Real-Time Safe Route Finding and Visualization Using Shared Edge Devices Takenori Hara1* , Hideo Saito1 1 Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan Abstract Conventional safe route-finding systems display routes on a map to avoid dangerous areas based on offline data such as statistical data on crime rates. However, such dangerous level changes in a dynamic manner, which can be detected by people walking around. This paper presents a system that shares the detected data measured by edge devices owned by walking people for dynamic safe route finding in real-time. This system evaluates the safety of routes in real time using and displays the route in AR on HMDs and smartphones. This paper reports findings from a simulation and edge device (smartphone) implementation of spatial safety assessment, safe route finding, and route visualization. Keywords 1 Safe Routing, Edge Device, Visualization, AR 1. Introduction outdoors and 30% of cases occur in a house (Figure 2). A higher percentage of assaults occur 1.1. Research Background on public transportation or in stores than assaults, and we often hear news stories of station staff, Violent crimes such as assault and injury shopkeepers, passersby, and passengers suddenly (Assault is defined in Japan as a crime in which being violently attacked. Although transportation the victim was not injured, while an injury is a and shopping are essential to daily life, the crime in which the victim was injured) are crimes possibility of sudden violence and crime prevents that occur close to people, have a significant people from leading their daily lives with peace of impact on the victim's mind and body, threaten mind. their peaceful daily life, and may increase social unrest. According to a study on violent crime conducted by the Ministry of Justice's Legal Research Institute in Japan[1], the number of assault cases has remained high at around 30,000 since 2006, and the number of injury cases has also remained in the 20,000s since 2008 (Figure 1). In addition, these are the number of crimes reported to the police; the actual number of assaults and injuries is expected to be much higher. The largest number of assault cases was from people who did not know each other, while the Figure 1: Number of assaults and injuries in number of injury cases was from people who Japan knew each other, as typified by domestic violence. About 30% of both assault and injury cases occur APMAR’22: The 14th Asia-PacificWorkshop on Mixed and Augmented Reality, Dec. 02-03, 2022, Yokohama, Japan *Corresponding author. EMAIL: gouki@hvrl.ics.keio.ac.jp (T.Hara); hs@keio.jp (H. Saito); ORCID: 0000-0002-7699-0376 (T.Hara); 0000-0002-2421-9862 (H.Saito); © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Wor Pr ks hop oceedi ngs ht I tp: // ceur - SSN1613- ws .or 0073 g CEUR Workshop Proceedings (CEUR-WS.org) Sukru et al. propose a mobile app for the social distancing of people in a COVID-19 environment. It allows each user to manually register their health status and then share their current location on the network in real-time, predicting the Figure 2: Location of injury and assault in Japan walking paths of other users and alerting them with sound and vibration to the possibility of approaching users with poor health status [4]. Maria et al. proposed a method that uses BLE 1.2. Research Objectives beacons to detect and share a user's location and provide a route to reach a destination without Conventional navigation systems are designed passing other people indoors [5]. To successfully to arrive at a destination smoothly and quickly. To avoid people, robot pathfinding methods can be achieve a safe and secure society, we research, helpful: Kapil et al. proposed a pathfinding develop, and implement navigation systems that method that predicts the movement path of a can avoid crimes and unpleasant events by group of people [6], and Lucia et al. proposed a detecting them in advance. The navigation system method to move around humans and obstacles in uses edge devices such as surveillance cameras a social compatible and safe manner [7]. and smartphones to measure the level of danger in As a mechanism for sharing information real-time and search for safe routes. The system sensed by the user, the Joint Tactical Information also visualizes the detected dangerous elements Distribution System (JTIDS) and the Inter- and the safe routes. vehicular Information System (IVIS), which are We are convinced that this system can used in military applications, can be used as contribute to solving various social problems. For references. These systems share the position, example, the system can be used for urban heading, altitude, and speed of detected aircraft, planning to prevent crimes, support services to ships, and vehicles with all platforms participating maintain social distance in infectious disease in the network. environments such as COVID-19, detect people in For safe route finding, it is necessary to trouble and automatically ask for help from evaluate the safety of a location. According to people around them, support services to protect Stacy et al., a person's gender, facial expression, wild animals, and services to find lost cats. gaze, appearance, clothing, attitude, and physique In this paper, we report on the findings and are known to help determine whether a person is problems encountered in simulating the level of suspicious [8]. For example, if a person is silent danger assessment, safe route search, and safe and stares at