Behavior Of Traffic Participants Research Articles

MTR++: Multi-Agent Motion Prediction With Symmetric Scene Modeling and Guided Intention Querying.

Motion prediction is crucial for autonomous driving systems to understand complex driving scenarios and make informed decisions. However, this task is challenging due to the diverse behaviors of traffic participants and complex environmental contexts. In this paper, we propose Motion TRansformer (MTR) frameworks to address these challenges. The initial MTR framework utilizes a transformer encoder-decoder structure with learnable intention queries, enabling efficient and accurate prediction of future trajectories. By customizing intention queries for distinct motion modalities, MTR improves multimodal motion prediction while reducing reliance on dense goal candidates. The framework comprises two essential processes: global intention localization, identifying the agent's intent to enhance overall efficiency, and local movement refinement, adaptively refining predicted trajectories for improved accuracy. Moreover, we introduce an advanced MTR++ framework, extending the capability of MTR to simultaneously predict multimodal motion for multiple agents. MTR++ incorporates symmetric context modeling and mutually-guided intention querying modules to facilitate future behavior interaction among multiple agents, resulting in scene-compliant future trajectories. Extensive experimental results demonstrate that the MTR framework achieves state-of-the-art performance on the highly-competitive motion prediction benchmarks, while the MTR++ framework surpasses its precursor, exhibiting enhanced performance and efficiency in predicting accurate multimodal future trajectories for multiple agents.

The connection of the experience of involvement in traffic accidents with the punishable behavior of traffic participants

This paper was written as part of the research project "Research of public opinion on the state of safety, behaviour and habits of participants in road traffic in the Republic of Croatia". The goal of this research is to determine the frequency of violations of traffic regulations related to drivers, pedestrians and passengers in vehicles, and to determine whether there is a connection between the experience of suffering of the aforementioned road users and their punishable behaviour. For this purpose, a quantitative research was conducted in which a multi-method approach was used in field data collection, a combination of telephone research (CATI) - 60% of the sample and online research (CAWI) - 40% of the sample. Respondents from the entire territory of the Republic of Croatia (N=2072) over the age of 16 were included in the research. In general, it can be concluded that the violation of traffic regulations is very frequent and present among the majority of different road users, and that road users who have recently experienced a traffic accident in any capacity, very rarely violate traffic regulations, as car drivers. Conversely, involvement in traffic accidents has not been shown to deter pedestrians and vehicle occupants from repeating traffic violations. The obtained results have a practical value, especially in the context of planning preventive programs and strategies, while the limitations of the research are reflected in the fact that only one independent variable was included in the correlation with committing traffic violations, regardless of other known predictors of punishable behaviour in traffic.

Graph-Based Prediction and Planning Policy Network (GP3Net) for Scalable Self-Driving in Dynamic Environments Using Deep Reinforcement Learning

Recent advancements in motion planning for Autonomous Vehicles (AVs) show great promise in using expert driver behaviors in non-stationary driving environments. However, learning only through expert drivers needs more generalizability to recover from domain shifts and near-failure scenarios due to the dynamic behavior of traffic participants and weather conditions. A deep Graph-based Prediction and Planning Policy Network (GP3Net) framework is proposed for non-stationary environments that encodes the interactions between traffic participants with contextual information and provides a decision for safe maneuver for AV. A spatio-temporal graph models the interactions between traffic participants for predicting the future trajectories of those participants. The predicted trajectories are utilized to generate a future occupancy map around the AV with uncertainties embedded to anticipate the evolving non-stationary driving environments. Then the contextual information and future occupancy maps are input to the policy network of the GP3Net framework and trained using Proximal Policy Optimization (PPO) algorithm. The proposed GP3Net performance is evaluated on standard CARLA benchmarking scenarios with domain shifts of traffic patterns (urban, highway, and mixed). The results show that the GP3Net outperforms previous state-of-the-art imitation learning-based planning models for different towns. Further, in unseen new weather conditions, GP3Net completes the desired route with fewer traffic infractions. Finally, the results emphasize the advantage of including the prediction module to enhance safety measures in non-stationary environments.

