safety from the perspectives of education, engineering, and law enforcement. The trajectory-based framework can be adapted to intelligent transportation systems to enhance safety management.

Coordination for Connected and Automated Vehicles at Non-Signalized Intersections: A Value Decomposition-Based Multiagent Deep Reinforcement Learning Approach

The recent proliferation of the research on multi-agent deep reinforcement learning (MDRL) offers an encouraging way to coordinate multiple connected and automated vehicles (CAVs) to pass the intersection. In this paper, we apply a value decomposition-based MDRL approach (QMIX) to control various CAVs in mixed-autonomy traffic of different densities to efficiently and safely pass the non-signalized intersection with fairish fuel consumption. Implementation tricks including network-level improvements, Q value update by TD ($\lambda$), and reward clipping operation are added to the pure QMIX framework, which is expected to improve the convergence speed and the asymptotic performance of the original version. The efficacy of our approach is demonstrated by several evaluation metrics: average speed, the number of collisions, and average fuel consumption per episode. The experimental results show that our approach's convergence speed and asymptotic performance can exceed that of the original QMIX and the proximal policy optimization (PPO), a state-of-the-art reinforcement learning baseline applied to the non-signalized intersection. Moreover, CAVs under the lower traffic flow controlled by our method can improve their average speed without collisions and consume the least fuel. The training is additionally conducted under the doubled traffic density, where the learning reward converges. Consequently, the model with maximal reward and minimum crashes can still guarantee low fuel consumption, but slightly reduce the efficiency of vehicles and induce more collisions than the lower-traffic counterpart, implying the difficulty of generalizing RL policy to more advanced scenarios.

Platoon-based collision-free control for connected and automated vehicles at non-signalized intersections

<abstract><p>This paper proposes a distributed collision-free control scheme for connected and automated vehicles (CAVs) at a non-signalized intersection. We first divide an intersection area into three sections, i.e., the free zone, the platoon zone, and the control zone. In order to enable the following vehicles to track the trajectory of their leading vehicle in the platoon zone and the control zone, as well as to guarantee the desired distance between any two adjacent vehicles, the distributed platoon controllers are designed. In the control zone, each vehicular platoon is taken as a whole to be coordinated via an intersection coordination unit (ICU). To avoid collision between each pair of the conflicting platoons approaching from different directions, a platoon-based coordination strategy is designed by scheduling the arrival time of each leading vehicle of different platoons. Specially, considering traffic efficiency and fuel economy, the optimal control problem of the leading vehicle is formulated subject to the constraint of allowable minimum arrival time, which is derived from coordination with other approaching platoons. The Pontryagin Minimum Principle (PMP) and phase-plane method are applied to find the optimal control sequences. Numerical simulations show the effectiveness of this scheme.</p></abstract>

Improvement of Stop Rate and Speed Suppression Effect by Changing the Position of the Stop Line at the Non-Signalized Intersection

Improvement of Stop Rate and Speed Suppression Effect by Changing the Position of the Stop Line at the Non-Signalized Intersection

Effect of Optimization Time-Scale on Learning-Based Cooperative Merging Control at a Nonsignalized Intersection

Automated driving and the widespread use of large-scale communication infrastructure are expected to facilitate highly cooperative driving. Although considerable research has focused on developing efficient cooperative control methods for nonsignalized intersections, the effect of cooperative control for conflicting target vehicles on future traffic flow is yet to be investigated. Therefore, we aim to investigate whether the impact of such cooperative control on future traffic should be considered. We established a traffic simulator and several machine-learning methods to select the optimal cooperative method. The decision tree and deep neural network were trained on two indices that evaluate short-term/long-term predictive control: to minimize the travel time of the (1) conflicting vehicles and (2) all vehicles including future traffic flow. Simulation analysis results indicated that there were no significant differences in the total travel times between these indices. This finding indicates that efficient traffic flow, which includes future traffic flow, is achievable by short-term cooperative control methods that can be established easily.

A quantitative traffic performance comparison study of bicycles and E-bikes at the non-signalized intersections: Evidence from survey data

As common transportation modes at non-signalized intersections, bicycles and e-bikes have been involved in most traffic crashes. Although a large number of studies have been dedicated to studying the safety problems caused by bicycles and e-bikes, there is still limited attention paid to the differences between them, especially at non-signalized intersections. This paper compares the differences between bicycles and e-bikes based on a self-administered questionnaire. This questionnaire was distributed to bicycle users (N = 453) and e-bike users (N = 439). The personal characteristics, decision-making capacity, the feeling of infrastructure, perceived level of service, and perceived level of risk were adopted as the performance indicators to depict the difference in the study area. Using statistical methodologies and the Structural Equation Model (SEM), key findings indicate that perceived level of service was found to be significantly different between bicycles and e-bikes at most non-signalized intersections. 43.4 % of e-bike riders often or always choose to avoid riding under extreme weather, while 58.7 % of bicycle riders avoid riding under extreme weather. Moreover, compared with bicycles, e-bikes’ decision-making capacity is affected more by infrastructure quality. The difference between bicycles and e-bikes highlights the need for differentiated development of cycling safety education and law enforcement.