another person, has an aggressive or route visualization of a location using edge predatory facial expression or body language, or devices. We also report our findings and problems is carrying a weapon such as a gun or a knife, he in a prototype system using a smartphone. or she is highly dangerous. Fan [9] and Fang [10] et al. proposed a method for detecting abnormal 1.3. Related Works human behavior in real-time from video images, and it is also provided as a service, such as the Many methods have been proposed for safe Incident Detection Solution [11]. Salamon et al. route finding that minimizes the level of danger proposed a method for sound classification [12], and distance on the path and displays the path on we think this method evaluates the safety of a a map. location by analyzing sounds such as Galbrun et al. proposed a safe route search conversations or detecting gunshots or explosions. method based on Total-Paths (total level of danger Kim et al. proposed a method to estimate the level on a path) and Max-Paths (maximum level of of danger of a location based on the content of danger on a path) [2]. Yizhou et al. conducted a geotagged messages on social media [13]. safe route search simulation experiment on an RedZone Map [14], a service that guides users to actual congestion dataset for each Paris train safe routes for cars based on government-released station based on another research result that crime map data; My Safety pin[15], a navigation "higher congestion leads to higher crime rate" and application that uses the results of interviews with confirmed the effectiveness of the method [3]. users about the safety of each location; CrimeReports[16], which works with police information systems to visualize crimes that have occurred in; My Safe Map[17], a map service that 2. Study of a navigation system that uses existing external data to search for safe streets and blocks, have also been put to practical can detect and avoid criminal use. Many methods have been proposed to damage and unpleasant events in evaluate the safety of a location, such as those in advance these previous studies. However, no method has been established that runs on an edge device and evaluates the safety of a location with sufficient accuracy. The freshness, spatial resolution, and accuracy 2.1. System Overview of the original data are important for assessing the level of danger. Ideally, the data should be We have conducted a system study to achieve updated in real-time and evaluated by an impartial a navigation system that can detect and avoid third party with a spatial resolution of about 3.5 m criminal damage and unpleasant events in [18], which is defined by Edward et al. as a social advance. This system consists of six subsystems distance that is hard to reach but easy to talk to. as shown in Figure 3. However, most of the aforementioned studies and services use statistical data such as crime maps, which are not real-time, have a low spatial resolution, and have low accuracy because they Safty are self-reported. And most of the existing services that detect abnormal behavior in real- time from video images are not available because (1) Edge Devices (2) Location danger level (3) Wide-area safe route they do not disclose the detection results. evaluation system search system Therefore, conventional navigation services are limited to route guidance to avoid areas that are statistically known to be dangerous. No safe route-finding method has been proposed to successfully pass by dangerous people in a narrow (4) Narrow-area safe route (5) Safe route (6) Data sharing server space while maintaining social distance from search system visualization system them. As for visualization, the system is limited to Figure 3: System Overview displaying routes on a 2D map and alerting drivers with sound and vibration. (1)Edge devices Therefore, we are conducting research, A device equipped with various sensors such development, and social implementation of an as a camera, microphone, thermo, GPS, etc. We information-sharing system and navigation assume surveillance cameras, smartphones, and system that can detect and avoid criminal damage wearable devices. and unpleasant events in advance, featuring the following three elements. (2) Location danger level evaluation system The system evaluates and shares the safety of (1) Edge devices accurately measure and share the a location based on information acquired by level of danger of a location in real-time. various sensors in edge devices. We can use other information such as sound (conversations, (2) A wide-area safe route-finding method based gunshots, explosions) or temperature (human on the level of danger measured by edge devices, body, road surface, fire, freezing) to evaluate the and a narrow-area safe route-finding method for safety of the location. This information can be successfully passing dangerous people in a used to set various conditions in addition to safety, narrow space while maintaining social distance. such as easy walking routes, cool routes, and routes with many pedestrians. (3) Social Distance and safe route visualization and human guidance methods (3) Wide-area safe route search system This system performs a wide-area route search to maximize safety and minimize distance based on the safety level and distance of the location evaluated by the edge device. The location and attributes of PFs, PDs, and PSs (4) Narrow-area safe route search system detected by the device and their measured level of This