Recognition of occluded pedestrians from the driver's perspective for extending sight distance and ensuring driving safety at signal-free intersections

Urban intersections without traffic signals are prone to accidents involving motor vehicles and pedestrians.Utilizing computer vision technology to detect pedestrians crossing the street can effectively mitigate the occurrence of such accidents.Faced with the complex issue of pedestrian occlusion at signal-free intersections,this paper proposes a target detection model called Head feature And ENMS fusion Residual connection For CNN(HAERC).Specifically,the model includes a head feature module that detects occluded pedestrians by integrating their head features with the overall target.Additionally,to address the misselection caused by overlapping candidate boxes in two-stage target detection models,an Extended Non-Maximum Suppression classifier(ENMS)with expanded IoU thresholds is proposed.Finally,leveraging the CityPersons dataset and categorizing it into four classes based on occlusion levels(heavy,reasonable,partial,bare),the HAERC model is experimented on these classes and compared with baseline models.Experimental results demonstrate that HAERC achieves superior False Positives Per Image(FPPI)values of 46.64%,9.59%,9.43%,and 6.78%respectively for the four classes,outperforming all baseline models.The study concludes that the HAERC model effectively identifies occluded pedestrians in the complex environment of urban intersections without traffic signals,thereby enhancing safety for long-range driving at such intersections.

Pedestrian lane formation with following–overtaking model and measurement of system order

Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.

Hybrid pedestrian positioning system using wearable inertial sensors and ultrasonic ranging

Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.

Virtual Reconstruction of Long Bone Fracture in Car-to-pedestrian Collisions Using Multi-body System and Finite Element Method

Lower limb injures are frequently observed in passenger car traffic accidents.Previous studies of the injuries focus on long bone fractures by using either cadaver component tests or simulations of the long bone kinematics,which lack in-depth study on the fractures in stress analysis.This paper aims to investigate lower limb impact biomechanics in real-world car to pedestrian accidents and to predict fractures of long bones in term of stress parameter for femur,tibia,and fibula.For the above purposes,a 3D finite element(FE) model of human body lower limb(HBM-LL) is developed based on human anatomy.The model consists of the pelvis,femur,tibia,fibula,patella,foot bones,primary tendons,knee joint capsule,meniscus,and ligaments.The FE model is validated by comparing the results from a lateral impact between simulations and tests with cadaver lower limb specimens.Two real-world accidents are selected from an in-depth accident database with detailed information about the accident scene,car impact speed,damage to the car,and pedestrian injuries.Multi-body system(MBS) models are used to reconstruct the kinematics of the pedestrians in the two accidents and the impact conditions are calculated for initial impact velocity and orientations of the car and pedestrian during the collision.The FE model is used to perform injury reconstructions and predict the fractures by using physical parameters,such as von Mises stress of long bones.The calculated failure level of the long bones is correlated with the injury outcomes observed from the two accident cases.The reconstruction result shows that the HBM-LL FE model has acceptable biofidelity and can be applied to predict the risk of long bone fractures.This study provides an efficient methodology to investigate the long bone fracture suffered from vehicle traffic collisions.

Analysis of conflict factors between pedestrians and right-turning vehicles at signalized intersections

Due to the fact that there is no protected signal phase for right turns at most signalized intersections, the conflict between pedestrians and right-turning vehicles is one of the most common conflict types for pedestrians. A pedestrian safety analysis of the common right-turn mode at four-phase signalized intersections is presented. Relative risk is used as a measure of the effect of behaviors. The analysis mainly includes five pedestrian factors that affect the conflict process between pedestrians and right-turning vehicles. Pedestrians tend to have a higher risk of being involved in conflicts in the following six situations: crossing with others, running over the crossing, entering the intersection, being near the exit lane, crossing in the middle or at the end of a green light when the right-turn lane is shared, crossing at the beginning of a green light or red period when the right-turn lane is exclusive. It is easier for pedestrians to get priority when crossing the street in the following situations: running over a crossing, entering the intersection, being near the entrance lane, and not using the crosswalk. However, pedestrians are more inclined to yield to right-turning vehicles when pedestrians are crossing in the middle of the green light time. Some measures to alleviate the conflict are put forward according to the conclusion. Video observations also indicate that a clear pedestrian waiting area must be marked for both pedestrian safety and right-turning vehicle efficiency at major flat intersections, particularly when the arms cover the lateral dividing strips.

