Vehicle Behavior Research Articles

Nonverbal Communication of In-Vehicle Humanoid Robot to Communicate Autonomous Vehicle Behavior

Nonverbal Communication of In-Vehicle Humanoid Robot to Communicate Autonomous Vehicle Behavior

CRUMPLE ZONE MODELLING OF PASSENGER VEHICLE USING MULTIPLE KELVIN MODEL AND OPTIMIZED WITH PARTICLE SWARM OPTIMIZATION

This paper presents the mathematical modelling of a vehicle crash system using a mass-spring-damper approach to simulate the behavior of a real vehicle during a frontal impact. A Multiple Kelvin model is developed to represent a real vehicle crash situation where the impact is introduced on the frontal vehicle body. The modelling process of Multiple Kelvin model is based on a single Kelvin model that developed into a set of seven mass-spring-damper systems representing the front crumple zone of a real vehicle. The model is then optimized for the parameters k and c using an optimization method namely Particle Swarm Optimization (PSO) algorithm in MATLAB-Simulink. The simulation results demonstrate deformation and acceleration responses closely follow the previous experimental results where the parameters namely Ni, Np, and iw are varied to enhance the model's precision through the calculation of error of 12.15 using parameters Ni = 80, Np = 40 and iw = 0.9.

Robust Static Output Feedback Control of a Semi-Active Vehicle Suspension Based on Magnetorheological Dampers

This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle states, an MR damper current controller that only considers the suspension deflection and deflection rate is proposed. A linear matrix inequality problem is formulated to design the current controller, with the objective of enhancing ride safety and comfort while guaranteeing vehicle stability and robustness against any road disturbance. A series of experiments demonstrates the enhanced performance of the proposed MR damper model, which exhibits greater accuracy than other state-of-the-art damper models, such as Bingham or bi-viscous. An evaluation of the vehicle behavior under two simulated road scenarios has been conducted to demonstrate the performance of the proposed output feedback MR damper-based semi-active suspension system.

Design Principles to Reduce Vehicle Pocketing at Guardrail-to-Concrete Barrier Transitions

Road restraint systems (RRSs) on European roads are provided by several manufacturers and, hence, lead to differences in geometry, material, and mode of operation. Focusing on the combination of soft steel RRSs with relatively stiffer concrete RRSs, it is vital to consider the potentially critical deformation kinematics during vehicle impacts, such as vehicle pocketing. Since a statutory test procedure was not introduced until mid-2024, much of the transition construction (TC) on Austrian roads has remained untested. Knowledge of the design features to be implemented during the refurbishment of such TCs is of great interest. The main focus of this study was to derive constructive measures (CMs) that increase traffic safety and are applicable to various TCs already installed on roads. The first step involved deriving design principles whose implementations in TCs reduce the risk of critical vehicle or RRS behavior. Based on finite element simulations, the functionality of a TC featuring all derived design principles was examined. The effect of each individual CM was analyzed in a parameter study. The results from a TB61 impact simulation on the derived TC showed the effectiveness of CMs, achieving smooth vehicle redirection. Vehicle pocketing was limited to a minimum, and neither penetration of the TC nor rollover of the vehicle was observed. The analysis of the influence of each CM indicated positive, and in some cases, negative effects. The working width was mainly positively influenced by the compaction of the posts, an additional steel bar, and the chamfering of the first concrete element. A rather diverse picture is drawn regarding the influence on the tensile forces in the guardrails. Some CMs had both positive and negative effects on the distribution of forces in the upper and lower guardrails. Nevertheless, all CMs had positive effects on the tensile forces in the coupling. The chamfering of the first concrete element was the most effective measure to prevent vehicle pocketing. However, through the combination of all CMs, the positive effects predominated, ensuring the functionality of the TC as a whole. This study provides basic insights into the effectiveness of constructive measures, which can serve as a reference for the renovation of in-service TCs or in the development phase of new TCs to be certified.

