Vehicle Distance Research Articles

Modelling Driver Behaviour at Urban Signalised Intersections Using Logistic Regression and Machine Learning

This study investigated several factors that may influence driver actions throughout the yellow interval at urban signalised intersections. The selected samples include 2,168 observations. Almost 33% of drivers stopped ahead of the stop line, 60% passed the intersection through the yellow interval, and 7% passed after the yellow interval was complete (red light running, RLR violations). Binary logistic regression models showed that the chance of passing went up as vehicle speed went up and down as the gap between the vehicle and the traffic light and green interval went up. The movement type and vehicle position influenced the passing probability, but the vehicle type did not. Moreover, multinomial logistic regression models showed that the legal passing probability declined with the growth in the green time and vehicle distance to the traffic signal. It also increased with the growth in the speed of approaching vehicles. Also, movement type directly affected the chance of legally passing, but vehicle position and type did not. Furthermore, the driver’s performance during the yellow phase was studied using the k-nearest neighbours algorithm (KNN), support vector machines (SVM), random forest (RF) and AdaBoost machine learning techniques. The driver’s action run prediction was the most accurate, and the run-on-red camera was the least accurate.

Developmental differences in children’s adaptation to vehicle distance and speed in street-crossing decision-making

Introduction: Young children cannot effectively adapt their behaviors to vehicles at varied distances and speeds, which is a critical cause of road accidents. However, the impact of this crucial ability on children’s street-crossing decision-making and the age at which they acquire it remain unclear. Method: This study examined the crossing decision-making behavior of children at 6, 8, and 11 years of age in facing 51 different videotaped traffic scenarios with varying vehicle distances and speeds. Sixty Chinese elementary school students, with 20 children evenly distributed into each of the three age groups (6 years, 8 years, and 11 years old), participated in a simulated street-crossing task using video projections. Hierarchical logistic regression models were used to analyze how age moderated the effects of vehicular motion factors (vehicle–pedestrian distance, vehicle speed) on children’s crossing safety, including dangerous crossing and crossing decision-making. Results: The results showed that when either vehicle–pedestrian distance decreased or vehicle speed increased all age groups tended to cross less frequently but probability of dangerous crossing increased. Compared to 8-year-old and 11-year-old children, 6-year-old children showed a less pronounced tendency toward both of these crossing decision-making behaviors, and had more dangerous crossing outcomes. Conclusions: These findings suggest that inadequate adaptation to vehicle–pedestrian distance and vehicle speed may partly contribute to the inferior safety of street-crossing behavior in 6-year-olds compared to 8-year-olds. No significant differences were observed between 8- and 11-year-old children, suggesting the turning point for this ability might occur between 6 and 8 years of age. Practical Applications: Preventive measures aimed to reduce crossing risks for children should consider children’s developmental stages.

Risk-informed longitudinal control in autonomous vehicles: A safety potential field modeling approach

The advent of autonomous vehicles (AVs) has complicated traffic flow and raised safety concerns, there is a pressing need to enhance traffic efficiency alongside ensuring robust safety. This paper proposes a dynamic longitudinal control strategy for AVs, based on real-time accident risk assessments. Data from traffic flow, road characteristics, and weather conditions are analyzed using a random forest algorithm, producing a real-time accident risk identification model through a support vector machine. In addition, the Perceived Risk Field Model (PRFM), a car-following model calibrated using the NGSIM dataset, considers vehicle distance, velocity, relative velocity, and acceleration. These models underpin our proposed longitudinal control strategies, tailored to variable accident risk levels. Under the normal risk control strategy, the PRFM outperforms the Intelligent Driver Model (IDM) and the Cooperative Adaptive Cruise Control Model (CACC) in stability and driving comfort. For high accident risk scenarios, our approach shows improved collision avoidance and traffic oscillation mitigation. Simulations indicate that the high accident risk control strategy offers better safety and stability than the normal risk strategy. Furthermore, traffic flow diagrams suggest an increase in road capacity as AV penetration rates rise, pointing to the promising efficiency of our proposed solution. This research provides a robust framework for AV operation, ensuring optimal traffic safety and efficiency.