Evacuation of pedestrians from a hall by game strategy update

In this paper, a cellular automaton model considering game strategy update is proposed to study the pedestrian evac- uation in a hall. Pedestrians are classified into two categories, i.e., cooperators and defectors, and they walk to an exit according to their own strategy change. The conflicts that two or three pedestrians try to occupy the same site at the same time are investigated in the Game theory model. Based on it, the relationship between the pedestrian flow rate and the evacuation time as well as the variation of cooperative proportion against evacuation time is investigated from the different initial cooperative proportions under the influence of noise. The critical value of the noise is found when there is a small number of defectors in the initial time. Moreover, the influences of the initial cooperative proportion and strength of noise on evacuation are discussed. The results show that the lower the initial cooperative proportion as well as the bigger the strength of noise, the longer the time it takes for evacuation.

Research on Open Block's Pedestrian System Based on the EnvironmentBehavior Theory: A Case Study of Shenzhen Oct-loft

In the context of banning gated communities, blocks returning to the human-oriented scale become the new normal, and pedestrian system design will be paid more attention in the urban planning field. Oct-Loft Creative Park is a template for open blocks in Shenzhen, with a convenient and humanized pedestrian system. This paper selects the creative park's pedestrian system as the research object, using the environment-behavior theory for analysis. Finally, optimization strategies of pedestrian system will be put forward.

A robust system for real-time pedestrian detection and tracking

A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.

Simulation-based optimization of inner layout of a theater considering the effect of pedestrians

We propose an extended cellular automaton model based on the floor field. The floor field can be changed accordingly in the presence of pedestrians. Furthermore, the effects of pedestrians with different speeds are distinguished, i.e., still pedestrians result in more increment of the floor field than moving ones. The improved floor field reflects impact of pedestrians as movable obstacles on evacuation process. The presented model was calibrated by comparing with previous studies. It is shown that this model provides a better description of crowd evacuation both qualitatively and quantitatively.Then we investigated crowd evacuation from a middle-size theater. Four possible designs of aisles in the theater are studied and one of them is the actual design in reality. Numerical simulation shows that the actual design of the theater is reasonable.Then we optimize the position of the side exit in order to reduce the evacuation time. It is shown that the utilization of the two exits at bottom is less than that of the side exits. When the position of the side exit is shifted upwards by about 1.6 m,it is found that the evacuation time reaches its minimum.

Multi-Dimensional Pedestrian System on the New Campus of University of Macao

The Multi-dimensional Pedestrian System( MPS) is an integral part of the new campus of University of Macao. It observes the principle of "pedestrian first " and features a pedestrian-vehicle dividing system.Based on a comprehensive analysis of the location,climate,ecology and other factors of the project site,the conception of the idea of MPS and the related researches are illustrated. The transportation features of the MPS,as summarized,include multi-dimensions,short-distance and weather-resistance. Its features for the sake of livability include integration of nature, respect for the environment and sharing of landscape. Upon the completion of the project, the effects on its users were tested. Finally, some constructive rules for the construction of similar campus pedestrian systems were proposed.

Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

Pedestrian detection and tracking are vital elements of today’s surveillance systems,which make daily life safe for humans.Thus,human detection and visualization have become essential inventions in the field of computer vision.Hence,developing a surveillance system with multiple object recognition and tracking,especially in low light and night-time,is still challenging.Therefore,we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at night.In particular,we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared(IR)images using machine learning and tracking them using particle filters.Moreover,a random forest classifier is adopted for image segmentation to identify pedestrians in an image.The result of detection is investigated by particle filter to solve pedestrian tracking.Through the extensive experiment,our system shows 93%segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof classes.Moreover,the system achieved a detection accuracy of 90%usingmultiple templatematching techniques and 81%accuracy for pedestrian tracking.Furthermore,our system can identify that the detected object is a human.Hence,our system provided the best results compared to the state-ofart systems,which proves the effectiveness of the techniques used for image segmentation,classification,and tracking.The presented method is applicable for human detection/tracking,crowd analysis,and monitoring pedestrians in IR video surveillance.

