Pedestrian-vehicle Interactions Research Articles

Augmented reality interfaces for pedestrian-vehicle interactions: An online study

Augmented Reality (AR) technology could be utilised to assist pedestrians in navigating safely through traffic. However, whether potential users would understand and use such AR solutions is currently unknown. Nine novel AR interfaces for pedestrian-vehicle communication, previously developed using an experience-based design method, were evaluated through an online questionnaire study completed by 992 respondents in Germany, the Netherlands, Norway, Sweden, and the United Kingdom. The AR indicated whether it was safe to cross the road in front of an approaching automated vehicle. Each interface was rated for its intuitiveness and convincingness, aesthetics, and usefulness. Moreover, comments were collected for qualitative analysis. The results indicated that interfaces that employed traditional design elements from existing traffic, and head-up displays, received the highest ratings overall. Statistical results also showed that there were no significant effects of country, age, and gender on interface acceptance. Thematic analysis of the textual comments offered detail on each interface design’s stronger and weaker points, and revealed unintended effects of certain designs. In particular, some of the interfaces were commented on as being dangerous or scary, or were criticised that they could be misinterpreted in that they signal that something is wrong with the vehicle, or that they could occlude the view of the vehicle. The current findings highlight the limitations of experience-based design, and the importance of applying legacy design principles and involving target users in design and evaluation. Future research should be conducted in scenarios in which pedestrians actually interact with approaching vehicles.

Traffic Analysis and Design of Signalling System for Pedestrial Interaction at Pusad Naka Road, Washim

Several studies are there to understand the pedestrian movement and all the studies are based on fundamental diagrams only. These studies construct a base to characterize pedestrian flow. Several experiments have conducted to understand the pedestrian flow, likewise some field observations have done to represent fundamental diagrams. Therefore, before going to analyze the data from the observation, it is necessary to note down the pedestrian flow parameters carefully. The aim of the paper is to build up the base to fundamental diagrams and for characterization of pedestrian. And derive the required flow diagrams and results from the field observations. Field survey is conducted to know the vehicle pedestrian interaction, and this field data with respect to pedestrian crossing at signalized, Unsignalized or at midblock sections 6is aimed to be observed. And the impact of vehicle pedestrian interaction at several intersections/midblock sections is to be studied. This data is used to determine the normal traffic flow and the impact of heavy vehicles or pedestrians on the volume of traffic. The information gathered is converted into PCUs (Passenger Car Units). Webster's method of signal design is logical. The design is straightforward and entirely based on Webster's formulas. The complete signal cycle is thus determined by what causes the shortest complete delay in the signal. To do this, several places are chosen from Pusad Naka Washim. It is aimed to observing whether the pedestrian fundamental diagram is different in alternate locations or not. In this study it is found that fundamental diagrams are different in different locations of Washim

Behavioral pathway analysis of pedestrian injury severity in pedestrian-motor vehicle crashes

Despite the health and environmental benefits of walking as an active and sustainable mode of transport, the increasing number of road crashes involving pedestrians has become a public health concern. This study investigates the direct and indirect effects of various contributing factors on pedestrian injury severity in pedestrian-motor vehicle crashes. Data on pedestrian-vehicle crashes from North Carolina between 2015 and 2019, including the pedestrian pre-crash actions and injury severity, were used. Path analysis was applied to uncover the inter-relationship between contributing factors, pedestrian pre-crash behaviors, and injury severity using random parameter ordered logit and mixed logit models. The results indicate that pedestrian intoxication, lighting condition, number of lanes, speed limit, and vehicle type contribute directly and indirectly (through pre-crash behaviors) to increasing injury severity. It was also observed that pedestrian position and traffic control directly decreased injury severity but indirectly increased injury severity through pre-crash behaviors. This study provides valuable information about the relationship between various crash factors and pedestrian injury severity. For researchers and agencies interested in pedestrian safety improvements, this study gives valuable insight by uncovering the roles of pedestrians’ pre-crash action on injury severity outcomes. For countermeasures, the study recommends better street lighting and re-evaluation of posted speed limits on roads with high pedestrian traffic. A regular educational campaign should inform pedestrians of the importance of yielding to motorists at locations without crosswalks. Also, drivers’ compliance with existing pedestrian-vehicle interaction laws should be monitored, especially at intersections.