Q‐EANet: Implicit social modeling for trajectory prediction via experience‐anchored queries

AbstractAccurately predicting the future trajectory and behavior of traffic participants is crucial for the maneuvers of self‐driving vehicles. Many existing works employed a learning‐based “encoder‐interactor‐decoder” structure, but they often fail to clearly articulate the relationship between module selections and real‐world interactions. As a result, these approaches tend to rely on a simplistic stacking of attention modules. To address this issue, a trajectory prediction network (Q‐EANet) is presented in this study, which integrates GRU encoders, MLPs and attention modules. By introducing a new explanatory rule, it makes a contribution to interpretable modeling, models the entire trajectory prediction process via an implicit social modeling formula. Inspired by the anchoring effect in decision psychology, the prediction task is formulated as an information query process that occurs before traffic participants make decisions. Specifically, Q‐EANet uses GRUs to encode features and utilizes attention modules to aggregates interaction information for generating the target trajectory anchors. Then, queries are introduced for further interaction. These queries, along with the trajectory anchors with added Gaussian noise, are then processed by a GRU‐based decoder. The final prediction results are obtained through a Laplace MDN. Experimental results on the several benchmarks demonstrate the effectiveness of Q‐EANet in trajectory prediction tasks. Compared to the existing works, the proposed method achieves state‐of‐the‐art performance with only simple module design. The code for this work is publicly available at https://github.com/Jctrp/socialea.

Uncertainties in Onboard Algorithms for Autonomous Vehicles: Challenges, Mitigation, and Perspectives

Autonomous driving is considered one of the revolutionary technologies shaping humanity’s future mobility and quality of life. However, safety remains a critical hurdle in the way of commercialization and widespread deployment of autonomous vehicles on public roads. Safety concerns require the autonomous driving system to handle uncertainties from multiple sources that are either preexisting, e.g., the stochastic behavior of traffic participants or scenario occlusion, or introduced as a result of processing, e.g., the application of neural networks. Thus, it is crucial to analyze the sources of uncertainties and quantify the risks associated with them, including the propagated risks that accumulate in the decision-making system. In this context, this paper provides an overview of uncertainty challenges and state-of-the-art techniques for mitigating these challenges. We argue that the uncertainties mainly originate from two aspects: 1) the external traffic environment, and 2) the internal autonomous driving system. Specifically, this paper first analyzes the safety challenges caused by the uncertainties and summarizes their sources. In addition, the corresponding techniques that mitigate and quantify the risk of uncertainties are presented. Finally, research perspectives are highlighted to facilitate future studies for guaranteeing the safety of autonomous vehicles.

Improve generalization of driving policy at signalized intersections with adversarial learning

Intersections are quite challenging among various driving scenes wherein the interaction of signal lights and distinct traffic actors poses great difficulty to learn a wise and robust driving policy. Current research rarely considers the diversity of intersections and stochastic behaviors of traffic participants. For practical applications, the randomness usually leads to some devastating events, which should be the focus of autonomous driving. This paper introduces an adversarial learning paradigm to boost the intelligence and robustness of driving policy for signalized intersections with dense traffic flow. Firstly, we design a static path planner which is capable of generating trackable candidate paths for multiple intersections with diversified topology. Next, a constrained optimal control problem (COCP) is built based on these candidate paths wherein the bounded uncertainty of dynamic models is considered to capture the randomness of driving environment. We propose adversarial policy gradient (APG) to solve the COCP wherein the adversarial policy is introduced to provide disturbances by seeking the most severe uncertainty while the driving policy learns to handle this situation by competition. Finally, a comprehensive system is established to conduct training and testing wherein the perception module is introduced and the human experience is incorporated to solve the yellow light dilemma. Simulation results indicate that the trained policy can handle the signal lights flexibly meanwhile realizing smooth and efficient passing with a humanoid paradigm. Besides, APG enables a large-margin improvement of the resistance to the abnormal behaviors and thus ensures a high safety level for the autonomous vehicle.

Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction

Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. Several methodologies have been proposed to solve prediction problems under different traffic situations. These works usually combine agent trajectories with either color-coded or vectorized high definition (HD) map as input representations and encode this information for behavior prediction tasks. However, not all the information is relevant in the scene for the forecasting and such irrelevant information may be even distracting to the forecasting in certain situations. Therefore, in this paper, we propose a novel generic representation for various driving environments by taking the advantage of semantics and domain knowledge. Using semantics enables situations to be modeled in a uniform way and applying domain knowledge filters out unrelated elements to target vehicle’s future behaviors. We then propose a general semantic behavior prediction framework to effectively utilize these representations by formulating them into spatial-temporal semantic graphs and reasoning internal relations among these graphs. We theoretically and empirically validate the proposed framework under highly interactive and complex scenarios, demonstrating that our method not only achieves state-of-the-art performance, but also processes desirable zero-shot transferability.

