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.

Cyber-physical Modeling and Control of Crowd of Pedestrians:A Review and New Framework

Recent advances in modeling and control of crowd of pedestrians are briefly surveyed in this paper.Possibilities of applying fractional calculus in the modeling of crowd of pedestrians have been shortly reviewed and discussed from different aspects such as descriptions of motion,interactions of long range and effects of memory.Control of the crowd of pedestrians have also been formulated using the framework of cyber-physical systems and been realized using networked Segways with onboard emergency response personnels to regulate the velocity and flux of the crowd.Platform for verification of the theoretical results are also provided in this paper.

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.

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.

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.

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.

A Study on the Methods for Estimating the Distribution of Pedestrians in an Underground Mall by Use of Watch Cameras

This paper shows the method of estimating spatiotemporal distribution of pedestrians by using watch cameras. We estimate the distribution without tracking technology, with pedestrian＇s privacy protected and in Umeda underground mall. Lately spatiotemporal distribution of pedestrians has being increasingly important in the field of urban planning, disaster prevention planning, marketing and so on. Although many researchers have tried to capture the information of location as dealing with some sensors, some problems still remain, such as the investment of sensors, the restriction of the number of people who has the device they are able to capture. From such background, we develop an original labelling algorithm and estimate the spatiotemporal distribution of pedestrians and the information of the passing time and the direction of pedestrians from sequential images of a watch camera.

Temporal and spatial compliance behaviour of pedestrians under the influence of time pressure at signalized intersections:A pedestrian simulator study

Pedestrian safety is at high stakes due to the non-compliance practices of pedestrians at signalized intersections.Additionally,when pedestrians are hurrying,they deliberately engage in such unsafe behaviour.Therefore,the purpose of this study was to understand how time pressure(i.e.,feeling of hurry or saving time)affected pedestrians'decisions to follow traffic rules at signalized junctions.To achieve the study objectives,a pedestrian simulator setup was used to collect the crossing behaviour of forty participants at a four-legged signalized intersection.Non-compliance,one of the riskiest pedestrian behaviours,was examined with respect to three different forms,comprising dangerous temporal non-compliance(D-TNC),non-dangerous temporal non-compliance(ND-TNC),and spatial non-compliance(SNC)behaviour under two distinct conditions:baseline(i.e.,no time pressure)and time pressure conditions.The effects of demographics,usual walking features,and time pressure on D-TNC and ND-TNC were investigated using a multinomial regression model,while SNC behaviour was investigated using a binary regression model.It was interesting to note that the majority of the factors related to pedestrians’usual walking behaviour had an impact on all kinds of non-compliance behaviours.Importantly,the results also showcased that time pressure had a contrasting impact on D-TNC and ND-TNC behaviour whereas SNC behaviour increased under time pressure.Additionally,the varying impacts of D-TNC,ND-TNC,and SNC were also reflected in the occurrence of the crashes,which were probably triggered by discrepancies in the influence of time pressure on non-compliance behaviours.These findings highlight the need for technical solutions,educational outreach,and efficient enforcement practices to reduce pedestrians'non-compliant behaviour.

IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection

Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents.To tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small targets.Second,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category imbalance.Third,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection accuracy.The test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only 9.1.In contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource overhead.On the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the GFLOPs.The improved algorithm has shown significant advancements in balancing accuracy and computational efficiency,making it promising for practical use in resource-limited scenarios.

A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

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.