A novel method for tracking pedestrians from real-time video

This novel method of Pedestrian Tracking using Support Vector (PTSV) proposed for a video surveillance instrument combines the Support Vector Machine (SVM) classifier into an optic-flow based tracker. The traditional method using optical flow tracks objects by minimizing an intensity difference function between successive frames, while PTSV tracks objects by maximizing the SVM classification score. As the SVM classifier for object and non-object is pre-trained, there is need only to classify an image block as object or non-ob-ject without having to compare the pixel region of the tracked object in the previous frame. To account for large motions between successive frames we build pyramids from the support vectors and use a coarse-to-fine scan in the classification stage. To accelerate the training of SVM, a Sequential Minimal Optimization Method (SMO) is adopted. The results of using a kernel-PTSV for pedestrian tracking from real time video are shown at the end. Comparative experimental results showed that PTSV improves the reliability of tracking compared to that of traditional tracking method using optical flow.

Tracking Pedestrians Under Occlusion in Parking Space

Many traffic accidents occur in parking lots.One of the serious safety risks is vehicle-pedestrian conflict.Moreover,with the increasing development of automatic driving and parking technology,parking safety has received significant attention from vehicle safety analysts.However,pedestrian protection in parking lots still faces many challenges.For example,the physical structure of a parking lot may be complex,and dead corners would occur when the vehicle density is high.These lead to pedestrians’sudden appearance in the vehicle’s path from an unexpected position,resulting in collision accidents in the parking lot.We advocate that besides vehicular sensing data,high-precision digital map of the parking lot,pedestrians’smart device’s sensing data,and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot.However,this subject has not been studied and explored in existing studies.Tofill this void,this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces.We also evaluate the proposed method through real-world experiments.The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy.It can also be used for pedestrian tracking in parking spaces.

A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

The COVID-19 virus is usually spread by small droplets when talking,coughing and sneezing,so maintaining physical distance between people is necessary to slow the spread of the virus.The World Health Organization(WHO)recommends maintaining a social distance of at least six feet.In this paper,we developed a real-time pedestrian social distance risk alert system for COVID-19,whichmonitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge,thus avoiding the problem of too close social distance between pedestrians in public places.We design a lightweight convolutional neural network architecture to detect the distance between people more accurately.In addition,due to the limitation of camera placement,the previous algorithm based on flat view is not applicable to the social distance calculation for cameras,so we designed and developed a perspective conversion module to reduce the image in the video to a bird’s eye view,which can avoid the error caused by the elevation view and thus provide accurate risk indication to the user.We selected images containing only person labels in theCOCO2017 dataset to train our networkmodel.The experimental results show that our network model achieves 82.3%detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall,Precision and mAP,proving the effectiveness of our system and the efficiency of our technology.

VIDEO BASED ESTIMATION OF PEDESTRIAN WALKING DIRECTION FOR PEDESTRIAN PROTECTION SYSTEM

Pedestrian protection has played an important role for driver assistance systems.Our aim is to develop a video based driver assistance system for the detection of the potentially dangerous situation between the vehicle and pedestrian,in order to warn the driver.In this paper,we address the problem of detecting pedestrian in real-world scenes and estimation of the walking direction with a single camera from a moving vehicle.Considering all the available cues for predicting the possibility of collision is very important.The direction in which the pedestrian is facing is one of the most important cues predicting where the pedestrian may move in the future.So we first address the problem of sin-gle-frame pedestrian orientation estimation in real-world scenes.Then again,we estimate the pedes-trian walking direction using multi-frame based on the result of single-frame orientation estimation.We propose a three-step method:pedestrian detection for single-frame step,orientation estimation for single-frame step and walking direction estimation for multi-frame step.To evaluate the proposed method in its robustness and accuracy,the experiments have been performed between numbers of images which is highly challenging uncontrolled conditions in real world.It shows a significant per-formance improvement in octant orientation estimation of about 64% accuracy in the orientation es-timation step and achieved surprisingly good accuracy in estimating the walking direction against 212 targeted objects.

Reviving the Historic Street through Pedestriansation: In the Case of Haileselassie Street, Piazza, Addis Ababa

Pedestrianisation is seen as a necessity in many cities of the world.Streets are main representatives of the city image,which have its reflection of its home country.Haileselassie Street in Piazza is shaped after the short-term confrontations of the Italians that profoundly affected the entire downtown.The prideful victory of Ethiopia is an important landmark of the urban fabric in Piazza,Addis Ababa.The Haileselassie Street lacks its vista and approach it deserves.Therefore,this paper introduces the scheme of pedestrianisation in Haileselassie Street by reclaiming the street for the people in order to remember the history.The pertinent questions this article seeks to address are:the factors that aim to transform the street into a‘pedestrian’street only?Which aspects of the pedestrianisation should be considered to improve the quality of the Haileselassie Street?Moreover,this article recommends the strategic proposal to improve the quality of the pedestrians in urban space.