Spatiotemporal Attention-Based Pedestrian Trajectory Prediction Considering Traffic-Actor Interaction

Accurate pedestrian trajectory prediction is significant and challenging for traffic-actor protection and intelligent driving. However, most of the existing methods focus on pedestrian-pedestrian interaction ignoring the pedestrian-vehicle interaction and containing much ineffective interaction. To tackle the problem, this paper proposes a spatiotemporal LSTM attention network (STLAN) for pedestrian trajectory prediction based on a spatiotemporal dual attention module (STDAM). First, a pedestrian social force model is established to analyze the effective pedestrian-pedestrian interaction and pedestrian-vehicle interaction in mixed traffic scene. Second, a pedestrian trajectory prediction network architecture is constructed to integrate temporal and spatial information. By temporal attention STDAM captures underlying dependencies between time steps in individual pedestrian, assigning different weights to the input sequence of it. By spatial attention STDAM extracts latent interaction between traffic actors, assigning different weights to every traffic actor. In addition, random noise is introduced to simulate the uncertainty of pedestrian trajectory. Experimental results on a real-world traffic actors dataset show that STLAN outperforms several mainstream methods. Specifically, STLAN achieves 0.17/0.11 ADE/FDE and 3.79/0.38 AMD/AMV in the representative pedestrian-pedestrian interaction scene, with 0.15/0.17 ADE/FDE and 2.36/0.21 AMD/AMV in pedestrian-vehicle interaction scene. Furthermore, ablation experiments validate the effectiveness of STDAM in improving trajectory prediction accuracy.

Red LED Strip Signalling Pedestrian Presence at Uncontrolled Mid-block Crosswalks

A relevant number of collisions between vehicles and pedestrians occur at night because of the reduced capacity of a driver to perceive and react to the presence of pedestrians at crosswalks. To reduce this risk of collision, new smart road technologies have been introduced. This study aims to evaluate the effectiveness of a red LED strip that warns the driver of the presence of pedestrians at unsignalized mid-block crosswalks at night-time. The technology consists of LED strips installed near the crosswalk on the side of oncoming vehicles. The LED is activated when a sensor detects a pedestrian approaching the crosswalk. In this driving simulation experiment, three different urban mid-block crosswalks were evaluated: (i) without an LED strip (baseline), (ii) with a fixed LED strip, and (iii) with a flashing LED strip. Each scenario was tested for three different levels of pedestrian time gap acceptance (PTGA) (i.e., 4, 6, 8 s) and two road sections (i.e., 1 lane vs. 2 lanes). Thirty-six participants took part in a within-subjects’ design. During the experiment, the minimum time to collision (mTTC), the post-encroachment time (PET), maximum vehicle speed, and reaction distance were monitored to quantitatively compare the different levels of vehicle-pedestrian interaction. Compared to the baseline, the LED strip resulted in a safer driver-pedestrian interaction, with an average increase in mTTC of 1.13 s, PET of 0.66 s, and a longer average reaction distance of 7.9 m. However, a slight (albeit not statistically significant) increase in speed was observed following installation of the LED strip. Furthermore, no significant differences were observed between fixed and flashing LED strips. Overall, these results confirm the LED strips effectiveness in alerting the driver to the presence of a pedestrian, thus increasing the safety of their interaction. Further studies should confirm these findings in a more ecological way, e.g., evaluating the safety impact of this technology under different weather and distracted driving conditions.

Pedestrians’ perceived vulnerability and observed behaviours relating to crossing and passing interactions with autonomous vehicles

Researchers have predicted that “vulnerable road users” (VRUs) such as pedestrians will feel less vulnerable and thus take more risks around autonomous vehicles (AVs) than around human-operated vehicles (HOVs). However, data on the behaviours pedestrians are likely to display during passing as well as crossing interactions with AVs – particularly from naturalistic studies – are currently lacking. Such data could help inform AV system designers and authorities, as well as researchers. So, a novel study was conducted in London, UK. Perceived vulnerability was gauged via a survey on hypothetical pedestrian-vehicle interactions (N = 267). Behaviours were observed during real crossing and passing interactions with AV shuttle pods in a shared space (N = 330). While pedestrians were the main focus, joggers and cyclists were also frequenting the observation site and were included in the analysis of passing interactions. The survey results showed that pedestrians were not perceived to be less vulnerable around AVs. Diminishing initial boldness in the crossing interactions, and high yielding in the passing interactions, supported this, demonstrating that VRUs were not taking undue risks; rather, they appeared to be experiencing some uncertainty and discomfort. Further results showed other VRU behaviours (gap acceptance, inattention, hesitation, changes in speed, explicit communication, a side preference) may be relevant in AV interactions, but not necessarily to the same degree as when around HOVs or not in line with UK road rules. Positive conclusions were drawn for AV programming, and for safety, at least in the short term, but concerns regarding mobility need addressing.