Reconstructing Road Roughness Profiles Using ANNs and Dynamic Vehicle Accelerations

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy.

Demand management of plug-in electric vehicle charging station considering bidirectional power flow using deep reinforcement learning

The recent development in plug-in electric vehicle technology has increased its popularity. Plug-in electric vehicles are widely used for their environment-friendly nature and they contribute to the reduction in global warming. With the increasing number of plug-in electric vehicles, charging coordination becomes essential for managing the charging station demand. The random charging behaviour of plug-in electric vehicles makes it a difficult task. This paper proposes a novel demand management strategy of charging stations. The proposed strategy supports the grid and reduces its burden during peak load. Deep-Q network based deep reinforcement learning is used in the proposed strategy. It is a value based deep reinforcement learning algorithm that approximates the Q value function using deep neural network. The deep reinforcement schedules the charging and discharging of plug-in electric vehicles to optimize the cost and manage the charging station load. Deep reinforcement learning enhances charging coordination by dynamically optimizing charging schedules according to real-time conditions and user preferences, thereby increasing efficiency and better integration with the grid. The reward function in deep reinforcement learning is designed based on the power price and demand of the charging station. A discount factor is introduced based on the service time to make the charging coordination efficient. A case study using dynamic pricing is carried out to validate the proposed strategy. The results prove that the proposed strategy optimizes charging and discharging costs and manages charging station demand efficiently. It is also observed that the proposed strategy is fast and incurs less computational cost.

Associations of Battery Cell Voltage Consistency with Driving Behavior of Real-world Electric Vehicles

Associations of Battery Cell Voltage Consistency with Driving Behavior of Real-world Electric Vehicles

Human-like Decision Making for autonomous lane change driving: a hybrid inverse reinforcement learning with game-theoretical vehicle interaction model

The development of automated driving vehicles aims to provide safer, comfortable, and more efficient mobility options. However, the decision-making control of autonomous vehicles still embraces limitations on human performance mimicry. These limitations become particularly evident in complex and unfamiliar driving scenarios, where weak decisionmaking abilities and poor adaptation of vehicle behaviour are prominent issues. This paper proposes a game-theoretic decisionmaking algorithm for human-like driving in the vehicle lane change scenario. Firstly, an inverse reinforcement learning (IRL) model is used to quantitatively analyse the lane change trajectories of the natural driving dataset, establishing the human-like human cost function. Subsequently, joint safety, comfort to build the comprehensive decision cost function. Use the combined decision cost function to conduct a non-cooperative game of vehicle lane changing decision to solve the optimal decision of host vehicle lane changing. The host vehicle lane-changing decision problem is formulated as a Stackelberg game optimization problem. To verify the feasibility and effectiveness of the algorithm proposed in this study, a lane change test scenario has been established. Firstly, we analyse the human-like decision-making model derived by the maximum entropy inverse reinforcement learning algorithm to verify the effectiveness and robustness of the IRL algorithm. Secondly, the human-like game decisionmaking algorithm in this paper is validated by conducting an interactive lane-changing experiment with obstacle vehicles of different driving styles. The experimental results prove that the human-like driving decision-making model proposed in this study can make lane-changing behaviours in line with human driving patterns in lane-changing scenarios of expressway.

Dynamic Analysis of a Vehicle–Bridge System Under Excitation of Random Road Irregularities

This paper presents a comprehensive study of the dynamic response analysis of vehicle–bridge coupled systems, with detailed simulation methods for the vehicles, bridges, and wheel–road coupling relationships. The simulation of the entire vehicle–bridge coupling system is carried out using the open-source finite element analysis platform OpenSees. A novel three-dimensional wheel–road coupling element is introduced to model the interactions between the wheel and road nodes. This element facilitates precise computation of the dynamic responses within the vehicle–bridge coupled system, including both vehicle and bridge behaviors, along with the interaction forces between the wheels and the bridge surface. The coupling element consists of a wheel node and all potential road nodes on the bridge surface that the wheel may traverse. This configuration preserves the finite element model of the entire vehicle–bridge coupled system throughout the vehicle’s movement, thereby improving the efficiency of numerical simulations of vehicle–road interactions. The study accounts for the impact of random road irregularities on the dynamic responses of both the vehicle and the bridge. These irregularities are treated as input parameters for the wheel–road coupling element rather than being accounted for through the wheel–road interaction constraint equations, thereby improving the convenience of simulating random road irregularities.