An extended social force model on unidirectional flow considering psychological and behavioral impacts of hazard source

An accurate assessment of the evacuation efficiency in case of disasters is of vital importance to the safety design of buildings and street blocks.Hazard sources not only physically but psychologically affect the pedestrians,which may further alter their behavioral patterns.This effect is especially significant in narrow spaces,such as corridors and alleys.This study aims to integrate a non-spreading hazard source into the social force model following the results from a previous experiment and simulation,and to simulate unidirectional pedestrian flows over various crowd densities and clarity–intensity properties of the hazard source.The integration include a virtual repulsion force from the hazard source and a decay on the social force term.The simulations reveal(i)that the hazard source creates virtual bottlenecks that suppress the flow,(ii)that the inter-pedestrian push forms a stabilisation phase on the flow-density curve within medium-to-high densities,and(iii)that the pedestrians are prone to a less orderly and stable pattern of movement in low clarity–intensity scenarios,possibly with lateral collisions passing the hazard source.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Method of improving pedestrian navigation performance based on chest card

With the development of positioning technology,loca-tion services are constantly in demand by people.As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation.The pedestrian naviga-tion based on radio is subject to environmental occlusion lead-ing to the degradation of positioning accuracy.The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit(MIMU)is less susceptible to environmental interference,but its errors dissipate over time.In this paper,a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods.To suppress atti-tude errors,optimal feedback coefficients are established by pedestrian motion characteristics.To extend navigation time and improve positioning accuracy,the step length in subsequent movements is compensated by the first step length.The experi-mental results show that the positioning accuracy of the pro-posed method is improved by more than 47%and 44%com-pared with the pure inertia-based method combined with step compensation and the traditional complementary filtering com-bined method with step compensation.The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.

Lightweight Cross-Modal Multispectral Pedestrian Detection Based on Spatial Reweighted Attention Mechanism

Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.

Measuring Pedestrian Stress Response (MPSR) Using Wearable Technologies

Walkability is an essential aspect of urban transportation systems. Properly designed walking paths can enhance transportation safety, encourage pedestrian activity, and improve community quality of life. This, in turn, can help achieve sustainable development goals in urban areas. This pilot study uses wearable technology data to present a new method for measuring pedestrian stress in urban environments and the results were presented as an interactive geographic information system map to support risk-informed decision-making. The approach involves analyzing data from wearable devices using heart rate variability (RMSSD and slope analysis) to identify high-stress locations. This data-driven approach can help urban planners and safety experts identify and address pedestrian stressors, ultimately creating safer, more walkable cities. The study addresses a significant challenge in pedestrian safety by providing insights into factors and locations that trigger stress in pedestrians. During the pilot study, high-stress pedestrian experiences were identified due to issues like pedestrian-scooter interaction on pedestrian paths, pedestrian behavior around high foot traffic areas, and poor visibility at pedestrian crossings due to inadequate lighting.

Hit-and-runs more common with pedestrians lying on the road: Analysis of a nationwide database in Japan

Purpose:To determine the trends with fatally or otherwise injured pedestrians lying on the road and the relationship to hit-and-run incidents in Japan.Methods:We extracted data for 2012e2016 from the records of the Institute for Traffic Accident Research and Data Analysis,Japan,a nationwide traffic accident database.All the injured and fatally injured pedestrians were selected.We examined the levels of pedestrian injury,vehicle speed immediately before the collision,whether or not the pedestrian was lying on the road,and hit-and-run incidents.Chi-square test was employed to make a statistical comparison between the two groups.Results:The database contained data on 286,383 pedestrian casualties and 7256 fatalities;8.3%of fatalities(602 persons)and 0.6%of casualties(1827 persons)involved pedestrians lying on the road.The rates of fatalities and severe injuries were significantly higher for pedestrians who were lying on the road than for those who were not.Hit-and-run incidents were evident in 4.0%of casualties and 7.3%of fatalities.The rate of hit-and-run cases was also significantly higher among pedestrians who were lying on the road.Among fatally injured pedestrians not lying on the road,the rates with speeds of30 km/h did not differ significantly between hit-and-run and other cases.However,when the pedestrians were lying on the road,the rate was significantly increased in hit-and-run cases.Conclusion:This is the first report to focus on pedestrians lying on the road and being involved in hitand-run incidents.In addition to preventing hit-and-run incidents,prevention of pedestrians lying on the road could also decrease fatalities.