TrajGen: Generating Realistic and Diverse Trajectories With Reactive and Feasible Agent Behaviors for Autonomous Driving

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can be used for validation and verification of self-driving system performance without relying on expensive and time-consuming real-world testing. Existing simulators rely on heuristic-based behavior models for background vehicles, which cannot capture the complex interactive behaviors in real-world scenarios. Meanwhile, existing learning-based methods are typical insufficient, yielding behaviors of traffic participants that frequently collide or drive off the road especially for a long horizon. To address this issue, we propose TrajGen, a two-stage trajectory generation framework, which can capture more realistic and diverse behaviors directly from human demonstration. In particular, TrajGen consists of the multi-modal trajectory prediction stage and the reinforcement learning based trajectory modification stage. In the first stage, we propose a novel auxiliary RouteLoss for the trajectory prediction model to generate multi-modal diverse trajectories in the drivable area. In the second stage, reinforcement learning is used to track the predicted trajectories while avoiding collisions, which can improve the reactivity and feasibility of generated trajectories. In addition, we develop a simulator I-Sim that can provide support for data-driven agent behavior simulation and train reinforcement learning models in parallel based on naturalistic driving data. The vehicle model in I-Sim can guarantee that the generated trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give comprehensive metrics to evaluate generated trajectories for simulation scenarios, which shows that TrajGen outperforms either trajectory prediction or inverse reinforcement learning in terms of fidelity, reactivity, feasibility, and diversity.

Modeling Cellular Network Infrastructure in SUMO

Communication networks are becoming an increasingly important part of the mobility system. They allow traffic participants to be connected and to exchange information related to traffic and roads. The information exchange impacts the behavior of traffic participants, such as the selection of travel routes or their mobility dynamics. Considering infrastructure-based networks, the information exchange depends on the availability of the network infrastructure and the quality of the communication links. Specifically in urban areas, today’s 4G and 5G networks deploy small cells of high capacity, which do not provide ubiquitous cellular coverage due to their small range, signal blocking, etc. Therefore, the accurate modeling of the network infrastructure and its integration in simulation scenarios in microscopic traffic simulation software is gaining relevance. Unlike traffic infrastructure, such as traffic lights, the simulation of a cellular network infrastructure is not natively supported in SUMO. Instead, the protocols, functions and entities of the communication system with the physical wireless transmission are modeled in a dedicated and specialized network simulator that is coupled with SUMO. The disadvantage of this approach is that the simulated SUMO entities, typically vehicles, are not aware which portions of the roads are covered by wireless cells and what quality the wireless communication links have. In this paper, we propose a method for modeling the cellular infrastructure in SUMO that introduces a cellular coverage layer to SUMO. This layer models cell sites in a regular hexagonal grid, where each site is served by a base station. Following commonly accepted guidelines for the evaluation of cellular communication system, the method facilitates standardized and realistic modeling of the cellular coverage, including cell sites, antenna characteristics, cell association and handover. In order to ease the applicability of the method, we describe the work flow to create cell sites. As a representative case, we have applied the method to InTAS, the SUMO Ingolstadt traffic scenario and applied real data for the cellular infrastructure. We validate the approach by simulating a Cellular V2X system with sidelink connectivity in an urban macro cell environment by coupling SUMO enhanced by the proposed connectivity sublayer with ARTERY-C, a network simulator for Cellular V2X. As a proof-of-concept, we present a signal-to-interference noise ratio (SINR) coverage map and further evaluate the impact of different types of interference. We also demonstrate the effect of advanced features of cellular networks such as inter-cell interference coordination (ICIC) and sidelink communication modes of Cellular V2X with dynamic switching between the in-coverage and out-of-coverage mode.

Analysis of Child Traffic Safety near Primary School Areas Using UAV Technology

Road safety in primary school areas is a delicate issue due to the vulnerability of children as road users. They are inexperienced traffic participants and sometimes their behavior in traffic situations is unpredictable. This paper reports a safety analysis conducted using video processing of conflict trajectories of vehicles and active transportation users (AT users). The videos were collected using unmanned aerial vehicles (UAVs) as this technology does not affect the actual behavior of traffic participants. Due to their airborne position, UAVs can conveniently gather information about driving behaviors and the exact positions of various participants. The safety analysis was conducted using surrogate indicators suitable for evaluating the risk of potential collisions as they incorporate both spatial proximity and speed. Three conflict indicators were used in the safety analysis: (i) time-to-collision, (ii) post-encroachment time, and (iii) heavy braking. The methodology was tested in a primary school area in the city of Zagreb. With the applied methodology, a total of 43 potential conflicts were identified in the school area (14 time-to-collision, 23 post-encroachment time, and 6 heavy braking). Based on the determined potential conflicts, safety measures were proposed to decrease the number and severity of potential conflicts and to increase traffic safety near school areas.