Multi-object trajectory extraction based on YOLOv3-DeepSort for pedestrian-vehicle interaction behavior analysis at non-signalized intersections

Multi-object trajectory extraction based on YOLOv3-DeepSort for pedestrian-vehicle interaction behavior analysis at non-signalized intersections

Comprehensive Automated Driving Maneuvers under a Non-Signalized Intersection Adopting Deep Reinforcement Learning

Automated driving systems have become a potential approach to mitigating collisions, emissions, and human errors in mixed-traffic environments. This study proposes the use of a deep reinforcement learning method to verify the effects of comprehensive automated vehicle movements at a non-signalized intersection according to training policy and measures of effectiveness. This method integrates multilayer perceptron and partially observable Markov decision process algorithms to generate a proper decision-making algorithm for automated vehicles. This study also evaluates the efficiency of proximal policy optimization hyperparameters for the performance of the training process. Firstly, we set initial parameters and create simulation scenarios. Secondly, the SUMO simulator executes and exports observations. Thirdly, the Flow tool transfers these observations into the states of reinforcement learning agents. Next, the multilayer perceptron algorithm trains the input data and updates policies to generate the proper actions. Finally, this training checks the termination and iteration process. These proposed experiments not only increase the speeds of vehicles but also decrease the emissions at a higher market penetration rate and a lower traffic volume. We demonstrate that the fully autonomous condition increased the average speed 1.49 times compared to the entirely human-driven experiment.

Improved reservation-based method with controllable gap strategy for vehicle coordination at non-signalized intersections

Recently, new research has emerged for signal-free intersection control via V2X technologies, and reservation-based intersection control for connected and automated vehicles (CAVs) has great potential to improve travel efficiency and traffic safety. A centralized traffic controller is the key component to coordinate movements of the approaching vehicles considering their priorities. In this paper, we propose a controllable gap strategy for the traffic controller considering the conflict relationship. To reduce the number of timestamp comparisons, we build three conflict modes of different traffic movements and present safe speed-dependent constraints for them. Then, based on the interactive relationship, we design an algorithm to manage the reservations for vehicles, which allows conflict-free vehicles crossing over the intersection synchronously. Furthermore, to ensure absolute safety in all cases, we present a rapid check criterion for conflict-free time slots in the conflict set of the reservation to avoid a potential collision, which removes unnecessary judgments. It is analyzed and proved that the aggregation of these local checks solves the conflicts globally. The strategy relaxes the limits of reservation policy. The proposed method is verified in the SUMO platform compared with the naive reservation-based method. Simulation results within 3000 s show that the proposed method can increase the traffic throughput and avoid vehicle collision for isolated intersections and traffic networks.

Pedestrian-Aware Statistical Risk Assessment

This paper proposes a statistical framework to assess the risk of passing a non-signalized intersection for vehicles. First, an intensity model of the near-accident event is established by regarding the near-accident event as a non-homogeneous Poisson process. The non-homogeneous Poisson process is defined on the sigma-algebra of the 2-dimension plane of vehicle velocity and distance to the intersection instead of in the time axis. On the other hand, the pedestrian intention is defined as a binary variable with 1 as passing through the crosswalk and 0 as stopping. Logistic function is applied to model the probability of pedestrian intention. The proposed statistical models are evaluated by the residual analysis-based model checking method. Besides, based on the two models, the pedestrian-aware risk model is established to give a predictive risk metric quantitatively when pedestrian appears.

Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections

Digital Twin, as an emerging technology related to Cyber-Physical Systems (CPS) and Internet of Things (IoT), has attracted increasing attentions during the past decade. Conceptually, a Digital Twin is a digital replica of a physical entity in the real world, and this technology is leveraged in this study to design a cooperative driving system at non-signalized intersections, allowing connected vehicles to cooperate with each other to cross intersections without any full stops. Within the proposed Digital Twin framework, we developed an enhanced first-in-first-out (FIFO) slot reservation algorithm to schedule the sequence of crossing vehicles, a consensus motion control algorithm to calculate vehicles’ referenced longitudinal motion, and a model-based motion estimation algorithm to tackle communication delay and packet loss. Additionally, an augmented reality (AR) human-machine-interface (HMI) is designed to provide the guidance to drivers to cooperate with other connected vehicles. Agent-based modeling and simulation of the proposed system is conducted in Unity game engine based on a real-world map in San Francisco, and the human-in-the-loop (HITL) simulation results prove the benefits of the proposed algorithms with 20% reduction in travel time and 23.7% reduction in energy consumption, respectively, when compared with traditional signalized intersections.

Investigating the Effects of Human-Machine Interface on Cooperative Driving at Non-Signalized Intersection Using an Integrated Co-Simulation Platform

Investigating the Effects of Human-Machine Interface on Cooperative Driving at Non-Signalized Intersection Using an Integrated Co-Simulation Platform

Trajectory-Based Conflict Investigations of Two-Wheel Vehicles at Non-Signalized Intersections with Computer Vision

Trajectory-Based Conflict Investigations of Two-Wheel Vehicles at Non-Signalized Intersections with Computer Vision