Robust Frontal Vehicle Pose Estimation Based on Structural Parameter Optimization Using Reliable Edge Point Sequences

In order to enhance the stability of vehicle pose estimation within driving videos, a novel methodology for optimizing vehicle structural parameters is introduced. This approach hinges on evaluating the reliability of edge point sequences. Firstly, a multi−task and iterative convolutional neural network (MI−CNN) is constructed, enabling the simultaneous execution of four critical tasks: vehicle detection, yaw angle prediction, edge point location, and visibility assessment. Secondly, an imperative aspect of the methodology involves establishing a local tracking search area. This region is determined by modeling the limitations of vehicle displacement between successive frames. Vehicles are matched using a maximization approach that leverages point similarity. Finally, a reliable edge point sequence plays a pivotal role in resolving structural parameters robustly. The Gaussian mixture distribution of vehicle distance change ratios, derived from two measurement models, is employed to ascertain the reliability of the edge point sequence. The experimental results showed that the mean Average Precision (mAP) achieved by the MI−CNN network stands at 89.9%. A noteworthy observation is that the proportion of estimated parameters whose errors fall below the threshold of 0.8 m consistently surpasses the 85% mark. When the error threshold is set at less than 0.12 m, the proportion of estimated parameters meeting this criterion consistently exceeds 90%. Therefore, the proposed method has better application status and estimation precision.

Vehicular Platoon Control With Gain Variations via Unreliable VANETs

This paper investigates the adaptive control for vehicular platoon with controller gain variations and packet dropouts. With leader predecessor following topology, vehicle speed tracking and distance keeping are achieved by tracking-error based control. The gain variations are compensated for by introducing an adaptive estimation law. A switching adaptive control law is presented to deal with packet dropouts, in which only distance measurements are used when packet dropouts occur. Combining the switched system approach and adaptive control, a set of stability conditions are obtained to design the adaptive controller for each follower. The results present a unified framework for determining the controller gains and the estimator adaptation law, yielding an adaptive control algorithm that guarantees both internal stability and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{2}$</tex-math></inline-formula> string stability. Numerical examples demonstrate the effectiveness of our method.

Pedestrians’ road-crossing decisions: Comparing different drift-diffusion models

The decision of whether to cross a road or wait for a car to pass, humans make frequently and effortlessly. Recently, the application of drift-diffusion models (DDMs) on pedestrians’ decision-making has proven useful in modelling crossing behaviour in pedestrian–vehicle interactions. These models consider binary decision-making as an incremental accumulation of noisy evidence over time until one of two choice thresholds (to cross or not) is reached. One open question is whether the assumption of a kinematics-dependent drift-diffusion process, which was made in previous pedestrian crossing DDMs, is justified, with DDM-parameters varying over time according to the developing traffic situation. It is currently unknown whether kinematics-dependent DDMs provide a better model fit than conventional DDMs, which are fitted per condition. Furthermore, previous DDMs have not considered reaction times for the not-crossing option. We address these issues by a novel experimental design combined with modelling. Experimentally, we use a 2-alternative-forced-choice paradigm, where participants view videos of approaching cars from a pedestrian’s perspective and respond whether they want to cross before the car or to wait until the car has passed. Using these data, we perform thorough model comparison between kinematics-dependent and condition-wise fitted DDMs. Our results demonstrate that condition-wise fitted DDMs can show better model fits than kinematics-dependent DDMs as reflected in the mean-squared-errors. The condition-wise fitted models need considerably more parameters, but in some cases still outperform kinematics-dependent DDMs in measures that penalize the parameter number (e.g., Akaike information criterion). Introducing a starting point bias provides support for the novel hypothesis of rapid early evidence build-up from the initial view of the vehicle distance. The drift rates obtained for the condition-wise fitted models align with the assumptions in the kinematics-dependent models, confirming that pedestrians’ decision processes are kinematics-dependent. However, the partial preference for condition-wise fitted models in the model selection suggests that the correct form of kinematics-dependence has not yet been identified for all DDM-parameters, indicating room for improvement of current pedestrian crossing DDMs. Developing more accurate models of human cognitive processes will likely facilitate autonomous vehicles to understand pedestrians’ intentions as well as to show unambiguous human-like behaviour in future traffic interactions with humans.

NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services

The Third Generation Partnership Project (3GPP) has specified Cellular Vehicle-to-Everything (C-V2X) radio access technology in Releases 15–17, with an emphasis on facilitating direct communication between vehicles through the interface, sidelink PC5. This interface provides end-to-end network slicing functionality together with a stable cloud-native core network. The performance of direct vehicle-to-vehicle (V2V) communications has been improved by using the sidelink interface, which allows for a network infrastructure bypass. Sidelink transmissions make use of orthogonal resources that are either centrally allocated (Mode 1, Release 14) or chosen by the vehicles themselves (Mode 2, Release 14). With growing interest in connected and autonomous vehicles, the advancement in radio access technologies that facilitate dependable and low-latency vehicular communications is becoming more significant. This is especially necessary when there are heavy traffic conditions and patterns. We thoroughly examined the New Radio (NR) sidelink’s performance based on 3GPP Releases 15–17 under various vehicle densities, speeds, and distance settings. Thus, by evaluating sidelink’s strengths and drawbacks, we are able to optimize resource allocation to obtain maximum coverage in urban areas. The performance evaluation was conducted on Network Simulator 3 (NS3.34/5G-LENA) utilizing various network metrics such as average packet reception rate, throughput, and latency.

Dynamic and real-time continuous look-ahead distance for autonomous vehicles: an explicit formulation

The advent of autonomous vehicles has brought about significant advancements in transportation technology, promising safer and more efficient means of travel. However, their full integration into society depends on the accuracy of path-tracking realised by a lateral controller. Lateral control is achieved by regulating the steering angle to minimise the lateral error between the vehicle and a target point at a look-ahead distance on the reference path. This paper investigates the look-ahead distance as it is considered a key parameter that impacts vehicle performance, stability, and energy consumption. A qualitative analysis is performed to deduct a set of rules to adapt the look-ahead distance to three parameters: vehicle velocity, road curvature, and road adherence. Then, an original explicit mathematical formulation is developed for the look-ahead distance as a function of the considered parameters. A fuzzy logic decision for the look-ahead distance is further established and compared with the formulated one. Both approaches are implemented on a look-ahead distance-based lateral controller based on the super-twisting sliding mode control. Simulation results carried out in a joint simulation between Simulink/MatLab and SCANeR TM Studio vehicle dynamics simulator demonstrate the effectiveness of the developed model on vehicle performance, stability, computational efficiency, and energy consumption.

An Optimization Design of Hybrid Parking Lots in an Automated Environment

This paper explores the minimum lateral parking distance and parking acceleration/deceleration distance of vehicles to improve the efficiency of automated valet parking (AVP) lots and save urban land. Specifically, the paper focuses on designing parking lots for automated guided vehicles (AGVs) and their parking attributes. To ensure AGV accessibility and maximize AVP capacity, graph theories and unique path-driving methods are used in designing mobile priority parking lots and decision spaces. Additionally, the paper proposes an optimization design for parking lots with obstacles, considering the layout of load-bearing columns and charging resources for electric vehicles in underground parking lots. The article further proposes an optimization design for hybrid parking lots based on spatio-temporal resource conversion in traffic design and the principle of traffic separation in traffic control since hybrid parking lots that accommodate both conventional vehicles and AGVs are crucial to the future development of urban parking lots. The experimental results show that the proposed optimization design for urban parking lots in automated environments is superior to the traditional parking lots design in terms of capacity and density. This paper provides an optimal layout scheme of urban parking lots in multiple scenarios, which can improve the service level of urban static traffic systems.

Investigation of performance and energy cost of automobile air-conditioner using alternative refrigerants based on thermal comfort using various cooling seats

During hot summer days, the rapid rise in temperature inside a vehicle and the direct transmission of radiant energy cause significant discomfort to drivers, and the driving distance of an electric vehicle is greatly reduced due to the operation of the air-conditioner. Therefore, it is essential to provide a thermally comfortable environment to the driver and reduce the energy consumption of air-conditioners in the EV. This study investigated the thermal comfort of the driver according to the use of a basic seat (BSS), ventilation seat (VLS), and cold-water seat (CWS) in the car cabin under summer condition. Also, based on thermal comfort, the system performance and energy cost of an automobile air-conditioner using various alternative refrigerants were analyzed and compared. As a result, when the CWS was used, the stress index of subjects was the lowest in the PPG, and the relative β/α of the subjects decreased in the EEG. In addition, the results of the subjective questionnaires and bio-signal measurement showed the most thermally comfortable when using the CWS. The COPs of the air-conditioner using R134a, R1234yf, R1234ze(E), and R152a increased by 19.3 %, 18.9 %, 19.0 %, and 19.3 %, respectively, and annual energy consumption of the air-conditioner decreased. In addition, in the five European countries, the annual electric charging costs and annual eq-CO2 emissions for using the CWS showed the most significant reduction in Germany. Therefore, it was confirmed that using CWS can provide a thermally comfortable environment for drivers inside the vehicle during summer and effectively reduce power consumption used for air conditioning in EVs.

