Driving Simulator Experiment Research Articles

Measuring the effects of mind wandering in people with insomnia: A driving simulator study

PurposeStudies have shown that individuals with insomnia experience more frequent and longer episodes of mind wandering (MW) while driving. However, the effect of the interaction between insomnia and MW on driving behavior is not fully understood. This study aimed to gain deeper insights into the relationships among insomnia, MW, and driving behavior.Patients and methodsForty-two participants (21 diagnosed with insomnia and 21 controls) were recruited, and subjective sleep quality and cognitive function were assessed. A driving simulator experiment with a within-subject design was performed, involving two distraction tasks (no-distraction task versus MW task) and two driving scenarios (lane-keeping versus lane-changing).ResultsIn the lane-keeping scenario, there was no significant between-group difference (people with insomnia and controls) in longitudinal driving performance for the no-distraction task, although the interaction between MW and insomnia significantly increased drivers’ longitudinal control variation. Correlation analysis confirmed that longitudinal driving performance was positively correlated with sleep quality and the cognitive level. Unlike longitudinal driving performance, lateral driving performance was significantly weaker in people with insomnia than in controls under both distraction tasks. In the lane-changing scenario, although there was no between-group difference in driving performance, the MW task led to significant changes in driving performance within each group compared with the no-distraction task, and these findings were associated with cognitive function, but not with sleep quality.ConclusionThese findings show that insomnia and MW combined can lead to reduced driving performance. Further research is needed to elucidate the factors that influence this phenomenon.

A graph method of description of driving behaviour characteristics under the guidance of navigation prompt message

AbstractTo verify whether a graph is suitable for describing driver behaviour performance under the effects of navigation information, this study applies two types of prompt messages: simple and detailed. The simple messages contain only direction instructions, while the detailed messages contain distance, direction, road and lane instructions. A driving simulation experiment was designed to collect the empirical data. Two vehicle operating indicators (velocity and lateral offset), and two driver manoeuvre indicators (accelerator power and steering wheel angle) were selected, and T-test was used to compare the differences of behavioural performance. Driving behaviour graphs were constructed for the two message conditions; their characteristics and similarities were further analysed. Finally, the results of T-test of behavioural performance and similarity results of the driving behaviour graphs were compared. Results indicated that the two different types of prompt messages were associated with significant differences in driving behaviours, which implies that it is feasible to describe the characteristics of driving behaviours guided by navigation information using such graphs. This study provides a new method for systematically exploring the mechanisms affecting drivers’ response to navigation information, and presents a new perspective for the optimisation of navigation information.

Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means

Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored.This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations.

Design and Evaluation of a Human-in-the-Loop Connected Cruise Control

The performance of automated vehicle can be greatly improved by enabling vehicle-to-vehicle communication. An example of such applications is Cooperative Adaptive Cruise Control (CACC) along the highway. Although the adoption rate of vehicular connectivity is predicted to grow rapidly, CACC can only benefit vehicles that are both connected and automated. To take a full advantage of vehicular connectivity, a human-in-the-loop Connected Cruise Control (hCCC) algorithm is developed for human-driven connected vehicle. In hCCC, the human driver remains engaged in the longitudinal control of the vehicle, and hCCC controller applies additional acceleration/deceleration on top of human actions according to the received status of preceding vehicle. By allowing coexistence of the automatic control and driver's actions in a beneficial way, hCCC helps the human driver stabilize the vehicle more efficiently and safely. The proposed hCCC inherits the feedback-feedforward control structure and velocity-dependent spacing policy from the typical CACC systems. String stability analysis shows that hCCC can offer broader string-stable ranges of human parameters than human driving alone or the existing acceleration-based Connected Cruise Control (CCC), indicating a better capability to mitigate traffic disturbance with the uncertain human behaviors. The desirable properties of hCCC were validated in driving simulator experiments, which showed that hCCC could reduce 36.8% acceleration, 31.2% time-gap fluctuation, 81.2% exposure time to unsafe driving situations, and 15.8% fuel consumption from those of human driving alone. In addition, two derivative designs of hCCC are proposed and proven effective, further lowering down the practice threshold of hCCC.

