Vehicle Simulation Research Articles

A test rig for the validation of CFD simulations of a passenger vehicle under hood environment

As part of global efforts to reduce greenhouse gas emissions, the automotive industry is moving towards the electrification of its fleet – including full electric and hybrid vehicles. Considering hybrid vehicles, the energy efficiency and thermal management of powertrains including IC engines remains an important contribution. In this regard, engineers need accurate tools to understand heat-transfer in engine bays. This work presents a flexible test rig design to be used for the validation of CFD simulations of an underhood environment. The test rig and measurement equipment are introduced in detail with experimental data (and CAD) being available.A possible test scenario presented in this research is when an engine is subjected to heavy loads (i.e., constant uphill driving) and the vehicle is subsequently stopped. The experimental results are analysed and, furthermore, the data in terms of flow and temperature fields is compared against the results of the numerical simulations. This sort of comparison is the main usage scenario for the constructed rig, and it demonstrates the value of this facility for research. The rig's geometry and the experimental data being available can additionally be used to facilitate development and validation of various CAE methodology as well as simulation techniques.

The Impact of Vehicle Technology, Size Class, and Driving Style on the GHG and Pollutant Emissions of Passenger Cars

Although technical improvements to engines and aftertreatment systems have the greatest impact on pollutant emissions, there is also potential for reducing emissions through driver behavior. This potential can be realized in the very short term, while better emission-control technologies only take effect once they have penetrated the market. In addition to a change in driving style, the vehicle owner’s choice of vehicle technology and size class will also have an impact on the future emissions of the vehicle fleet. The effects of different driving styles, the tire choice, the vehicle size class, and propulsion technologies on energy consumption and tailpipe and non-exhaust emissions are analyzed in this paper for different traffic situations and start temperatures for cars with petrol and diesel combustion engines and for battery electric vehicles. The analysis is completed with the corresponding upstream emissions from fuel and electricity production. The analysis is based on a vehicle simulation using the Passenger car and Heavy-duty Emission Model (PHEM), which is based on a large database of vehicles created using measurements of real driving conditions. For the assessment of the driving style, a novel method was developed in an H2020 project, which reproduces a measured trip with a virtual eco-driver. Carbon dioxide equivalent emissions (CO2eq) increase with increasing vehicle size, but can be reduced by around 20% for conventional vehicles and 17% for battery electric vehicles (BEVs) through an environmentally conscious driving style. On average, BEVs have around 50% lower CO2eq emissions than conventional vehicles, if the emissions from vehicle production are also taken into account. On an average journey of 35 km, the cold start of modern diesel vehicles accounts for around half of the total NOx emissions, while the proportion of cold starts for petrol vehicles is around 25%. Tire and brake wear together generate a similar amount of PN23 emissions as the exhaust gases from new cars.

A novel suppressing Kalman filter divergence method for the state of charge estimation of lithium-ion batteries under complex conditions

The estimation method for state of charge (SOC) of lithium-ion batteries based on the Extended Kalman Filter (EKF) is prone to losing the ability to track sudden changes under complex driving conditions. Thus, this method cannot guarantee estimation accuracy and robustness. To overcome this deficiency, fading factors are introduced into the error covariance to enhance the effect of newly generated measurement data on the current filtering estimation. A Suboptimal Multiple Fading EKF (SMFEKF) SOC estimation method is proposed herein. The fading factors are solved by forcing the new error sequence of the predicted and actual terminal voltage values to remain orthogonal. The new error sequence that remains orthogonal undergoes transitions from strong autocorrelation in the original stable state to weak autocorrelation. Through real-time adjustment of the Kalman gain matrix, the effective tracking ability of the system in the event of sudden changes gets enhanced. To verify the effectiveness of the proposed method, first, a battery dynamic condition test library covering multiple driving scenarios (such as cities, suburbs, and highways) is established through ADVISOR advanced vehicle simulator driving condition tests. Second, a second-order equivalent circuit model is established and the recursive least squares method is used for parameter identification. Finally, a detailed comparison is made between SMFEKF and EKF in terms of SOC estimation accuracy and robustness under driving conditions with different levels of complexity. It is found that four operating conditions are the main reasons for filtering divergence in EKF. The results show that the root mean square errors of SMFEKF are 63.72 %, 54.20 %, and 21.30 % those of EKF under the three complex conditions, respectively. The new method effectively reduces SOC estimation error and solves the problem of EKF filtering divergence under complex conditions.