Assessing the Impact of Calendar Events upon Urban Vehicle Behaviour and Emissions Using Telematics Data

Assessing the Impact of Calendar Events upon Urban Vehicle Behaviour and Emissions Using Telematics Data

Topology sensing of FANET under missing data

The topological structure of a flying ad hoc network (FANET) is crucial to understand, explain, and predict the behavior of unmanned aerial vehicle (UAV) swarms. Most studies focusing on topology sensing use perfect observations and complete datasets. However, the received signal dataset, being non-cooperative, commonly encounters instances of missing data, causing the performance of the existing algorithms to degrade. We investigate the issue of topology sensing of FANET based on external observations and propose a topology sensing method for FANET with missing data while introducing link-prediction methods to correct the topology inference results. First, we employ multi-dimensional Hawkes processes to model the communication event sequence in the network. Subsequently, to solve the problem in which the binary decision threshold is difficult to determine and cannot be adapted to the application scenario, we propose an extended multi-dimensional Hawkes model suitable for FANET and use the maximum likelihood estimation method for topology inference. Finally, to solve the problem of the low accuracy of inference results owing to missing data, we perform community detection on the observation network and combine the community detection and inference results to construct a mixed connection probability matrix, based on which we perform topology correction. The results of the analysis show that the topology sensing method proposed in this study is robust against missing data, indicating that it is an effective solution for solving this problem.

Efficient Data Collection Strategy for Modeling the Dynamics of Floating Robotic Vehicles Using Neural Networks

As the hydrodynamic model of a surface vehicle is highly nonlinear and of high dimension, an extensive set of experiments is required to estimate the parameters of the model. Typically, the dynamic behavior of a surface vehicle is investigated through experimental tests such as planar motion mechanisms or free running tests in specialized test facilities. However, the cost of such tests is very high, and it is challenging to design effective and efficient test conditions, especially when the model is represented by numerous parameters such as neural networks. In this paper, we propose an efficient data collection strategy for modeling the dynamics of a floating robotic vehicle using neural networks. For generalized modeling, it is important to collect diverse data by exploring all the reachable state space of the vehicle. To achieve this, a data collection policy is built based on sampling-based planning toward reducing the uncertainty of the neural network-based model. The proposed method allows for collecting more comprehensive data than other commonly used random-based data collection policies. We show experimentally that the proposed method enables more accurate modeling of both fully actuated and under-actuated floating robotic vehicles, as demonstrated by the effective execution of various tasks.

An innovative virtual sensing system for the vehicle-centric evaluation of emissions in the sustainable mobility transition

The objective of the research is to develop a new methodology capable of quantifying the emissions of internal combustion engine cars based not only on their technology but also on the dynamic behavior of the individual vehicle. No complicated exhaust measurement apparatus is needed. Employing just a telematic device equipped with GNSS and an inertial measurement unit, which can be easily attached to the vehicle, we demonstrate how individualised, accurate climate-altering emissions result by using figures extracted from European test databases, appropriately combined with real travelled distances, speeds, and driving style of each individual car. Pollutant emissions are, in this paper, also assessed though in a more general manner, and this represents a starting point for a future complete vehicle-centric emissions evaluation. This research demonstrates that circulating car emissions have to be assessed considering not only the Euro class and type of fuel, but also how the vehicle is driven. In this work a new, implementation oriented methodology is proposed in order to properly merge average European standard emission data with car-based telematic measures to highlight a new paradigm for regulating in a more conscious way the transition of the internal combustion engine car park towards a large scale, sustainable mobility ecosystem. This approach would place greater responsibility on the driver, without leaving anyone behind and without compelling the car scrapping solely based on the Euro class of its engine. The key contribution is ultimately to establish an easy to handle methodology enabling targeted and behavior-based traffic management policies. In fact, through a massive telematics recent dataset, the proposed virtual emission sensing system reveals that an extensive and inefficient use of eco-friendly vehicles produces much more greenhouse gas emissions than older vehicles driven in an environmentally responsible way.