Research on the transition marking between the broken line and solid line of expressway

Drivers often cross the solid line unintentionally during the lane-changing, when the sight distance is blocked by the vehicle ahead. There are significant potential risks to change lanes in solid line area, which would cause traffic flow disruption and traffic safety hazards. If the drivers with limited sight distance can be reminded in advance that the lane marking ahead is a solid line, many drivers will refrain from changing lanes in the restricted area, therefore, it is important to help the drivers with limited sight distance to obtain the information of lane marking ahead. There is limited research on this specific issue presently, nor any relevant regulations or manuals available in China. This paper puts forward a novel concept of transition marking, and a creative approach by adding the transition marking between the broken line and the solid line. The transition marking is considered an information carrier to predict the pass-through right of the road ahead. We have studied the form and length of the transition marking and evaluated its effectiveness through the driving simulation experiment. The form analysis shows that the white mutation broken line has better visibility and legibility. The transition marking length is shown to be closely related to the vehicle speed and distance. The simulation results indicate that the transition marking length is negatively correlated with vehicle distance and positively correlated with speed. Based on the simulation results and detailed statistical analysis, the reference value of transition marking length is proposed. The effectiveness evaluation demonstrates that the transition marking reduces the probability of solid-line crossing during lane-changing dramatically by 92%.

Effects of Speed Reduction Marking Patterns on Simulated Driving Speed and Lane Position

Speed reduction markings have been installed on highways as perceptual countermeasures for speeding. However, little is known about the effects of the shape and interval of road markings on driving speed and lane position. In this paper, a driving simulator experiment and questionnaires were performed to explore the effects of speed reduction marking patterns on driving speed and standard deviation of lane position (SDLP), as well as drivers’ subjective feelings, mental workload, and visual attention. Thirty-nine participants drove on a simulated two-lane rural highway where speed reduction markings with different shapes and intervals were presented at horizontal curves. The pavement markings were associated with reduced throttle values and mean speed in advance of a horizontal curve. The marking shape did not affect participants’ speed choice or SDLP. A cognitive alerting effect of the speed reduction markings was dominant because the participants did not drive more slowly with the markings with converging intervals toward the traveling direction compared to those with a constant interval. A questionnaire on drivers’ attention reflected a potential use of road markings for drivers’ lane-position maintenance. Since less than 18 % of the participants noticed the convergence in marking intervals, speed reduction markings may also induce the perceptual illusion of acceleration.

Investigating driver reactions to movements of autonomous vehicle in permitted right turn through driving simulator experiments

Traffic safety has always been a hot topic for human-driven (HDV) and autonomous vehicles (AV) mixed flow. The conflict between permitted right-turn vehicles (PRT) and opposing through vehicles (TH) at signalized intersections (left-handed traffic) is extraordinarily critical. AVs with aggressive behaviors are able to accept short gap time without losing safety. However, such a turning maneuver may lead to dangerous feelings and cause unexpected reactions of approaching drivers. This study aims to investigate and model drivers’ reactions in TH movements to PRT AVs considering the trust degree of drivers to AVs. Questionnaire surveys and driving simulator experiments were conducted for 41 participants. Results reveal that the right turn timing of PRT AV will significantly influence drivers’ reactions. Basically, TH drivers will brake with a high probability under the situation of small expected post encroachment time (PET). It is also found that female drivers and drivers with low trust in AVs are more vigilant to PRT AVs than other drivers. Based on this finding a two-layer model for reproducing TH drivers’ reactions to PRT AVs is proposed. The first layer is to determine the braking decision and the second layer is to calculate the parameters of braking behaviors (brake lag, braking time, and speed drop). The significance and coefficients variables in these models proved that the trust in AV will influence drivers’ decisions and braking behaviors (brake lag and braking time). The more the drivers trust AVs, the smaller the expected PET to AVs they can accept for passing without braking, and the more gently they will brake (longer brake lag and shorter braking time) due to the cutting in of PRT AVs. This effect will become significant after drivers have experienced several interactions with AVs.

