Vehicle Behavior Research Articles

A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions.

Autonomous vehicles (AVs) rely heavily on sensors to perceive their surrounding environment and then make decisions and act on them. However, these sensors have weaknesses, and are prone to failure, resulting in decision errors by vehicle controllers that pose significant challenges to their safe operation. To mitigate sensor failures, it is necessary to understand how they occur and how they affect the vehicle's behavior so that fault-tolerant and fault-masking strategies can be applied. This survey covers 108 publications and presents an overview of the sensors used in AVs today, categorizes the sensor's failures that can occur, such as radar interferences, ambiguities detection, or camera image failures, and provides an overview of mitigation strategies such as sensor fusion, redundancy, and sensor calibration. It also provides insights into research areas critical to improving safety in the autonomous vehicle industry, so that new or more in-depth research may emerge.

A Systematic Review of Consumer Perception and Purchasing Behavior of Electric Vehicles

Purpose: This literature review aims to analyze the factors influencing consumer perception and purchasing behavior towards electric vehicles (EVS), examining both the opportunities and challenges associated with EV adoption. Design/Methodology/Approach: This literature review incorporates data from diverse sources, including textbooks, scholarly articles from Google Scholar, ResearchGate, and Academia databases, and a thesis retrieved from the Shodhganga repository. Findings/Result: Environmental concerns, government incentives, and technological advancements drive electric vehicle (EV) adoption, though high costs, range anxiety, and inadequate charging infrastructure pose challenges. Addressing psychological factors and enhancing battery development, charging networks, and financial incentives are essential for wider EV acceptance. Originality/Value: This review synthesizes a wide range of research findings on EV adoption, highlighting both the progress made and the remaining challenges. It provides a comprehensive overview of the factors influencing consumer perception and behavior towards EVs, serving as a valuable resource for researchers, policymakers, and industry stakeholders. Paper Type: Systematic literature review

Queuing‐based energy‐efficient processing algorithm for smart transportation through V2V communication

SummaryApplications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. Computational offloading based on Vehicle‐to‐Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computational tasks. Therefore, a decision‐making and intelligent task offloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete‐time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy‐efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy‐efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100–500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%.

A novel risk-averse optimal scheduling strategy for active distribution networks equipped with power-to-X technologies

This paper proposes a novel risk-averse approach for scheduling of an active electrical distribution network (AEDN). The network is connected to an electric vehicle parking lot (PL) and gas and hydrogen fueling stations (GHFSs). In addition, the AEDN operator is able to participate in both day-ahead and real-time markets to supply energy demands. In order to address the uncertainties of the studied network, a two-stage stochastic programming incorporating conditional value-at-risk (CVaR) is employed to model uncertainties of wind turbine and photovoltaic output, electric vehicles behavior in PL, and GHFSs energy consumption, while a robust optimization method is used to handle the uncertainty of real-time market price. According to the numerical results, the AEDN operator can decrease the risk level by up to 1.2% while the operational cost rises by 7.5%. Furthermore, the optimal use of multiple energy storage technologies and price-sensitive shiftable electric loads (PSELs) reduces the operational cost by 7.4%.

Behavior recognition of non-motorized transport at intersections using dual-channel grid model based on disordered trajectory point data

Accurately identifying the passing and waiting behavior of pedestrians and non-motorized vehicles at intersection is essential to planning management measures for non-motorized transport. Much previous study in this field has focused on methods based on continuous individual trajectory detection, which are almost ineffective under the poor detection condition in dense traffic scenarios. To address the task, this paper establish a workable framework for inferring the probabilities of passing or waiting behavior of pedestrians and non-motorized vehicles in arbitrary space using disordered trajectory point data. First, a two-channel model is proposed to perform a formalized grid-level representation of an intersection, in which the functional attributes and occupancy characteristics of grids are comprehensively defined and quantified. Then, the quantitative characteristics of the grids are used to detect the real-world occurrence space of passing and waiting behaviors, by the clustering and expanding operations on grids. Finally, through feature transfer along path, characteristics is decomposed into passing and waiting occurrence characteristics for behavior probability computation. Results indicate that the method achieves over 91% accuracy of behavior recognition, which is better than compared methods in various Multiple Object Tracking Accuracy (MOTA). Although the method is sensitive to spatial detection conditions, it obtains steady accuracy under various target detection settings.