Tire–road contact modelling for multibody simulations with regularised road and enhanced UA tire models

AbstractMultibody simulations play an important role in the study of the dynamic behaviour of road vehicles, being one of the key aspects the modelling of the tire-road interaction. The tire-road contact modelling is required to detect the contact between the tire and road and to obtain the tire contact forces from the tire-road relative motion. The first part of the problem is solved by a contact detection algorithm, whereas the second part is handled by a tire model. In both cases, the trade-off between accuracy and computational cost needs to be addressed in the road dynamics scenario. For the contact detection problem, this work proposes a methodology in which the road is described by a mesh of triangles while the tire is described by a toroid. The triangular mesh road input can be directly extracted from any CAD model. To avoid the geometric discontinuities due to the transition between triangular patches, a set of auxiliary points is used to obtain a continuous variation of the road surface normal, based on the method proposed by Rill (Multibody Syst. Dyn. 45:131–153, 2019. In the process of contact detection, all kinematic quantities required by the tire-road contact model are evaluated. The tire–road forces are evaluated using the enhanced UA tire model, which is a numerically stable and efficient development of the original tire model proposed by Gim and Nikravesh (Int. J. Veh. Des. 12, 1991; Int. J. Veh. Des. 12:19–39, 1991; Int. J. Veh. Des. 12:217–228, 1991). Both the contact and tire models have smooth transitions between their internal transitions, thereby reducing the need for the variable-step ODE integrators to unnecessarily decrease time steps. The advances on contact detection and the tire–road contact models, proposed here, are demonstrated with a set of road vehicle simulation scenarios in which the influence of various modelling parameters on the stability and efficiency of the simulations is addressed. The novelties of the work consist not only in the demonstration of the enhanced UA tire model in practical situations but also in the description of the new tire–road contact methodology that employs a simple description of the road geometry, to achieve smooth tire–road contact forces.

Safety in context: routines and the effect of a balanced safety and operations focus on worker perceptions and performance

PurposeTo investigate the influence of logistics and transportation workers’ perceptions of their management’s simultaneous safety and operations focus (or lack thereof) on related worker safety and operational perceptions and behaviors.Design/methodology/approachThis multi-method research consisted of two studies. Study 1 aimed to establish correlational relationships by evaluating the impact of individual-level worker perceptions of operationally focused routines (as a moderator) on the relationship between worker perceptions of safety-related routines and workers’ self-reported safety and in-role operational behaviors using a survey. Study 2 aimed to establish causal relationships by evaluating the same conceptual relationships in a behavioral-type experiment utilizing vehicle simulators. After receiving one of four pre-task briefings, participants completed a driving task scenario in a driving simulator.FindingsIn Study 1, the relationship between perceived safety focus and safety behavior/in-role operational behavior was strengthened at higher levels of perceived operations focus. In Study 2, participants who received the balanced pre-task briefing committed significantly fewer safety violations than the other 3 treatment groups. However, in-role driving deviations were not impacted as hypothesized.Originality/valueThis research is conducted at the individual (worker) level of analysis to capture the little-known perspectives of logistics and transportation workers and explore the influence of balanced safety and operational routines from a more micro perspective, thus contributing to a deeper understanding of how balanced routines might influence worker behavior when conducting dynamic tasks to ensure safe, effective outcomes.

Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

Physical Modeling and Simulation of Reusable Rockets for GNC Verification and Validation

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their cross-couplings must be captured to achieve sufficiently representative simulations, hence a better assessment of the GNC performance and robustness. This paper focuses on of the main aspects related to the physical (acausal) modeling of reusable rockets, and the integration of these models into a suitable simulation framework oriented towards GNC Validation and Verification (V&V). Firstly, the modeling challenges and the need for physical multibody models are explained. Then, the Vertical Landing Vehicles Library (VLVLib), a Modelica-based library for the physical modeling and simulation of reusable rocket dynamics, is introduced. The VLVLib is built on specific principles that enable quick adaptations to vehicle changes and the introduction of new features during the design process, thereby enhancing project efficiency and reducing costs. Throughout the paper, we explain how these features allow for the rapid development of complex vehicle simulation models by adjusting the selected dynamic effects or changing their fidelity levels. Since the GNC algorithms are normally tested in Simulink®, we show how simulation models with a desired fidelity level can be developed, embedded and simulated within the Simulink® environment. Secondly, this work details the modeling aspects of four relevant vehicle dynamics: propellant sloshing, Thrust Vector Control (TVC), landing legs deployment and touchdown. The CALLISTO reusable rocket is taken as study case: representative simulation results are shown and analyzed to highlight the impact of the higher-fidelity models in comparison with a rigid-body model assumption.

High-fidelity aero-acoustic evaluations of a heavy-lift eVTOL in hover

Electric Vertical Take-off Landing (eVTOL) vehicles can transform aviation, but the unconventional aircraft development currently requires an extensive knowledge base. This paper presents a high-fidelity aerodynamic and acoustic evaluation of a heavy-lift multi-rotor eVTOL design in hover. The design, known as Skybus, has an MTOW of 14,000 kg, and six tiltable rotors attached at the tip of three sets of tandem wings with flaps. High-fidelity scale-adaptive CFD simulations of the full-scale Skybus vehicle in hover, with three design variations, were conducted using the HMB3 framework. The aerodynamic performance and interactions were analysed in detail. The baseline airframe caused a blockage force of about 13% of the MTOW, and the compact hovering rotors induced a sinusoidal single-blade loading variation, blended with small local interactions. The near-field acoustics were then directly extracted from the high-fidelity CFD results, and far-field acoustic propagation was computed using the acoustic analogy, following reference acoustic measurements for eVTOLs proposed by EASA. Different aerodynamic interactions and propeller acoustic characteristics showed strong impacts on the vehicle acoustics. The EPNL metric was also computed and analysed assuming various hover duration, and restrictions on maximum EPNL and hover duration are recommended for future rotorcraft hover noise certification. These novel results provide valuable guidance for eVTOL acoustic design, infrastructure planning, and policy-making.

Adaptive MATLAB GUI for Hydraulic Brake Stopping Distance Simulation

The crucial segment of any vehicle is the braking unit which decides the vehicle’s performance and passenger safety on a large quotient. It is highly essential to decelerate the vehicle within a reduced distance to avoid any collision. The stopping distance is also dependent on many characteristics apart from the vehicle itself which are GVW(Gross Vehicle Weight), brake sizing, Road Friction, and other vehicle design parameters. Hereby, the result of any brake system is the Stopping Distance. Therefore, for every vehicle, it is inevitable to design in such a way that meets all requirements to pass the safety standards. In this article, we provide an interactive GUI of Mathematical modelling using MATLAB that depicts the running vehicle simulation while the brakes are applied and highlights the Stopping distance for any given inputs. Here, we have used a novel approach to calculate the stopping distance, given we have all vehicle properties. Importantly, cuts the design phase to a large extent saving an ample amount of time with all ease of access and dexterity. The core features of GUI: One will be able to generate and compare stopping distance and other brake-related results for any two given different design scenarios. GUI is automated so that it can simulate the stopping distance just by providing the brake/vehicle parameters inputs to the interface. Additional functionalities to compare design results for various standards (FMVSS, IS, ECE) can also be done effectively. Keywords: Braking Systems, MATLAB Application, Vehicle Safety, Stopping Distance

Braking system analysis with the aid of a dynamic vehicle simulator with nine degrees of freedom