Research on the effectiveness of a intersection risk warning system based on driving simulation experiment

In order to improve the traffic safety condition of intersections, a real-time traffic conflict risk warning system (RTCRWS) is proposed for uncontrolled intersections. To evaluate the effectiveness of this system, a driving simulation experiment was designed and conducted. In this study, a virtual experimental scene including static road, traffic environment and dynamic traffic flow was constructed, and 45 drivers were recruited to complete the driving simulation experiment at 13 intersections. Three different data analysis methods were employed: (1) descriptive analysis of driving behavior characteristics; (2) descriptive analysis of physiological and psychological reactions of drivers; (3) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) of RTCRWS. The results show that RTCRWS can effectively control the vehicle speed and reduce the driver's tension. In addition, the following conclusions are also drawn: (1) The early warning signs with better warning effect among the two types signs of RTCRWS were compared; (2) Among the elderly and young and middle-aged drivers, RTCRWS has a better warning effect on the elderly drivers. (3) Among the male and female drivers, RTCRWS has a better warning effect on female drivers.

Non‐instinct detection of cellphone usage from lane‐keeping performance based on eXtreme gradient boosting and optimal sliding windows

Driving distraction caused by cellphone usage has become a common safety threat. As distraction detection methods based on driver's position or eye movement may raise privacy issues, a promising way is to analyze the vehicle's lane-keeping performance. This paper proposed a detection algorithm based on eXtreme gradient boosting (XGBoost), to develop a real-time driving distraction detection based on lane-keeping performance. The algorithm includes knowledge-based volatility feature extraction and feature selection by recursive feature elimination (RFE). To obtain dynamic patterns of lane-keeping performance affected by different types of cellphone usage, browsing a short message, browsing a long message, and answering a phone call, a driving simulator experiment was conducted on 28 drivers. Results showed that the proposed XGBoost-RFE method is reliable and promising to predict phone usage with 80% accuracy. The results also evoke the fact that sliding window size, which is about 80% of subtask duration, can be appropriate for real-time detection of multiple cellphone usages. For overlap percentages, 67% of sliding window size can balance the efficiency and continuity of data in adjacent sliding windows. The paper's potential application includes the design of a real-time driving distraction detection system.

In-vehicle warnings for work zone and related rear-end collisions: A driving simulator experiment

Work zone area on roads is a critical component of road networks which concerns the safety of workers and passing by drivers. However, the passive speed reduction and lane changes caused by lane closure have led to frequent rear-end collisions in work zone areas. To help drivers better anticipate work zone situation and reduce collision risks, this paper proposes two types of in-vehicle warnings for work zone areas: Leading Vehicle Brake Warning (LVBW), and Lane-Closed Warning & Leading Vehicle Brake Warning (LCW & LVBW). The LVBW delivers a danger warning message to drivers upon the brake of the leading vehicle, while the LCW & LVBW provides an additional work-zone position message to remind drivers to decelerate in advance. A driving simulator experiment was conducted with 44 participants (24 males and 20 females) to test drivers’ performance in work zone area under different conditions, comprising two warning types (LVBW vs. LCW & LVBW), four warning times (3 s, 5 s, 7 s and 9 s) and two visibility conditions (clear and foggy weather). The results showed significant safety benefits of the lane-closed warning message under the LCW & LVBW condition. In contrast, the warning of leading vehicle’s brake in both LVBW and LCW & LVBW conditions had limited efficacy, which indicates that earlier warning about lane-closure is important to assist drivers in anticipating the complex situations in work zones. Drivers’ speed control and collision avoidance performances were impaired in fog, but the impairment was compensated by the warning messages. Compared with male drivers, female drivers tend to be more cautious when approaching the work zone areas. Overall, this study plays a pioneering role in developing effective safety countermeasures for work zone areas and providing strong support for implementing in-vehicle warning technologies.