Design of auto obstacle avoidance system based on machine learning under the background of intelligent transportation

AbstractWith the process of urbanization and the increase in car ownership, traffic problems are becoming increasingly prominent. In order to improve traffic mobility and improve traffic safety, a machine learning based autonomous obstacle avoidance system was studied and designed in the context of intelligent transportation. Design an obstacle avoidance hardware system consisting of a tracking sensor module, an intelligent patrol module, an obstacle avoidance sensor module, and a motor module. Through the coordination and cooperation of multiple modules, the adaptive ability of the obstacle avoidance system is improved. On the basis of hardware design, a road coordinate system is established, and the lane‐changing path is planned with the longitudinal, lateral distance and speed of the ego vehicle and the preceding vehicle as input, and the vehicle steering and lane‐changing control is completed using the front wheel angle of the ego vehicle as the control quantity. The model predictive control method is used for obstacle avoidance trajectory planning. Based on the obstacle avoidance path planning results, the reinforcement learning method is used to design the vehicle's autonomous obstacle avoidance early warning to improve the efficiency of obstacle avoidance. The experimental results show that the designed system can maintain the lateral stability of the vehicle under continuous steering conditions, and the fit between the path tracking and the reference path is better, that is, the vehicle obstacle avoidance control effect is better; the convergence speed is faster. The vehicle autonomous obstacle avoidance warning time is short, which can ensure the safety of the vehicle to the greatest extent. This research achievement will provide important support for the development and practical application of intelligent transportation systems, and promote innovation and progress in the transportation field.

Regenerative Braking of BLDC Motor applied to Electric Vehicles

Vehicle uses mechanical brakes to increase friction of wheels for deceleration purposes. However, kinetic energy is converted into thermal energy which reduces the wastage of energy. To avoid this condition regenerative braking scheme has been employed in electric vehicles. This paper proposes a simple method of braking with energy regeneration using BLDC motor for electric vehicles (EVs). Regenerative braking is a procedure in which energy is recovered while brakes are applied. Hence slowing the car, kinetic energy of the car is converted into electrical energy which in turn is used to top up the battery. Due to these, electric vehicles can run for a longer time and extended range without the need of being plugged into an external charger. Usage of brushless DC motor (BLDC) is recommended for implementing the regenerative braking scheme where it can improve efficiency and can extend the driving distance of electric vehicles.

Geographic Routing Protocol Using Vanet Routing

Previously, vehicular ad hoc networks (VANETs) were designed by using several geographic routing schemes. That doesn’t sustaining any routing configurations in the existing geographic routing methods. Vehicular Ad hoc Network is networks which consist of mobile nodes, consequently it is very harsh to make and remain up end-to-end links. The main aim of this research work is widespread inter vehicle distance, high speed of vehicles also support the density of vehicle is altered. The exact real time location information of vehicles is accessed with high reliability via vehicle to vehicle inter connection. Due to frequent link entrapped and rapid topology changes it is not easy to augment well-organized routing etiquettes for VANETs. To find the exact location of stirring vehicles is very difficult and may incur in the existing routing networks. Additionally the position information about stirring vehicle accessible to other vehicles is usually time-lagged as it is always stirring over time. Luckily, we scrutinize that the interim upcoming locations of vehicles can be forecast. Anchored in the significant surveillance, we propose a novel approach for geographic routing. It uses the extrapolative locations of automobiles. The etiquettes anchored in the vehicles' positions also called as geographic routing protocols (GR) were uncovered to be the most adequate to the VANETs because of their sturdiness in making with the dynamic environment changes and the high-ceilinged mobility of the automobiles. The etiquette is incorporated with a multi-hop information delivery virtualization deal that works on the crest of the transportation stratum and gives soaring speed for multi-hop one after the other information broadcasts. We demeanor practical computer imitations to display the recital advantage of the etiquette over additional approaches at the indistinguishable time vigor efficiency will be enhanced.