Want a ride? Attitudes towards autonomous driving and behavior in autonomous vehicles

SummaryResearch conducted previously has focused on either attitudes toward or behaviors associated with autonomous driving. In this paper, we bridge these two dimensions by exploring how attitudes towards autonomous driving influence behavior in an autonomous car. We conducted a field experiment with twelve participants engaged in non-driving related tasks. Our findings indicate that attitudes towards autonomous driving do not affect participants’ driving interventions in vehicle control and eye glance behavior. Therefore, studies on autonomous driving technology lacking field tests might be unreliable for assessing the potential behaviors, attitudes, and acceptance of autonomous vehicles.

Mechanism and modeling of car-following behavior under local multi-vehicle influence

Aiming at the limitations of single-dimensional car-following model to describe vehicle car-following behavior in local multi-vehicle environment, the mechanism of vehicles in adjacent lane influencing the subject vehicle car-following behavior is explored, and a vehicle car-following model more suitable for local multi-vehicle environment is attempted to be established. The driving behavior variable reflecting the influence of vehicles in adjacent lane was determined by correlation analysis. Vortisch indicator of similarity(VIS) was used to characterize the influence of vehicles in adjacent lane on car-following behavior. Chi-square independent test and kernel density curve were used to determine the VIS demarcation threshold reflecting whether the influence is significant. Recursive feature elimination was used to screen the variables related to car-following samples significantly affected by vehicles in adjacent lane. The influence mechanism of variables was determined according to the statistical analysis results. Based on the mechanism proposed, the car-following model suitable for local multi-vehicle environment was constructed and its prediction effect was evaluated. Results show that VIS between the speed of subject vehicle and following vehicle in adjacent lane can characterize the influence and the VIS threshold is 0.668. It is concluded that the attention mechanism and memory effect can explain the influence mechanism of car-following behavior in multi-vehicle environment. Considering the attention mechanism and memory effect, RMSE decrease by 65%in Full Velocity Difference Model (FVD) model and 62%in Intelligent Driver Model (IDM) model, MAE decrease by 65%and 59%and R2 increased by 180%and 288%respectively, which proved the rationality of the attention mechanism and memory effect in explaining the car following behavior of subject vehicle in local multi-vehicle environment.

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

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

An Ontology-Based Vehicle Behavior Prediction Method Incorporating Vehicle Light Signal Detection.

Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results. To address the limitations of the above deep learning methods in the field of vehicle behavior prediction, this paper proposes a front vehicle behavior prediction method that combines deep learning techniques with ontology reasoning. Specifically, YOLOv5s is first selected as the base model for recognizing the brake light status of vehicles. In order to further enhance the performance of the model in complex scenes and small target recognition, the Convolutional Block Attention Module (CBAM) is introduced. In addition, so as to balance the feature information of different scales more efficiently, a weighted bi-directional feature pyramid network (BIFPN) is introduced to replace the original PANet structure in YOLOv5s. Next, using a four-lane intersection as an application scenario, multiple factors affecting vehicle behavior are analyzed. Based on these factors, an ontology model for predicting front vehicle behavior is constructed. Finally, for the purpose of validating the effectiveness of the proposed method, we make our own brake light detection dataset. The accuracy and mAP@0.5 of the improved model on the self-made dataset are 3.9% and 2.5% higher than that of the original model, respectively. Afterwards, representative validation scenarios were selected for inference experiments. The ontology model created in this paper accurately reasoned out the behavior that the target vehicle would slow down until stopping and turning left. The reasonableness and practicality of the front vehicle behavior prediction method constructed in this paper are verified.