The role of yielding cameras in pedestrian-vehicle interactions at un-signalized crosswalks: An application of game theoretical model

ObjectivePedestrian–vehicle interaction has gained substantial interest in the field of travel behavior and safety. Although traffic rules stipulate the right of way at unsignalized crosswalks, the actual behavior of road users is usually based on game-theoretical solutions. Moreover, non-cooperative games between pedestrians and drivers can be more complicated in the presence of law enforcement equipment. This study aimed to investigate the impact of yielding cameras on road users’ joint behavior at unsignalized crosswalks using a game-theoretical model.Methods and analysisA field survey was conducted using aerial and roadside cameras. In the game-theoretical model, road users were assumed to behave in accordance with the quantal response equilibrium. Additionally, the presence of a yielding camera was introduced into the payoff functions. Influencing factors, including demographics, road users’ behavioral characteristics, and environment, were incorporated into road users’ utilities. The beliefs of road users regarding other road users’ actions were also considered in the model. Furthermore, a questionnaire survey was conducted to investigate road users’ knowledge and awareness of yielding cameras and their right of way.ResultsThe results indicate that the yielding camera significantly affected driver behavior but had minimal effect on pedestrians crossing decisions because fewer pedestrians were aware of the yielding camera while crossing. Moreover, the camera increased the probability of confusion in the game between pedestrians and drivers (drivers chose to yield, whereas pedestrians chose to stay), thus adversely affecting traffic efficiency.Novelty and improvementA novel game-theoretical model was established to evaluate road users’ joint behaviors at unsignalized crosswalks. Several practical suggestions have been provided to improve the effectiveness of yielding cameras and traffic efficiency.

An Examination of Child Pedestrian Rule Compliance at Crosswalks around Parks in Montreal, Canada.

This study aims to examine child pedestrian safety around parks by considering four rule-compliance measures: temporal, spatial, velocity and visual search compliance. In this regard, street crossing observations of 731 children were recorded at 17 crosswalks around four parks in Montreal, Canada. Information on child behaviors, road features, and pedestrian-vehicle interactions were gathered in three separate forms. Chi-square tests were used to highlight the individual, situational, behavioral and road environmental characteristics that are associated with pedestrian rule compliance. About half of our sampled children started crossing at the same time as the adults who accompanied them, but more rule violations were observed when the adult initiated the crossing. The child's gender did not have a significant impact on rule compliance. Several variables were positively associated with rule compliance: stopping at the curb before crossing, close parental supervision, and pedestrian countdown signals. Pedestrian-car interaction had a mixed impact on rule compliance. Overall, rule compliance among children was high for each of our indicators, but about two-thirds failed to comply with all four indicators. A few measures, such as longer crossing signals and pedestrian countdown displays at traffic lights, may help to increase rule compliance and, ultimately, provide safer access to parks.

How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study.

One of the major challenges for autonomous vehicles (AVs) is how to drive in shared pedestrian environments. AVs cannot make their decisions and behaviour human-like or natural when they encounter pedestrians with different crossing intentions. The main reasons for this are the lack of natural driving data and the unclear rationale of the human-driven vehicle and pedestrian interaction. This paper aims to understand the underlying behaviour mechanisms using data of pedestrian–vehicle interactions from a naturalistic driving study (NDS). A naturalistic driving test platform was established to collect motion data of human-driven vehicles and pedestrians. A manual pedestrian intention judgment system was first developed to judge the pedestrian crossing intention at every moment in the interaction process. A total of 98 single pedestrian crossing events of interest were screened from 1274 pedestrian–vehicle interaction events under naturalistic driving conditions. Several performance metrics with quantitative data, including TTC, subjective judgment on pedestrian crossing intention (SJPCI), pedestrian position and crossing direction, and vehicle speed and deceleration were analyzed and applied to evaluate human-driven vehicles’ yielding behaviour towards pedestrians. The results show how vehicles avoid pedestrians in different interaction scenarios, which are classified based on vehicle deceleration. The behaviour and intention results are needed by future AVs, to enable AVs to avoid pedestrians more naturally, safely, and smoothly.