Research on the Driving Behavior and Decision-Making of Autonomous Vehicles (AVs) in Mixed Traffic Flow by Integrating Bilayer-GRU-Att and GWO-XGBoost Models

Research on the Driving Behavior and Decision-Making of Autonomous Vehicles (AVs) in Mixed Traffic Flow by Integrating Bilayer-GRU-Att and GWO-XGBoost Models

Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach

There are fewer simulation studies that comprehensively consider the impact of collision events due to heterogeneous driving behaviour on multi-lane traffic flow. This comprehensive study utilized multi-agent modeling to simulate the driver-vehicle-road system at the mesoscale level, integrating the characteristics of heterogeneous driving behavior. We developed a traffic flow simulation model for a three-lane urban arterial road segment and used rule-based algorithms to simulate vehicle following, lane changing, and collision behavior. A visualization simulation program was developed using NetLogo software to observe and analyze vehicle behavior. The findings revealed that collision events were the leading cause of traffic bottlenecks, impacting the continuity and coherence of traffic flow. Even after the collision event subsided and congestion eased, the average vehicle speed could not fully recover to its original level, reaching only about 70% of the maximum vehicle speed. Aggressive drivers exhibited distinct speed control strategies compared to conservative and ordinary drivers. This study demonstrates the effectiveness of multi-agent modeling in capturing the relationship between traffic bottlenecks and collision events, highlighting the influence of dynamic traffic events on driving behavior.

A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations.

Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.

Evaluation of ride quality in a passenger car with non-uniform tyre radial stiffness

This study is dedicated to examining the influence of varying radial stiffness along the circumference of tyres on a road vehicle ride quality and with a specific focus on aspects relevant to maintenance. Computer simulations employing a three-dimensional road-vehicle model, developed within commercial multi-body software, were conducted to explore the multifaceted factors influencing vehicle behaviour. These factors encompass road unevenness, driving speed, and the range of radial force variation resulting from harmonic changes in tire radial stiffness. Furthermore, simulation tests were carried out using a model of a diagnostic wheel balancing machine, which holds significance in vehicle maintenance practices. Measurements obtained from a passenger car equipped with standard tires with minimal non-uniformity, served as reference values for assessing the car's vibration levels. Additionally, selected scenarios were examined, involving vehicle with multiple wheels exhibiting non-uniform radial stiffness. The findings underscore that non-uniform radial stiffness can magnify the detrimental effects of road irregularities, potentially leading to amplified vibration levels and a heightened need for maintenance considerations.

Joining SUMO and Unreal Engine to Create a Bespoke 360 Degree Narrow Passage Driving Simulator

The use of simulators is widespread in driver behavioural research. The ability of driving simulators to achieve the high levels of behavioural fidelity desired by behavioural researchers is argued to be resultant of the physical fidelity of the simulator. Whilst attempts to maximise the physical fidelity of simulators have often been focused on the hardware capabilities of the simulator, the software of the simulator has been argued to be as important. This is because the software of a simulator controls the intelligence and the heterogeneity of the behaviours of the simulated vehicles, as well as the quality of the graphics of the simulation. Despite the importance of intelligent simulated agents, previous driving simulator studies have tended to simplify the behaviours of simulated vehicles and the scenarios that are presented to participants. This is particularly true of simulator studies investigating the decision-making of drivers at narrow passages, a relatively unregulated but hazardous situation in which two opposing vehicles must negotiate how to safely pass through a road narrowing, in which the interactive nature of the interaction has often been neglected. Following a review of the requirements for a representative narrow passage driving simulator, it is argued that co-simulation, an approach which combines multiple simulator types to create a global simulation, provides the best approach to creating intelligent simulated agents within an immersive environment for narrow passage behavioural research. As such, the development of a simulator for narrow passage behavioural research that combines SUMO and Unreal Engine is described. In particular, the development of a novel narrow passage behavioural model within SUMO that utilises previous behavioural findings is highlighted. To this end, it is argued that this approach facilitates higher levels of behavioural fidelity for narrow passage interaction studies and provides a framework for the investigation of other driver behaviours.

Sumonity: Bridging SUMO and Unity for Enhanced Traffic Simulation Experiences

This paper presents "Sumonity," an interface that bridges SUMO (Simulation of Urban MObility) and Unity, combining SUMO's robust traffic modeling capabilities with Unity's advanced graphical and physical engine, enhancing realism in traffic simulations. The study explores Sumonity's development and implementation, showcasing its capabilities. The interface offers a significant improvement in simulation fidelity by adopting a pure pursuit control approach within Unity for simulating each traffic agent. This methodological shift allows for more granular control over individual vehicle behaviors, aligning with autonomous and common vehicle dynamics. The paper also discusses the broader implications of Sumonity for future research in this field.