An Algorithm for Predicting Vehicle Behavior in High-Speed Scenes Using Visual and Dynamic Graphical Neural Network Inference

Accidents caused by vehicles changing lanes occur frequently on highways. Moreover, frequent lane changes can severely impact traffic flow during peak commuting hours and on busy roads. A novel framework based on a multi-relational graph convolutional network (MR-GCN) is herein proposed to address these challenges. First, a dynamic multilevel relational graph was designed to describe interactions between vehicles and road objects at different spatio-temporal granularities, with real-time updates to edge weights to enhance understanding of complex traffic scenarios. Second, an improved spatio-temporal interaction graph generation method was introduced, focusing on spatio-temporal variations and capturing complex interaction patterns to enhance prediction accuracy and adaptability. Finally, by integrating a dynamic multi-relational graph convolutional network (DMR-GCN) with dynamic scene sensing and interaction learning mechanisms, the framework enables real-time updates of complex vehicle relationships, thereby improving behavior prediction’s accuracy and real-time performance. Experimental validation on multiple benchmark datasets, including KITTI, Apollo, and Indian, showed that our algorithmic framework achieves significant performance improvements in vehicle behavior prediction tasks, with Map, Recall, and F1 scores reaching 90%, 88%, and 89%, respectively, outperforming existing algorithms. Additionally, the model achieved a Map of 91%, a Recall of 89%, and an F1 score of 90% under congested road conditions in a self-collected high-speed traffic scenario dataset, further demonstrating its robustness and adaptability in high-speed traffic conditions. These results show that the proposed model is highly practical and stable in real-world applications such as traffic control systems and self-driving vehicles, providing strong support for efficient vehicle behavior prediction.

Cooperative Adaptive Cruise Control Platoon Fleet Formation Model Considering Efficiency and Stability

In the existing formation model, vehicles in the same lane or adjacent lane are regarded as the structure, and the driving behavior of vehicles is studied from the perspectives of safety, speed consistency, and stability, and the speed control model is proposed from the perspective of vehicles themselves, to obtain a stable fleet with the same distance and speed. However, in this process, the initial condition of the vehicle, the traffic flow environment, and the efficiency of the fleet formation are less considered. Therefore, based on summarizing the existing fleet building model, this paper puts forward the rapid construction model and algorithm of a cooperative adaptive cruise control platoon fleet. One of the important goals of forming a team is to enter the team with the smoothest trajectory in the shortest time. Therefore, this chapter studies the trajectory optimization of the vehicle formation process from the perspective of vehicle dynamics.

Aerostructural Analysis of a Deployable Aeroshell in Transonic Flow

The deployable aeroshell, a contemporary reentry system, utilizes a thin flexible membrane surface sustained by pressurized gas to efficiently decelerate a reentry vehicle at high altitudes while enabling low-ballistic-coefficient flight. When an in-flight aerodynamic force is applied, the membrane structure undergoes large deformation that may affect its performance. Hence, a coupled analysis that considers the feedback effect of structural deformation is essential for accurately predicting the behavior of such reentry vehicles. In this study, a numerical framework for coupled aeroelastic analysis was constructed in a partitioned manner using open-source software to elucidate the combined effect of structural deformation and compressible flow dynamics and characterize the aerodynamic performance of reentry vehicles with inflatable structures. The results revealed that the membrane surface was deformed elastically by the aerodynamic force owing to the large difference in pressure distribution between the front and back of the aeroshell. Fluctuating behavior was observed in the aerodynamic coefficients owing to the small-amplitude oscillation of the capsule. This oscillation was induced by the large wake behind the vehicle. Several wrinkles and concave-shaped depressions formed on the membrane surface, which exhibited a circumferential movement tendency with time.