Multi-object trajectory extraction based on YOLOv3-DeepSort for pedestrian-vehicle interaction behavior analysis at non-signalized intersections

Multi-object trajectory extraction based on YOLOv3-DeepSort for pedestrian-vehicle interaction behavior analysis at non-signalized intersections

Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features

Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particularly challenging as pedestrian behavior can be influenced by multiple factors. In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach. To this end we investigate real-world pedestrian-vehicle interactions with human drivers. Based on human driving behavior we then derive appropriate reaction patterns of an automated vehicle and determine requirements for the prediction of pedestrians. This includes a novel metric tailored to measure prediction performance from a system-level perspective. The proposed metric is evaluated on a large-scale dataset comprising thousands of real-world pedestrian-vehicle interactions. We furthermore conduct an ablation study to evaluate the importance of different contextual cues and compare these results to ones obtained using established performance metrics for pedestrian prediction. Our results highlight the importance of a system-level approach to pedestrian behavior prediction.

Lane-based Distance-Velocity model for evaluating pedestrian-vehicle interaction at non-signalized locations

Pedestrian vehicle conflicts at non-signalized crosswalks are a world-wide safety concern. Although the “pedestrian priority” policy is applied in some regions to improve pedestrian safety, its effect needs further investigation. This study proposes the Lane-based Distance-Velocity model (LDV) to investigate pedestrian-vehicle interaction at non-signalized crosswalks. Compared with the DV model, the LDV model considers the lateral distance between vehicles and pedestrians. Therefore, the LDV model extends the application of the DV model by allowing it to be applied not only on one-lane streets to multi-lane streets. The conflict severities of pedestrian-vehicle interaction in the LDV model are classified into four categories: safe-passage, mild-interaction, potential-conflict and potential-collision. Based on that, pedestrian crossing decisions are graded as safe-crossing, risky-crossing, and dangerous-crossing. The experimental data are collected at a non-signalized crosswalk through drone footage collected in Xi’an City (China) with a Machine Vision Intelligent Algorithm. The model is tested through a case study to evaluate pedestrian crossing safety when interacting with private cars and taxis. Results from the case study suggest that the proposed model works well in the pedestrian-vehicle interaction analysis. Firstly, 87.9% of drivers are willing to provide right-of-way to pedestrians when they have enough time to react and yield. Then, both the DV model and LDV model have reached consistent conclusions: the deliberate violation rate (DVR) of taxi drivers is 22.64%, which is double that of private car drivers. Last, taxis commit a higher percentage of pedestrians' dangerous or risky crossing situations than private cars. Relevant government departments can utilize the results of this study to manage urban traffic better and improve pedestrian safety.

A critical review on pedestrian crossing behaviour and pedestrian-vehicle interactions

A critical review on pedestrian crossing behaviour and pedestrian-vehicle interactions

Microsimulation Modeling of Traffic Congestion at Signal Intersection of Thirumangalam Chennai Metrocity

Background: Generally, Chennai is a hub of traffic congestion, where the major roads of the city are filled with vehicle. It gives the challenging situation to the road and traffic management officials to control and regulate the existing traffic with the existing facilities. Objectives: This paper deals with the microscopic simulation of existing road network model which helps in evaluating the performance of the Thirumangalam road intersection such as delay, queue length and also to evaluate and understand the pedestrian vehicle interaction at the intersection and to propose the improvements of signalized intersection in Thirumangalam at Chennai which is without basic facilities like pedestrian crossing time and it causes heavy congestion during peak hour which makes the management to operate the signals manually. Methods/Findings: PTV VISSIM software is used for evaluation and validation of the road network in order to choose the best alternative and optimization measures before implementation. This study helps to improve the existing junction by suggesting the improvement proposals by referring Indian road congress codes (IRC 106, 86, 103) and it also gives the new optimized signal design which is automated to regulate the traffic. Keywords: Signalized Intersections; Signal Design; Microscopic Simulation; Junction Improvements