Driving simulator performance worsens after the Spring transition to Daylight Saving Time

SummaryCircadian desynchrony and sleep deprivation related to the Spring transition to Daylight Saving Time (DST) have been associated with several unfavorable outcomes, including an increase in road traffic accidents. As previous work has mainly focused on analyzing historical crash/hospitalization data, there is virtually no literature investigating the effects of DST on specific driving performance indicators. Here, the effect of the Spring transition to DST on driving performance was investigated by means of a driving simulator experiment, in which participants completed two trials (one week distance, same time and day of the week) on exactly the same simulated route, the second trial taking place in the week after the transition to DST. Results were compared to those of a control group (who also underwent two trials, both before the DST transition), and documented significant worsening of driving performance after DST, as measured by a comprehensive set of simulator-derived indices.

Improvements of Warning Signs for Black Ice Based on Driving Simulator Experiments.

Black ice is one of the main causes of traffic accidents in winter, and warning signs for black ice are generally ineffective because of the lack of credible information. To overcome this limitation, new warning signs for black ice were developed using materials that change color in response to different temperatures. The performance and effects of the new signs were investigated by conducting driver behavior analysis. To this end, driving simulator experiments were conducted with 37 participants for two different rural highway sections, i.e., a curve and a tangent. The analysis results of the driving behavior and visual behavior experiments showed that the conventional signs had insufficient performance in terms of inducing changes in driving behavior for safety. Meanwhile, the new signs actuated by weather conditions offered a statistically significant performance improvement. Typically, driver showed two times higher speed deceleration when they fixed eyes on the new weather-actuated warning sign (12.80 km/h) compared to the conventional old warning sign (6.84 km/h) in the curve segment. Accordingly, this study concluded that the new weather-actuated warning signs for black ice are more effective than the conventional ones for accident reduction during winters.

Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving

The objective of this study was to verify the effectiveness of eye-tacking metrics in indicating driver’s mental workload in semi-autonomous driving when the driver is engaged in different non-driving related tasks (NDRTs). A driving simulator was developed for three scenarios (high-, medium-, and low-mental workload presented by SAE (Society of Automotive Engineers) Levels 0, 1, and 2) and three uni-modality secondary tasks. Thirty-six individuals participated in the driving simulation experiment. NASA-TLX (Task Load Index), secondary task performance, and eye-tracking metrics were used as indicators of mental workload. The subjective rating using the NASA-TLX showed a main effect of autonomous level on mental workload in both visual and auditory tasks. Correlation-matrix calculation and principal-component extraction indicated that pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy were effective indicators of a driver’s mental workload in the visual and auditory multi-tasking situations of semi-autonomous driving. The accuracy of predicting the mental-workload level using the K-Nearest Neighbor (KNN) classifier was 88.9% with bootstrapped data. These results can be used to develop an adaptive multi-modal interface that issues efficient and safe takeover requests.

Car following with an inertia-oriented driving technique: A driving simulator experiment

BackgroundFor many decades, car-following (CF) and congestion models have assumed a basic invariance: drivers’ default driving strategy is to keep the safety distance. The present study questions that Driving to keep Distance (DD) is a traffic invariance and, therefore, that the difference between the time required to accelerate versus decelerate must necessarily determine the observed patterns of traffic oscillations. Previous studies have shown that drivers can adopt alternative CF strategies like Driving to keep Inertia (DI) by following basic instructions. The present work aims to test the effectiveness of a DI course that integrates 4 tutorials and 4 practice sessions in a standard PC computer designed to learn more adaptive driving behaviors in dense traffic. Methods. Sixty-eight drivers were invited to follow a leading car that varied its speed on a driving simulator, then they took a DI course on a PC computer, and finally they followed a fluctuating leader again on the driving simulator. The study adopted a pretest-intervention-posttest design with a control group. The experimental group took the full DI course (tutorials and then simulator practice). The control group had access to the DI simulator but not to the tutorials. Results. All participating drivers adopted DD as the default CF mode on the pre-test, yielding very similar results. But after taking the full DI course, the experimental group showed significantly less accelerations, decelerations, and speed variability than the control group, and required greater CF distance, that was dynamically adjusted, spending less fuel in the post-test. A group of 8 virtual cars adopting DD required less space on the road to follow the drivers that took the DI course.