Experimental study of jet and cavity coupling under vertical motion of underwater vehicle

After the underwater vehicle detaches from the launch tube and starts its engine, the interaction between the tail cavity and the high-speed jet significantly impacts its motion stability and engine efficiency. Therefore, it is crucial to study the evolution mechanism of cavities in vertical motion. However, in traditional water tunnel experiments, the evolution of the cavity differs from the actual situation of vertical launch due to the influence of buoyancy. This study investigates the dynamics and motion trajectories of tail cavities and high-speed gas jets in the vertical movement of vehicles under different Froude numbers and nozzle stagnation pressure ratios through experimentation. The experimental results show two modes of tail cavity evolution: intact cavity (IC) and foam-conical cavity (FC-CC). Increasing the Froude numbers and stagnation pressure ratios facilitates the transition of the cavity from the IC mode to the FC-CC mode, effectively suppressing the formation and intensity of re-entrant jets, thereby reducing its impact on the bottom of the vehicle and enhancing motion stability. By deriving the formula for the length of the jet core area, it was found that the jet length is linearly related to the nozzle stagnation pressure, further revealing the mechanism of cavity evolution. These findings offer a new perspective for a deeper understanding and prediction of the dynamic behavior of underwater vehicles.

Enhanced UA (University of Arizona) tire model for computationally efficient and stable multibody simulations

Multibody simulations are widely used to study the dynamic behaviour of road vehicles, requiring the representation of the tire-road contact forces. The UA tire model, developed by Gim and Nikravesh, is a physical model that requires the selection of few parameters, which are easily obtained without the need of extensive testing. However, the analytical formulas of the UA tire model imply force discontinuities that are challenging in multibody simulations, affecting the numeric time integration of the accelerations and velocities, and consequently, the simulation stability and efficiency. This work proposes an enhanced UA tire model by smoothing such force discontinuities. For the purpose, the kinematic conditions that imply discontinuities are identified and a cubic smoothing function is applied. Simulation results highlight the discontinuities in the lateral force and self-aligning moment when using the original UA tire model. The use of the enhanced model within multibody simulations is significantly more efficient, without affecting the quality of the results or modifying the range of applications of the original tire model. The performance of the enhanced UA tire model is demonstrated with the tire model performance test (TMPT), which is used for the international benchmark test of tire models.

Modelling the fundamental diagram of traffic flow mixed with connected vehicles based on the risk potential field

AbstractThe fundamental diagram (FD) of traffic flow can effectively characterize the macroscopic characteristics of traffic flow and provide a theoretical foundation for traffic planning and control. The rapid development of connected vehicles (CVs) has led to changes in traffic flow characteristics. However, research on the FD of traffic flow involving CVs and non‐connected vehicles (NCVs) is still in its early stages. Most FDs do not well characterize the motion behaviour of different vehicles, nor do they study the interaction between mixed vehicles. Therefore, in this study, the FD of mixed traffic flows (i.e. with CVs and NCVs) was constructed within a unified framework. First, the car‐following behaviours of CVs and NCVs were modelled based on risk potential field theory. Subsequently, the FD of mixed traffic flows was derived based on the relationship between car‐following behaviour and the macroscopic traffic flow under steady‐state conditions. To validate the model, rigorous verifications were conducted via numerical experiments using the Monte Carlo method. The results indicate significant agreement between the scatter plots obtained from the experiments and the theoretical curves for different penetration rates. The proposed FD has a unified framework and a more rigorous mathematical structure.

Leveraging Cooperative Intent and Actuator Constraints for Safe Trajectory Planning of Autonomous Vehicles in Uncertain Traffic Scenarios

This study explores the integration of dynamic vehicle trajectories, vehicle safety factors, static traffic environments, and actuator constraints to improve cooperative intent modeling for autonomous vehicles (AVs) navigating uncertain traffic scenarios. Existing models often focus solely on interactions between dynamic trajectories, limiting their ability to fully interpret the intentions of surrounding vehicles. To address this limitation, we present a more comprehensive approach using the Cooperative Intent Multi-Layer Graph Neural Network (CMGNN) model. The CMGNN analyzes not only the dynamic trajectories but also the lane position relationships, vehicle angle changes, and actuator constraints and performs group interaction analysis. This richer information allows the CMGNN to more accurately capture the cooperative intent and better understand the surrounding vehicle behavior. This study investigated the impact of the CMGNN in the Carla simulator on surrounding vehicle trajectory prediction and AV safe trajectory planning. An innovative mechanism for dynamic trajectory risk assessment is introduced, which takes into account the constraints of the actuators when evaluating trajectory planning metrics. The results show that incorporating cooperative intent and considering the actuator limitations enhanced the CMGNN’s safety and driving efficiency in uncertain scenarios, significantly reducing the probability of AVs colliding. This is achieved as the model dynamically adapts its driving strategy based on the real-time traffic conditions, the perceived intentions of the surrounding vehicles, and the physical constraints of the vehicle actuators.