Research of time headways at roundabout approaches

It is known that traffic flow characteristics have significant influence at the capacity of all functional segments of the road and street network. One of the most important traffic flow parameters which affect the capacity of roundabouts is follow-up headway at minor approaches of roundabouts. This traffic flow parameter, like the most others, depends on driver behaviour, i.e. local traffic conditions. This paper presents the research results related to follow-up headway at three roundabouts obtained by the photographic method. This data collection method is chosen because its application completely eliminates the impact of research at behaviour of traffic participants. After the research, a representative sample is formed and its processing and analysis led to conclusions about the value of follow-up headway at roundabouts which can be applied in standard procedures for capacity calculation at roundabouts in midsize cities of our region.

Interactive Behavior Prediction for Heterogeneous Traffic Participants in the Urban Road: A Graph-Neural-Network-Based Multitask Learning Framework

Effectively predicting interactive behaviors of traffic participants in the urban road is the key to successful decision-making and motion planning of intelligent vehicles. In this article, based on the data collected from vehicle on-board sensors, a graph-neural-network-based multitask learning framework (GNN-MTLF) is proposed to accurately predict trajectories of traffic participants with interactive behaviors. The interactive behavior considered in this research includes interactive events and trajectories that are modeled as spatial-temporal graphs using the GNN. Under the GNN-MTLF, the prediction process contains two main parts: recognition of interactive events and prediction of interactive trajectories. An integrated loss function is designed for multitask learning with the purpose of prediction and recognition. The proposed framework is verified using naturalistic driving data in the urban road. Experimental results show a superior performance of the GNN-MTLF compared to baseline methods and the potential for improving the road mobility.

Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment

The prediction of lane change intention of vehicles is an important part of the decision planning and control systems of intelligent vehicles. In the dynamic and complex traffic environment, the behaviors of traffic participants interact and influence each other. In lane change prediction, it is necessary to study the predicted vehicle and surrounding vehicles as an interactive correlation system. Otherwise, great errors are made in the motion prediction. Based on this, the motion state of the predicted vehicle, the position relationship between the predicted vehicle and lane, as well as the motion state of vehicles around the predicted vehicle are considered systematically in this paper, and the prediction of lane change intention of vehicles is studied. The influence of the three above-mentioned factors on the prediction of lane change intention is analyzed in this paper. On the basis of screening the prediction features of lane change intention, the lane change intention of vehicles is predicted by a feed-forward neural network. The data collected by the virtual driving experiment platform are divided into a training set, a verification set, and a test set. The neural network parameters of vehicles’ lane change intentions are identified by a training set, and the effect of prediction is tested by a verification set and a test set. The results show that the accuracy of the prediction model is high. The model is compared with the model of common features at the present stage and the model based on a Support Vector Machine, and the results show that the accuracy of the prediction model proposed in this paper was improved by 6.4% and 2.8%, respectively, compared with the two models. Finally, the virtual driving experiment platform was used to predict the lane change intention of the front vehicle and the vehicle in the left adjacent lane. The results show that, based on the same model and input features, the lane change intention of the front vehicle and the vehicle in the left adjacent lane can be predicted by the model at 2.8 s and 3.4 s before the lane change, and the model is a certain generality for the prediction of lane change intention of adjacent vehicles.

Interaction-Aware Intention Estimation at Roundabouts

Roundabouts have many benefits when compared with traditional signal-controlled intersections: improve safety, reduce delay, improve traffic flow, are less expensive and occupy less area. The behavior of traffic participants is full of uncertainties in the real world. An automated system that relies only on its perception is unable to safely enter the roundabout until a large gap occurs or the vehicle approaching has actually left the roundabout or passed the conflict area. In order to improve the driving quality, autonomous vehicles should be able to infer the correct intention at roundabout as early as possible. In this work, a method to classify the intentions of the surrounding vehicles at unsignalized roundabouts is proposed. For each vehicle at the scene, a Dynamic Bayesian Network is instantiated and the intentions are inferred using a particle filter.