Simulating the effect of different lighting conditions on left-turn driving behavior using a scenario-based anger method

Anger is a key factor affecting drivers' subjective judgment and driving skills. The influence of anger on driving behavior has been widely studied, but there is a lack of comparative research under different lighting conditions. Through a driving simulation experiment, this paper studies the influence of anger on left-turn driving behavior under two light conditions day and night. In the experiment, 32 licensed participants were divided into two groups, one in emotional neutrality and the other in anger. Among them, the emotional state of anger is induced by a traffic-related video. The results showed that compared with daytime participants, participants at night had higher anger intensity, shorter gap acceptance, and post encroachment time (PET) when left-turn driving. In addition, compared with neutral emotion participants, angry participants tended to accept shorter gap acceptance and PET when turning left. This indicates that participants failed to respond correctly to left-turn driving behavior in a state of anger. However, the response of gender differences to situational driving anger was not affected by light conditions. The anger intensity of male participants during the day and night was higher than that of female participants, and the gap between acceptance and PETs during left-turn was shorter than that of female participants. This shows that male participants are more likely to produce high-intensity anger and are more likely to make dangerous driving decisions in a state of anger. This paper puts forward some suggestions on identifying anger and preventing angry driving.

Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing.

The aim of this study was to quantify the effect of radius over horizontal curve sections on driving workload (DW). Twenty-five participants participated in the driving simulation experiments and completed five driving scenes. The NASA-TLX scale was used to measure the mental demand, physical demand, and temporal demand in various scenes, which were applied to assess subjective workload (SW). Objective workload (OW) assessment methods were divided into three types, in which the eye tracker was used to measure the blink frequency and pupil diameter, and the electrocardiograph (ECG) was used to measure the heart rate and the heart rate variability. Additionally, the simulator was used to measure the lateral position and the steering wheel angle. The results indicate that radius is negatively correlated with DW and SW, and the SW in a radius of 300 m is approximately twice that in a radius of 550 m. Compared with the ECG, the explanatory power of the OW can be increased to 0.974 by combining eye-movement, ECG, and driving performance. Moreover, the main source of the DW is the maneuver stage, which accounts for more than 50%. When the radius is over 550 m, the DW shows few differences in the maneuver stage. These findings may provide new avenues of research to harness the role of DWs in optimizing traffic safety.

An exploratory study of drivers’ EEG response during emergent collision avoidance

Introduction: EEG (electroencephalogram) has been applied as a valuable measure to estimate drivers’ mental status and cognitive workload during driving tasks. However, most previous studies have focused on the EEG features at particular driver status, such as fatigue or distraction, with less attention paid to EEG response in emergent and safety–critical situations. This study aims to investigate the underlying patterns of different EEG components during an emergent collision avoidance process. Method: A driving simulator experiment was conducted with 38 participants (19 females and 19 males). The scenario included a roadside pedestrian who suddenly crossed the road when the driver approached. The participants’ EEG data were collected during the pedestrian-collision avoidance process. The log-transformed power and power ratio of four typical EEG components (i.e., delta, theta, alpha and beta) were extracted from four collision avoidance stages: Stage 1-normal driving stage, Stage 2-hazard perception stage, Stage 3-evasive action stage, and Stage 4-post-hazard stage. Results: The activities of all four EEG bands changed consistently during the collision avoidance process, with the power increased significantly from Stage 1 to Stage 4. Drivers who collided with the pedestrian and drivers who avoided the collision successfully did not show a significant difference in EEG activity across the stages. Male drivers had a higher delta power ratio and lower alpha power ratio than females in both hazard perception and evasive action stages. Conclusions: Enhanced activities of different EEG bands could be concurrent at emergent and safety–critical situations. Female drivers were more mentally aroused than male drivers during the collision avoidance process. Practical Applications: The study generates more understanding of drivers’ neurophysiological response in an emergent and safety–critical collision avoidance event. Driver state monitoring and warning systems that aim to assist drivers in impending collisions may utilize the patterns of EEG activity identified in the collision avoidance process.