ABSTRACT The braking system is one of the most important vehicle systems, therefore, its optimised operation is essential in the dynamic performance of the automotive project; in addition to ensuring the necessary security of the object of study. Consecutively, the in-depth study on the sizing and performance of the braking system becomes necessary and, using software to assist in the manipulation of the parameters that influence this system, the automotive industry has presented different solutions in recent years, especially due to the use of central electronics such as the brake system module (BSM). In this context, the present work simulates, via a multibody virtual model and the aid of software such as VI-CarRealTime, the dynamic behaviour of a vehicle according to the braking system efficiency loss. With this goal, the virtual model is correlated with a physical test car and the conditions proposed in the AMuS test are executed, where the friction between the pad and the brake disc is changed. Using driver-in-the-loop simulation in a vehicle dynamics simulator with nine degrees of freedom, it was noted, qualitatively, that the loss of brake efficiency through g-force in each braking results in a long time for the vehicle to react according to the driver’s assessment and by telemetry.

Constructing Pavement Roughness of a Three-Dimensional Bridge in Abaqus for Vehicle Scanning Method

In this study, a procedure for constructing pavement roughness in a three-dimensional bridge subjected to a vehicle using Abaqus is proposed for numerical simulation of vehicle–bridge interaction problems. As the plug-in RufGen, a graphical user interface in Abaqus/CAE for surface interaction, creates a hexahedron with a rough surface and attaches it to the bridge deck, the resulting weight of a bridge model can be much larger than the original one. In the vehicle scanning method, it is essential to keep a finite element bridge model with an unchanged weight after imposing a rough surface on it in consideration of the roughness effect for bridge health monitoring. As such, the proposed procedure is aimed at resolving this issue, with the following two highlights: First, the Toeplitz matrix is introduced to generate a rough surface in three dimensions to ensure the randomness of roughness in the two perpendicular directions under a given roughness profile. Second, a shell-type rough pavement is established for directly using as the bridge pavement or attaching to the bridge deck, which minimizes weight increment in the bridge.

Design of Active Suspension Controller for Ride Comfort Enhancement and Motion Sickness Mitigation

This paper presents a method for designing an active suspension controller for ride comfort enhancement and motion sickness mitigation. For this, it is necessary to design an active suspension controller, which aims to reduce the vertical acceleration and pitch rate of a sprung mass in a vehicle. A half-car vehicle model was selected. For the controller design, a static output feedback (SOF) control was selected instead of a full-state feedback control because it is hard to measure all state variables in real vehicles. With the available signals, three types of SOF controller were proposed. To determine the gains of the SOF controllers, a linear quadratic optimal control methodology and a simulation-based optimization method were adopted. To validate the proposed method, a simulation was carried out using vehicle simulation software. The simulation results show that the proposed method is quite effective for ride comfort enhancement and motion sickness mitigation.

On investigating drivers’ attention allocation during partially-automated driving

The use of partially-automated systems require drivers to supervise the system functioning and resume manual control whenever necessary. Yet literature on vehicle automation show that drivers may spend more time looking away from the road when the partially-automated system is operational. In this study we answer the question of whether this pattern is a manifestation of inattentional blindness or, more dangerously, it is also accompanied by a greater attentional processing of the driving scene. Participants drove a simulated vehicle in manual or partially-automated mode. Fixations were recorded by means of a head-mounted eye-tracker. A surprise two-alternative forced-choice recognition task was administered at the end of the data collection whereby participants were quizzed on the presence of roadside billboards that they encountered during the two drives. Data showed that participants were more likely to fixate and recognize billboards when the automated system was operational. Furthermore, whereas fixations toward billboards decreased toward the end of the automated drive, the performance in the recognition task did not suffer. Based on these findings, we hypothesize that the use of the partially-automated driving system may result in an increase in attention allocation toward peripheral objects in the road scene which is detrimental to the drivers’ ability to supervise the automated system and resume manual control of the vehicle.