LAW -theory and practice No. 4 / 2021

The exogenous characteristics of traffic participants, such as: alcoholism, fatigue and psychoactive substances, have a significant impact on a safe behaviour in traffic, especially the motor vehicle drivers. In that sense, they represent the factors that can directly influence the unsafe behaviour in traffic. The influence of the exogenous characteristics on a safe behaviour of traffic participants has been analysed on the basis of the statements in documents as well as the results of numerous empirical studies. For the purposes of this paper, there have been selected and analysed the statistical data on the influential factors of traffic accidents for the period from 2010 to 2019 in Republic of Serbia. After reviewing the statistical data, both a qualitative and quantitative content analyses were performed. In addition to the statistical method, there were also used the content analysis methods, empirical method, descriptive method, comparative methods, as well as deduction and induction methods, in order to analyse the trend and presence of these influential factors of traffic accidents in Republic of Serbia. The statistical data being processed in this paper, originated from the database of the Traffic Safety Agency of Republic of Serbia. The results of the research show the extent to which the exogenous characteristics affect a safe behaviour of traffic participants, as well as the severity of the consequences of traffic accidents. Observed individually, the listed characteristics have a different intensity of influence on a safe behaviour in traffic. In this regard, from the external characteristics of traffic delinquents, the most common is the drunk driving as an influential factor, which especially affects the severity of traffic accidents in Republic of Serbia.

A közlekedési morál helyreállításának lehetőségei Magyarországon

Jelen tanulmány a fennálló, mindenki számára ismert helyzet – statisztikai adatokkal történő – bemutatását követően a megoldások lehetőségét kívánja számba venni, miután néhány nemzetközi szabályozási megoldást is számba vesz és a múltba is visszatekint. Mindezek mellett a közúti közlekedési balesetek magas számának okait is megpróbálja feltárni. Tekintettel arra, hogy olyan társadalmi problémáról van szó, amely valamennyi állampolgárt érint, kiemelkedően fontos a közúti közlekedési balesetek megelőzése érdekében a közlekedés szereplőinek általános viselkedését, szokásait, esetenkénti magatartását vizsgálat tárgyává tenni. A szerzők egyelőre csak elméleti szinten közelítenek e rendkívül összetett jelenséghez, de a cikkben leírásra kerül néhány jövőbe mutató, a megelőzést szolgáló technikai lehetőség, elképzelés.

Application of cluster analysis in the behaviour of traffic participants relating to the use of safety systems and mobile phones

This paper presents a cluster analysis related to the behavior of traffic participants in relation to the use of safety systems and mobile phones. The data on traffic behavior were downloaded from an open data portal in Serbia. Three types of cluster analysis have been applied: hierarchical clustering, Bayesian Information Criterion (BIC) clustering and model clustering. The obtained results point to the various possibilities of using these three clustering methods in the field of traffic and suggest further research.

Stochastic Forecasting of Vehicle Dynamics Using Sequential Monte Carlo Simulation

Predictive control is a popular approach for further improving the efficiency and performance of vehicular systems enabling intelligent systems behavior appropriate to the driving situation. To calculate such control strategies, the future vehicle dynamics or subsequent states have to be predicted. We introduce a stochastic framework based on an explanatory model and stochastic processes to predict future vehicle dynamics with road network data. The distributions of the future states are approximated using sequential Monte Carlo simulation. The proposed approach enables stochastic forecasts incorporating uncertain driver behavior and available road data. Parameter inference is shown for exemplary real-drive test data, and predictive performance is evaluated using commonly used reference models. The results show that the explanatory model provides more specific information than time-series models do, still considering the uncertainty in the driver's behavior or the situation. The framework can be applied with predictive control algorithms enabling intelligent control of vehicular systems. Furthermore, the framework or parts of it may be usable for other applications like predicting behavior of traffic participants or general characterization of driver behavior.

Scientific Basis of the Expert System of Road Safety

Developing an objective approach to take decisions aiming at reducing the number of injuries and damage inflicted by the road traffic incidents and accidents has become a top priority issue. A rational solution deals with setting up an intelligent road safety expert system comprising a complex software system that allows gathering the knowledge and applying it to improve road safety control.The methods to assess comprehensively the road traffic safety status were developed. The status is determined according to the behavior of traffic participants and conditions of the road facilities. The paper suggests using the theory of fuzzy sets as the most rational research tool.

ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES

Abstract. For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably, e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time tables of nearby public transportation.