Interdependence of driver and pedestrian behavior in naturalistic roadway negotiations

Objective This paper characterizes the actions of pedestrian-driver dyads by examining their interdependence across intersection types (e.g., zebra crossings, stop signs). Additionally, the analysis of interdependence captures other external factors, such as other vehicles or pedestrians, that may influence the interaction. Methods A 228 epoch vehicle-pedestrian interaction dataset was extracted from a large naturalistic driving data collection effort, which included vehicle, pedestrian, and contextual information (e.g., intersection type, jaywalking, vehicle maneuver, and lead vehicle presence). An expanded Actor-Partner Interdependence Model (APIM) was used to analyze driver-pedestrian dyads using driver and pedestrian standard deviations of velocity as the independent variables and wait times as dependent variables. APIM structural equation models were augmented to include driver effects (i.e., lead vehicle and maneuver type) and pedestrian effects (i.e., lead pedestrian, crossing group size, crossing direction). Results The level of protection afforded by an intersection had an effect on the extent of driver-pedestrian dyadic behavior. Interactions in undesignated crossings (i.e., jaywalking) were associated with interdependent behavior whereas interactions in designated crossings (i.e., crosswalks and parking lots) showed a partner effect on the driver’s wait time but no significant corresponding partner effect on the pedestrian. Finally, protected intersection interactions (i.e., traffic lights and stop signs) demonstrated no significant partner effects. Conclusions The difference in behavior patterns associated with the intersection type and level of protection shows that context can mediate the level of negotiation required between drivers and pedestrians. These findings inform how context and driver-pedestrian interactions should be incorporated in future modeling efforts which may, ultimately, support design of automated systems that are able to interact more safely, efficiently, and socially.

Modeling the Dynamic Exclusive Pedestrian Phase Based on Transportation Equity and Cost Analysis.

The exclusive pedestrian phase (EPP) has proven to be an effective method of eliminating pedestrian–vehicle conflicts at signalized intersections. The existing EPP setting conditions take traffic efficiency and safety as optimization goals, which may contribute to unfair interactions between vehicles and pedestrians. This study develops a multiobjective optimization framework to determine the EPP setting criteria, with consideration for the tradeoff between transportation equity and cost. In transportation equity modeling and considering environmental conditions, the transportation equity index is proposed to quantify pedestrian–vehicle equity differences. In cost modeling, traffic safety and efficiency factors are converted into monetary values, and the pedestrian–vehicle interaction is introduced. To validate the proposed optimization framework, a video-based data collection is conducted on wet and dry environment conditions at the selected intersection. The parameters in the proposed model are calibrated based on the results of the video analysis. This study compares the performance of the multiobjective evolutionary algorithm based on decomposition (MOEA) and the nondominated sorting genetic algorithm II (NSGA-II) methods in building the sets of nondominated solutions. The optimization results show that the decrease in transportation equity will lead to an increase in cost. The obtained Pareto front approximations correspond to diverse signal timing patterns and achieve a balance between optimizing either objective to different extents. The sensitivity analysis reveals the application domains for the EPP and the traditional two-way control phase (TWC) under different vehicular/pedestrian demand, yielding rate, and environment conditions. The EPP control is more suitable at intersections with high pedestrian volumes and low yielding rates, especially in wet conditions. The results provide operational guidelines for decision-makers for properly selecting the pedestrian phase pattern at signalized intersections.

Evaluating the influence of approaching vehicles on pedestrian’s visual patterns and crossing behaviors at an uncontrolled crosswalk