The Influence of CRTS II Slab Ballastless Track Upper Arch Deformation on the Wheel Jumping Law of High-Speed Vehicle

To study the impact of upwarp deformation in the ballastless track on the jumping behavior of the high-speed vehicle, utilizing UM and ANSYS joint simulation, a vertical vibration model of high-speed vehicle on CRTS II slab ballastless track was developed based on the non-Hertz wheel–rail contact model of virtual penetration theory. By using the single-wave cosine curve simulating the characteristics of upwarp deformation in the track slab, we calculated the whole process of wheel jumping. This allowed us to analyze how the amplitude and wavelength of the track slab upward deformation influence the vibration response of the vehicle–track system. Our findings indicate that when a wheel passes through the arch section of the track slab, the entire wheel jumping process consists of distinct stages: “wheel–rail bonding, wheel–rail separation, wheel–rail impact (one or more times), and wheel–rail bonding.” As the amplitude of upwarp deformation increases and the wavelength decreases, significant changes occur in several parameters, including the vertical force between the wheel and rail, wheel unloading rate, wheel jump height, frequency, duration, and vertical displacement of the rail. Additionally, when the wavelength is between 2 and 6[Formula: see text]m and the amplitude is 8[Formula: see text]mm, the vertical force between the wheel and rail becomes zero, the wheel load reduction rate is one, and the wheel jumps. When the wavelength is less than 3[Formula: see text]m, the wheel jump height exceeds the flange height, increasing the risk of derailment. Meanwhile, during the first wheel–rail impact, the wheel–rail vertical force and the rail vertical displacement reach their maximum, potentially impacting rail service performance negatively. Finally, compared to the amplitude of the track slab camber deformation, its wavelength has a greater impact on the entire process of wheel jumping. It is recommended that attention be paid to the change in the wavelength of the track slab camber during the maintenance and repair of the ballastless track.

A new contact and road model for multi-body dynamic simulation of wheeled vehicles on soft-soil terrain

AbstractIn this paper, a new high-performance and memory-efficient contact and road model is developed. Specifically, the road is modeled as a rectangular structured grid of deformable springs in the vertical direction, thus enabling fast execution. The new road model stands out due to its ability to handle large road scenarios by allocating computer memory dynamically for each spring, resulting in efficient memory utilization. Furthermore, each spring represents a small road patch that entails various information, such as the soil elevation, the soil properties, and the soil compaction, allowing for complicated simulations incorporating spatially varying soil properties and phenomena related to the multi-pass effect. In addition, using the new contact model, complex terrain geometries are handled in a computationally efficient way by approximating locally the irregular road profile with a suitable equivalent plane. For this, two different strategies are proposed, namely the radial basis function (RBF) interpolation method and the 3D enveloping contact model. Finally, the proposed techniques are implemented in Altair MotionSolve, a comprehensive multi-body simulation software for complex mechanical systems. In particular, a single-wheel test bed is initially examined followed by a four-wheeled rover model and the next-generation NATO reference mobility model (NG-NRMM). In all cases, the proposed model is validated by using available experimental data. Lastly, a case involving both wheeled and tracked vehicles is also examined by using a shared road model.

Autonomous Driving System Architecture with Integrated ROS2 and Adaptive AUTOSAR

In the automotive industry, research is now underway to apply Adaptive Automotive Open System Architecture (AUTOSAR) to the development of next-generation mobility, such as autonomous driving and connected cars. However, research on autonomous driving is being predominantly conducted on the robotics platform ROS2 (Robot Operating System 2). This demonstrates a considerable distance between autonomous driving research and its application in actual vehicles. To bridge this gap, interoperability that leverages the strengths of the Adaptive AUTOSAR and ROS2 platforms and compensates for their weaknesses is required. Therefore, this study proposes an architecture for interoperability between the two platforms, named Autonomous Driving System with Integrated ROS2 and Adaptive AUTOSAR (ASIRA). The proposed architecture enables communication between each of the two platforms through the ROS2 SOME/IP Bridge and allows for the necessary data exchange. It validates them in autonomous driving scenarios and goes beyond vehicle development, testing, and prototyping to exploit the advantages of each platform. Additionally, the simulation of autonomous vehicles within the ASIRA architecture is demonstrated by interoperating the ROS2 representative open-source autonomous driving project, Autoware, with the Adaptive AUTOSAR simulator. This study contributes to the assimilation of ROS2 into the automotive industry and its application in real vehicles by linking ROS2 and Adaptive AUTOSAR.

Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm

Unsignalized roundabouts have a significant impact on traffic flow and vehicle safety. To address the challenge of autonomous vehicles passing through roundabouts with low penetration, improve their efficiency, and ensure safety and stability, we propose the proximal policy optimization (PPO) algorithm to enhance decision-making behavior. We develop an optimization-based behavioral choice model for autonomous vehicles that incorporates gap acceptance theory and deep reinforcement learning using the PPO algorithm. Additionally, we employ the CoordConv network to establish an aerial view for spatial perception information gathering. Furthermore, a dynamic multi-objective reward mechanism is introduced to maximize the PPO algorithm’s reward pool function while quantifying environmental factors. Through simulation experiments, we demonstrate that our optimized PPO algorithm significantly improves training efficiency by enhancing the reward value function by 2.85%, 7.17%, and 19.58% in scenarios with 20, 100, and 200 social vehicles, respectively, compared to the PPO+CCMR algorithm. The effectiveness of simulation training also increases by 11.1%, 13.8%, and 7.4%. Moreover, there is a reduction in crossing time by 2.37%, 2.62%, and 13.96%. Our optimized PPO algorithm enhances path selection during autonomous vehicle simulation training as they tend to drive in the inner ring over time; however, the influence of social vehicles on path selection diminishes as their quantity increases. The safety of autonomous vehicles remains largely unaffected by our optimized PPO algorithm.

Design of a Modularization-Based Automation Performance Simulation Framework for Multi-Vehicle Interaction System

With the electrification and connectivity of vehicles in transportation, traditional vehicles with single drivetrains are being replaced by pure electric or hybrid electric vehicles (HEVs). This evolution has given rise to diverse electromechanical coupling drivetrains. There is a pressing need to update simulation software to assess the economic performance of vehicles in various environments, and promote sustainable development and energy conservation. This paper presents a unified framework for the construction and automated operation of large-scale automated vehicle simulations with multiple drivetrain types, facilitating synchronous information exchange among vehicles. Central to the framework is the development of a plug-and-play vehicle model based on a modular component design, facilitating the rapid assembly of vehicles with varied drivetrain configurations and standardizing simulation file management. Additionally, a standardized simulation process construction is established to accommodate the automated operation of simulations. Furthermore, a data scheduling method among vehicles is introduced to achieve multi-vehicle interconnection simulation. Finally, the effectiveness of the framework is demonstrated through a case study involving queue-following control for HEVs. This framework aims to provide a comprehensive solution for quickly establishing automated simulation environments for multi-vehicle interaction, enhancing model reusability and scalability.

Evolution of Microstructure and Mechanical Properties of DP590 Structural Steel of Vehicle in Heat-Treatment Post-Fire Simulation

Evolution of Microstructure and Mechanical Properties of DP590 Structural Steel of Vehicle in Heat-Treatment Post-Fire Simulation

How Do Humanlike Behaviors of Connected Autonomous Vehicles Affect Traffic Conditions in Mixed Traffic?

Different methodologies are being used to study the effects of autonomous vehicles (AVs) in mixed traffic to exhibit the interactions between autonomous and human-driven vehicles (HVs). Microscopic simulation tools are popular in such an assessment, as they offer the possibility to experiment in economical, robust, and optimistic ways. A lack of reliable real-world data (also known as natural data) to calibrate and evaluate the connected autonomous vehicle (CAV) simulation model is a major challenge. To deal with this situation, one interesting methodology could be to deal with the CAVs as conventional human-driven vehicles and predict their possible characteristics based on the simulation inputs. The conventional human-driven vehicles from the real world, in this methodology, come to act as a benchmark to offer the measure of effectiveness (MoE) for the calibration and validation. For the three most common driving behaviors, a sensitivity analysis of the behaviors of AVs and an effective assessment of CAVs in a mixed traffic environment were performed to explore the humanlike behaviors of the autonomous technology. The findings show that, up to a certain point, which is directly related to the quantity of interacting vehicles, the impact of CAVs is typically favorable. This study validates the approach and supports past studies by showing that CAVs perform better than AVs in terms of their traffic performance and safety aspects. On top of that, the sensitivity analysis shows that enhancements in the technology are required to obtain the maximum advantages.