Vehicle Behavior Discovery and Three-Dimensional Object Detection and Tracking Based on Spatio-Temporal Dependency Knowledge and Artificial Fish Swarm Algorithm.

In complex traffic environments, 3D target tracking and detection are often occluded by various stationary and moving objects. When the target is occluded, its apparent characteristics change, resulting in a decrease in the accuracy of tracking and detection. In order to solve this problem, we propose to learn the vehicle behavior from the driving data, predict and calibrate the vehicle trajectory, and finally use the artificial fish swarm algorithm to optimize the tracking results. The experiments show that compared with the CenterTrack method, the proposed method improves the key indicators of MOTA (Multi-Object Tracking Accuracy) in 3D object detection and tracking on the nuScenes dataset, and the frame rate is 26 fps.

Mobility heterogeneity of urban freight areas: Geospatial evidence from shared logistics dynamics

Amid rising mobility services in cities, the logistics sector plays a crucial role in envisioning daily services for local neighborhoods. Previous research has primarily identified key freight areas (KFAs) in small-scale regions using traditional census and travel survey data. However, differing from KFAs, other areas with distinct freight patterns remain largely unexamined, and there is a lack of comprehensive indicators to evaluate the travel behaviors of large-scale freight vehicles. This study introduces a data-driven geospatial framework to characterize and understand the patterns exhibited by different freight areas through freight dynamics data collected in cities. To analyze key aspects of freight mobility within spatial units, a set of areal indicators is developed using GPS trajectories of shared freight vehicles in Hong Kong. Descriptive statistics illustrate the inherent differences across various areas and their spatial distributions. A hierarchical clustering approach groups all areas based on proposed indicators, providing a quantitative evaluation of collective spatial patterns. The analysis also explores the non-linear relationships between different categories of freight activities and the local built environments and socioeconomic conditions. The results suggest a strong spatial heterogeneity in the areal mobility profile of Hong Kong. Loading and unloading behaviors are frequently observed in districts such as Eastern Kowloon, Kwai Chung, and the Airport, highlighting prevalent freight trip origins and destinations. In contrast, areas with high transit times are mostly found in suburban regions, particularly on the periphery. This study provides essential insights for area-based planning and management of urban freight activities, with relevant practical implications.

Enhancing end-to-end control in autonomous driving through kinematic-infused and visual memory imitation learning

This paper presents an exploration, study, and comparison of various alternatives to enhance the capabilities of an end-to-end control system for autonomous driving based on imitation learning by adding visual memory and kinematic input data to the deep learning architectures that govern the vehicle. The experimental comparison relies on fundamental error metrics (MAE, MSE) during the offline assessment, supplemented by several external complementary fine-grain metrics based on the behavior of the ego vehicle at several urban test scenarios in the CARLA reference simulator in the online evaluation. Our study focuses on a lane-following application using different urban scenario layouts and visual bird-eye-view input. The memory addition involves architectural modifications and different sensory input types. The kinematic data integration is managed with a modified input. The experiments encompass both typical driving scenarios and extreme never-seen conditions. Additionally, we conduct an ablation study examining various memory lengths and densities. We prove experimentally that incorporating visual memory capabilities and kinematic input data makes the driving system more robust and able to handle a wider range of challenging situations, including those not encountered during training, in terms of reduction of collisions and speed self-regulation, resulting in a 75% enhancement. All the work we present here, including model architectures, trained model weights, comparison tool, and the dataset, is open-source, facilitating replication and extension of our findings.

Determinants of road user behavior at marked midblock crosswalks

Drivers do not properly respect pedestrian priority at marked mid-block crosswalks. This study assesses pedestrians and vehicles behavior at such locations. Video recording at two mid-block crosswalks was used to analyze twelve crossing attributes and their correlations. Analysis of 884 pedestrians’ and 2087 vehicle’s data showed that all demographic and crossing related attributes affected the crossing speed and crossing time of pedestrians. The average crossing speed of pedestrians was 1.3 m/s. Only parked car at crosswalk affected the waiting and delay times of pedestrians. Lastly, gender, and crossing related parameters were found to affect the accepted gap of pedestrians. The average accepted gap of pedestrians was 6.17seconds. Vehicle speeds before, at, and after crosswalks were statistically different and had mean values of 19.38kmph, 17.31kmph, and 20.40kmph respectively. Driver yielding rate was found to be 40% and was statistically significantly influenced by gender, dressing style, crossing in-group, and rolling behavior.

Autonomous vehicle lane-change maneuver accounting for emotion-induced driving behavior in other vehicles

Autonomous vehicle lane-change maneuver accounting for emotion-induced driving behavior in other vehicles