Sistem Pemantauan Slot Parkir Menggunakan Sensor Ultrasonik JSN-SR04T dan Pengenalan Plat Nomor Kendaraan dengan ESP32-CAM

A parking slot monitoring system prototype has been produced using the JSN-SR04T ultrasonic sensor and vehicle license plate recognition with ESP32-CAM. The system was created to make it easier for motorists to monitor the availability of parking slots remotely by utilizing Telegram which is connected to the ESP32-CAM module and Arduino Mega 2560. The ultrasonic sensor detects the distance of vehicles entering or exiting the parking lot, then a servo motor opens the entry and exit bars. Vehicle users can monitor the availability of parking slots on the Telegram bot. In each slot there is an ultrasonic sensor that detects the distance so that it can find out whether there is a vehicle in the slot. The test results show that the ultrasonic sensor has an average error percentage of 0.65%. The parking barrier can be opened when the ultrasonic sensor detects the presence of a vehicle at the entrance or exit and the ultrasonic sensor can detect the presence of a vehicle according to the conditions in the slot. The system is equipped with vehicle license plate character recognition using the Optical Character Recognition (OCR) method. Through the ESP32-CAM module camera webserver, the OCR program can detect vehicle plates with the furthest distance of 140 cm using video resolution (400x296) and 300 cm using video resolution (1600x1200). The OCR program can read the vehicle plate characters correctly and the results for the number of characters obtained correspond to the license plate being tested, then the vehicle plate data is stored in the database

The impact of passenger electric vehicles on carbon reduction and environmental impact under the 2050 net zero policy in Taiwan

In response to the 2050 net-zero emission goal, Taiwan initiated the “Pathway to Net-Zero Emissions” in 2022, including a focus on electric vehicles (EVs). However, the emission reduction effect of electric vehicles has not been fully studied in Taiwan. Therefore, this study aims to conduct life cycle assessment (LCA) on environmental performance by replacing petrol vehicles with electric vehicles on the basis of the well-to-wheel method. This study also establishes a feedback mechanism through system dynamics to correlate the DALY results of LCA with population and energy demand. Results of the impact of DALY on lifespan and population is relatively small, indicating that the reduction in environmental impact from electric vehicles helps mitigate the decline in population. The emission-reducing potential and environmental impact of electric vehicles by 2050 are also estimated. Results demonstrate that within a 95% confidence interval and considering uncertainties arising from total driving distance, fuel economy, and energy intensity of electric vehicles, the greenhouse gas emissions of electric vehicles in 2050 will be between 7.826 × 105 and 1.069 × 106 tCO2e, respectively. In addition, the emission-reducing potential of electric vehicles is in the range of 1.076 × 106 to 1.804 × 106 tCO2e.

The Effect of Smartphone and Vehicle Distance on Vehicle Safety System Time Response Based on the Internet of Things

&lt;p class="IndexTerms"&gt;This paper addresses the prevalent issue of increasing crimes in Indonesia, particularly vehicle thefts. To combat this challenge, we propose the development of an Internet of Things (IoT)-based vehicle security device capable of detecting intruders within the car and subsequently notifying the car owner through smartphone alerts. The implementation of this innovative technology holds the potential to enhance vehicle security and contribute to a safer environment for vehicle owners in the country. In this study, we conducted a series of tests in a rural mountain area to examine the impact of vehicle and smartphone distance variation, as well as internet provider variation, on the response time of a vehicle safety device under limited internet signal conditions. The experiment involved distance variations of 100m, 200m, 300m, 400m, 500m, and 600m, with the internet providers being the most expensive and the cheapest provider in Indonesia. By investigating these factors, we aimed to gain insights into optimizing the response time of the safety device, especially in areas with poor internet connectivity&lt;span lang="IN"&gt;. &lt;/span&gt;Utilizing the two-way ANOVA method, this experiment successfully demonstrates that variations in distances and internet providers significantly influence the notification response. The effective combination of the HC-SR501 passive infrared sensor and Nodemcu microcontroller is shown to detect movements inside the vehicle cabin and reliably notify the vehicle owner through their smartphone. These findings underscore the viability and efficiency of the proposed system in enhancing vehicle security and owner awareness.&lt;/p&gt;

Improved Backward Smoothing Square Root Cubature Kalman Filtering and Fractional Order—Battery Equivalent Modeling for Adaptive State of Charge Estimation of Lithium‐Ion Batteries in Electric Vehicles