The visual behaviors and movement characteristics of pedestrians are related to their surrounding potential safety hazards, such as approaching vehicles. This study primarily aimed to investigate the visual patterns and walking behaviors of pedestrians interacting with approaching vehicles. Field experiments were conducted at two uncontrolled crosswalks located at the Cuihua and Yanta roads in Xi’an, China. The visual performance of pedestrians was assessed using the eye tracking system from SensoMotoric Instruments (SMI). Moreover, motion trajectories of the pedestrians and approaching vehicles were obtained using an unmanned aerial vehicle. Subsequently, the visual attributes and movement trajectories of pedestrians and motion trajectories of approaching vehicles were statistically analyzed. The results showed that approaching vehicles distracted the fixation of crossing pedestrians significantly, and occupied 29.5% of the total duration of fixation; that is, pedestrians always directed more fixation points to the approaching vehicles compared to other stimuli. As a vehicle approached, pedestrians’ fixation shifted from other areas of interest to the vehicle. Moreover, an increase in the velocity of the vehicle and a closer distance between pedestrian and the vehicle resulted in an increase in the pedestrians’ duration of fixation on the approaching vehicle, and they implemented more saccades. Furthermore, approaching vehicle’s velocity and distance between pedestrian and approaching vehicle are not significantly associated with pedestrian’s movement attributes. These findings provide insights into the crossing behavior of pedestrians during pedestrian-vehicle interactions, which could assist future researchers and policy makers.

A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections

Investigating pedestrian crossing and driver yielding decisions should be an important focus considering the high risks of pedestrians in exposed to motorized traffic. Limitations, however, exist in previous studies – variables considered previously have been limited; how their behavior affect each other (defined as interactive impacts) were not sufficiently considered. This paper aims to provide a methodological approach for pedestrian crossing and driver yielding decisions during their interactions, considering of different variable types including interactive impact variables, traffic condition variables, road design variables, and environment variables. A Distance-Velocity (DV) framework proposed in an earlier study is introduced for definitions and concepts in studying pedestrian-vehicle interactions. Logistic regression, support vector machines, neural networks and random forests, are introduced as candidate models. A case study involving six crosswalk locations is conducted, focusing on interactions between pedestrians and right-turn vehicles. The proposed methodological approach is applied, with the performance of the four machine learning methods compared in terms of model generalization and confusion matrix. The model with the best performance is further compared to the typical gap-based model. Results show that random forest and logistic regression models performed the best in modeling pedestrian crossing and driver yielding decisions respective, in terms of model generalization. Besides, the DV-based modeling method (average accuracy of over 90% for pedestrians and 80% for drivers) outperformed the traditional gap-based method in all test seeds. As a key finding, interactive impacts from each other (the pedestrian and the driver) act as a key contributing variable on their decisions.

Evaluating the safety of autonomous vehicle–pedestrian interactions: An extreme value theory approach

With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on the Extreme Value Theory (EVT), with the peak over threshold modeling technique, to develop an estimate of AV-pedestrian collisions using AV-pedestrian conflicts. The proposed framework was applied to two AV datasets, collected from three locations in the US and Singapore, using the operating AV fleets of two developers, Motional and Lyft. Both datasets included trajectory data for the subject AV, as well as LiDAR point clouds and annotated video data from AV cameras to capture the trajectories of surrounding road users. The datasets were processed to extract the AV-pedestrian conflicts along with the corresponding conflict indicators, mainly the post-encroachment time (PET) and time-to-collision (TTC). Relevant covariates were introduced to the proposed models to enhance their performance and prediction accuracy, including turning movements and conflict speeds. The results indicate an alarming risk to pedestrians when interacting with AVs, especially at the early stages of AV adoption. The expected number of collisions ranged from 4 to 5.5 per million vehicle kilometers travelled (VKT) of the AVs. With the addition of the covariates, the expected number of collisions went down to a range of 2.3–3.7 per million VKT, but with a narrower confidence interval of the resulting estimate and a better fit. The introduced approach shows promising prospects for the application of EVT methods to address AV safety impacts. It also invites future applications to address issues of concern for pedestrian safety in different conditions of urban traffic.

Modeling pedestrian–vehicle mixed-flow in a complex evacuation scenario

Traffic simulation model has been widely used in urban traffic evacuation and pedestrian evacuation. However, few research efforts have been devoted to a complex evacuation scenario that includes vehicles and pedestrians. Even some of the research studies have focused on pedestrian–vehicle mixed-flow simulation models; they can hardly simulate the pedestrian–vehicle interaction dynamics in complex evacuation scenarios such as intersections, parking lots, and so on. This paper developed a pedestrian–vehicle mixed-flow model based on the Social Force Model (SFM), a car-following model, and a lane change model to solve this problem. Further, the vehicle–pedestrian force and Time to Conflict (TTC) model the interaction between people and vehicles. In the case study of parking lot evacuation, the model is proved to be effective in simulating the behavior of pedestrian–vehicle flow in a complex scenario.