Estimation of Probability of Swarming Pedestrians' Violation at Signalized Intersections in Developing Cities

We made an on-site investigation about pedestrian violation of traffic signals at a signalized intersection in Xi＇an, Shaanxi province, China. Based on it, we studied the impact of pedestrian＇s waiting time on violation decision and the impact of the number of pedestrians in colony on the probability of swarming pedestrians＇ violation. The result revealed that the probability of pedestrian violation rose with the waiting time for the pedestrians＇ green signal. Then we developed a Monte Carlo model for simulating mixed vehicles and pedestrians and used the on-site investigation data to validate the model. When traffic volume is fight, the error between the simulated values and the measured ones is 2.67%. When traffic volume is heavy, the error is 3.38%.

Self-Perception and General Perception of the Safety Impact of Autonomous Vehicles on Pedestrians, Bicyclists, and People with Ambulatory Disability

For autonomous vehicles (AVs) to receive general acceptance, society must have a positive perception about their safety impact on vulnerable road users. Using data from a statewide random-digit-dialing telephone survey of 1001 adults, this paper examines how New Jersey residents perceive the safety impact of AVs on pedestrians, bicyclists, and people with ambulatory disability. It uses a combination of confirmatory factor analysis and ordered probit models. Confirmatory factor analysis is used to create latent variables on socioeconomic status and built environment. Three ordered probit models are used to examine people’s perception of AV safety impact on each of the three population groups. The models also examine how frequent walkers, bicyclists, and people with ambulatory disability perceive their own safety as well as the safety of the other two groups. All three models examine the effect of familiarity with AV, gender, age, income, education, race, ethnicity, number of vehicles in household, political party affiliation, as well as built environment and socioeconomic status of the municipalities where the survey respondents live. The analysis showed that men, people with familiarity with the AV concept, Democrats, bicyclists, and people with high household income generally have a positive perception about the safety impact of AVs. While frequent walkers are ambivalent about their own safety as pedestrians, bicyclists have a positive perception about their own safety and the safety of pedestrians, whereas people with ambulatory disability have a strong negative perception about their own safety. The models did not show statistically significant effects of socioeconomic status or built environment of municipalities on AV safety perception.

Investigation of Braking Timing of Drivers for Design of Pedestrian Collision Avoidance System

The braking behavior of drivers when a pedestrian comes out from the sidewalk to the road was analyzed using a driving simulator. Based on drivers＇ braking behavior, the braking control timing of the system for avoiding the collision with pedestrians was proposed. In this study, the subject drivers started braking at almost the same time in terms of TTC （Time to Collision）, regardless of the velocity of a subject vehicle and crossing velocity of pedestrians. This experimental result showed that brake timing of the system which can minimize the interference for braking between drivers and the system is 1.3 s of TTC. Next, the drivers＇ braking behavior was investigated when the system controlled braking to avoid collision at this timing. As a result, drivers did not show any change of braking behavior with no excessive interference between braking control by the system and braking operation by drivers for avoiding collisions with pedestrians which is equivalent to the excessive dependence on the system.

Designing Right-Turn Vehicle Box as a Supplemental Treatment to Eliminate Conflicts with Pedestrians and Bicycles

Field observations illustrated that, right-turn vehicles stopped at various positions when proceeding within the right-turn lanes, while some of them trespassed on the crosswalks with multiple stops. In this case, pedestrians and bikes (ped/bike) are encountered unsmooth and hazardous crossings when right-turn vehicles encroaching their lanes. Meanwhile, this also causes conflicts between right-turn and through vehicles at the crossing street. To better protect ped/bike at crossings with right-turn vehicles, this paper proposes a concept of “right-turn vehicle box” (RTVB) as a supplemental treatment within right-turn lanes. Sight distance, geometric conditions, and behaviors of vehicles and ped/bike are key factors to consider so as to set up the criteria and to design the suitable treatment. A case study was conducted at an intersection pair in Houston, USA to shape the idea of RTVB, together with driving simulator tests under relevant scenarios. The preliminary crosscheck examination shows that the right-turn vehicle box could possibly provide ped/ bike with smoother and safer crossings. In the interim, the safety and efficiency of right-turn operations were also improved. To further validate the effects, implementation studies should be conducted before the RTVB can make its debut in practice. Future works will focus on the complete warrants and design details of this treatment. Moreover, the concept of “vehicle box” could also be transplanted to other places where turning movement(s) needs assistance or improvements.