The accuracy of lithium‐ion battery state of charge (SOC) estimation affects the driving distance, battery life, and safety performance of electric vehicles. Herein, the polarization reaction inside the battery is modeled using a second‐order fractional‐order equivalent circuit model and uses an adaptive genetic algorithm for model parameter identification. Then, an improved adaptive fractional‐order backward smoothing square root cubature Kalman filtering algorithm (AFOBS‐SRCKF) is proposed by integrating Sage Husa adaptive filtering and backward smoothing processes to optimize the square root cubature Kalman filter for improving the accuracy and adaptability of real‐time estimation of SOC in a complex environment. Finally, the algorithm is compared with the integer‐order SRCKF, fractional‐order SRCKF through simulation, and fractional‐order backward smoothing SRCKF through simulation. Under complex operating conditions, the error sum of SOC estimation of the AFOBS‐SRCKF algorithm is controlled within 1.0% and the convergence speed is improved by at least 30%. The results show that the AFOBS‐SRCKF algorithm effectively improves the accuracy, stability, and convergence of SOC estimation.

Analysis of vehicle paths for sharing e-commerce logistics resources in China

Abstract With the change in shopping methods, people have higher and higher requirements for e-commerce logistics transportation. In this paper, we first briefly study the dynamic vehicle path problem under pickup and delivery mixture and determine the modeling idea of regular path adjustment strategy. Then, based on the constraints of vehicle access rules, travel time, vehicle continuity, loading capacity ceiling, dynamic vehicle path adjustment, etc., a mathematical model to minimize the total cost consisting of vehicle call cost, travel cost, and time window penalty cost together is constructed. Finally, a single-objective optimization with the total vehicle distance as the objective is performed on the original data, and the interrelationship between the objectives in the score MOMDVRP algorithm is solved for the optimal route scheme. In the route of the optimal compromise scheme, the distance of each vehicle, although not reaching the equilibrium degree in the social scheme, is much better than the economic scheme and the environmental scheme, and the difference between the longest drive and the shortest drive is 1.47km, which is about 28.79% and 69.9% less than the difference between the economic scheme and the environmental scheme of 2.171km and 495km, and each objective has reached a more balanced state. It is of great practical significance to study the optimization of the shared path of e-commerce logistics transportation resources.

Effect of TPRD diameter and direction of release on hydrogen dispersion and jet fires in underground parking

The paper presents safety strategies to support choice of diameter and release direction for thermally activated pressure relief device (TPRD) onboard a hydrogen fuel cell electric vehicle. Analytical and numerical modelling campaigns were performed to study hazards of unignited and ignited hydrogen releases from a vehicle in a real underground parking. TPRD diameter to prevent flammable mixture formation during upward release was determined using similarity law for hydrogen concentration decay along jet centreline. Parametric CFD simulations studied effect of different TPRD diameter, release direction, vehicle distance from obstacles, ceiling height, influence of carpark ventilation on hydrogen hazards. Hazards of unignited releases were associated with the lower flammability limit of hydrogen-air clouds. For ignited releases the temperature threshold 70 °C was used as a no-harm criteria for assessment of thermal hazards to people and threshold 300 °C as a damage criteria for mechanical ventilation system following requirements of BS 7346-7-2013 standard for carpark ventilation. Recommendations on TPRD design were formulated to support safe integration of hydrogen-powered vehicles in the existing underground parking facilities and infrastructure.

Research on V2V Time Headway Strategy Considering Multi-Front Vehicles Based on Artificial Potential Field

Within the Internet of Vehicles, the driver’s conduct is more affected by the information about the vehicles in front of them. To meet the driver’s demand for the design of the safety vehicle distance of the adaptive cruise control system in the Vehicle-to-Vehicle (V2V) environment, this paper employs the artificial potential field (APF) theory to enhance the vehicle time headway strategy and constructs a mixed force function in the global force field with three front vehicles as the center of the three potential fields. Taking into account the inadequate control effect of conventional APF in a dynamic environment, this paper describes the relative kinematic characteristics of the vehicles in front and the host vehicle as parameters and obtains the improved APF. The improved strategy based on APF enhances the forward-looking and anti-interference ability of the distance strategy. Also, the strategy presented in this paper can achieve time headway control under all operating conditions and prevent acceleration fluctuations due to switching between various control strategies. For collaborative simulation, this research uses MATLAB/Simulink and Carsim software. The simulation is conducted under three common traffic scenarios, and it is determined that the improved control method results in smoother changes in vehicle speed, acceleration, and car-following distance, hence improving driving and riding comfort.