Stochastic Cooperative Game-Based Assistant Fault-Tolerant Control Considering Driver Behavior Uncertainty

A stochastic cooperative game (SCG)-based assistant fault-tolerant control (FTC) is proposed in this article for maintaining the stability of the distributed drive electric vehicle (EV) suffering actuator failure, which also takes the driver’s uncertain behavior into account. A distributed stochastic model prediction control (DSMPC) framework is leveraged to simulate the interaction among the driver, automatic steering, and in-wheel motors. First, a stochastic driver-vehicle model that includes driver uncertainties and the potential actuator failure is established. Then, the idea of the cooperative game is introduced to mathematically describe the conflict and cooperation among the driver and automatic control subsystems. Specifically, the human driver and the automated control subsystems are modeled as players, whose interests are represented by the mathematical expectation of the cost function considering the stochastic nature of the system. Finally, the Pareto solution of the cooperative game is derived using a convex iteration algorithm. A driving simulator experiment is performed to capture the stochasticity of different drivers. Numerical simulations of two serious failures prove that the proposed SCG method can better maintain the stability of a failed vehicle while also taking account of the driver behavior uncertainty.

Behavior model and guidance strategies of the crossing behavior at unsignalized intersections in the connected vehicle environment

The connected vehicle environment is considered to be a disruptive technology that reconstructs the way people travel and road transport, and more importantly, ensures traffic safety. This study aims on investigating drivers’ interactive behavior at an unsignalized intersection in the connected vehicle environment. Specifically, a simplified iterative behavior model was established to predict the potential conflicting vehicles’ behavior. Furthermore, based on principles of safety, efficiency, and comfort, the guidance strategies were proposed to help the subject vehicles cross intersections. A multi-user driving simulator experiment was carried out and 48 participants were divided into 24 pairs to complete the test. The simplified iterative behavior model was constructed based on the dynamic interaction of each participant pair as they approached the intersection from straight-crossing directions. The comparison results showed the behavior model was an effective microscopic simulation tool for vehicles at unsignalized intersections with high accuracy and good applicability. Then, the guidance strategies under different compliance rates (baseline, 50 % compliance, 100% compliance) were evaluated based on three indexes i.e. standard deviation of speed (SDS), duration of crossing the intersection (DCI), and time exposed post-encroachment-time (TEP). The numerical simulation results showed that the guidance strategies could effectively improve the safety and efficiency of drivers crossing the intersection under both 50% and 100% compliance rates. Besides, with the increase of compliance rate, the comfort level also increased evidently. This study can provide theoretical and algorithmic references for microscopic simulation and guidance strategy at unsignalized intersections in the connected vehicle environment.

Quantitative Study on Road Traffic Environment Complexity under Car-Following Condition

With the development of the drive of electronic communication technology, the driving assistance system that perceives the external traffic environment has developed rapidly. However, when quantifying the complexity of the road traffic environment without fully considering the driving characteristics and subjective feelings, the false alarm rate of the driving warning system increases and affects the early warning effect. In order to more accurately quantify the complexity of the road traffic environment, we analyzed the impact of road traffic environment changes on drivers under the condition of car-following. Firstly, we selected the influencing factors of the traffic environment complexity, such as the driving operation indicators, the vehicle driving status indicators and the road environmental indicators. The weight calculation model of each influence factor is established based on the principal component analysis method. Secondly, the driver’s reaction time during car-following is used as the quantitative index of road traffic environment complexity. The quantitative model of road traffic environment complexity is constructed combined with the weight of road traffic environment complexity. Finally, the driving simulation experiment is designed to verify the complexity quantification model of the road traffic environment. The road traffic environment complexity value calculated in our study is better than the TTC, and the early-warning threshold is raised by 2–5%. The research conclusion can provide a basis for the design of the car alarm system.