system searches for a safe route in a danger are sent to the data-sharing server as a narrow space (e.g., a street or a station) to pass by Danger Point (DP). Although the DP is quantified a dangerous person while maintaining a social by the location safety evaluation system by distance from him or her. analyzing the facial expressions and attitudes of each person, we set the DP uniformly for each (5) Safe route visualization system attribute as shown in Table 1 for simplicity in this This system visualizes shared information like simulation. dangerous persons detected by other edge devices. This system also visualizes the routes and social Table 1 distance calculated by the safe route search Attributes and DP of people system. Wide-area safe routes are displayed on a Attributes Color DP Number map, and narrow-area safe routes for passing each PE other are displayed on the HMD. person with an - 100 edge device (6) Data-sharing server A server that accumulates real-time spatial PF person with a 1 10 safety information. It stores information uploaded fever from each edge terminal and distributes it to each terminal in real-time. PD 10 10 dangerous person 2.2. Simulator PS police officer or -10 10 We will evaluate the usefulness of the security guard designed system. However, our system includes subsystems that are difficult to implement at this Figure 4 shows the results of mapping the time. In particular, as mentioned in section 1.3, current positions of PF, PD, and PS detected by there is no established method to evaluate the 100 edge devices on a 2D map. We drew bubbles safety of a location with sufficient accuracy on an (spheres) colored according to the attributes at the edge device. In Addition, there are also no small, detected person's location. This bubble is drawn at lightweight, safe, optical see-through HMDs that the location of the PF, PD, and PS at the time the can be easily worn. Because of these difficulties PE detected them, but they could move. Also, the in implementing our system in the real world, we map would be filled with bubbles of newly first conducted our system evaluation in a detected PFs, PDs, and PSs. Therefore, we set the simulator. transparency of the bubbles to indicate how new We constructed a virtual Akihabara city [19] the information is, and set them to gradually area of approximately 625 m x 625 m. All disappear in one minute so that the map would not subsystems operate ideally in a virtual Akihabara. be filled with bubbles. Users can see that the For example, virtual Akihabara has an ideal edge disappearing bubble is old information. device that measures the safety of the location with 100% accuracy. Then, We randomly placed autonomously roaming people with various attributes PE, PF, PD, and PS shown in Table 1 in a virtual Akihabara. PE is a person with an edge device (100 people), PF is a person with a fever (10 people), PD is a person who can be judged dangerous for some reason such as a facial expression or attitude (10 people), and PS is a person who can reduce the level of danger in that location such as a police officer or security guard (10 people). The PE has an ideal edge device that detects PF, PD, and PS within a 30-meter radius without error and measures the level of danger. Figure 5: Wide-area safe route search Figure 4: Mapping results of detected PF(Red), PD(Yellow), and PS(Green) When a PE with an edge device sets a destination, a wide-area safe route system performs the route search. We have extended the Dijkstra method, which is used for route finding in navigation systems, to perform wide-area safe route finding considering distance and level of danger in space. Dijkstra's method is an algorithm in graph theory that can find the shortest distance from a start node to a goal node and its path. We generated a graph structure from map data in which the intersections are represented as nodes I and the roads connecting the intersections as edges E. We defined the Edge Danger Level (EDL) as the level of danger between intersection In and the adjacent intersection In+1, where EDL is the sum of the DP of PF, PD, and PS that exist between the Figure 6: Narrow-area safety route search intersections. We set a danger threshold value DLth, and if EDL exceeds the DLth, the path is Figure 5 shows an example of wide-area safe considered impassable and the node connection is routing. When a PE with an edge device starts disconnected. In this simulator, we set the from point S and goes to G, Figure 5(a) is usually threshold DLth = 5. The system evaluates the EDL the shortest path. If one PF is detected at E(I1, I2), of all edges, updates the graph structure, and finds one PF and one PD at E(I2, I3), and one PF, PD, the shortest and safest path using the Dijkstra and PS at E(I3, I4), the level of danger EDL for each method. edge is as follows EDL(I1, I2)＝ 1 ：passable EDL(I2, I3)＝ 11 ：impassable EDL(I3, I4)＝ 1 ：passable Considering the threshold DLth = 5, edge E(I2, I3) is cut off because it is impassable, and the shortest path search is performed on the remaining edges, resulting in the path shown in Figure 5(b). E(I2, I3) and E(I3, I4) are passable, but there are PF and PD, so a narrow-area safe path search is performed to find a path that allows them to pass or PD on the route, the system performs a narrow- each other while maintaining the social distance. area safe route search and shows PE0 a route to Figure 6 shows an example of a narrow-area safe pass them while maintaining social distance. path search. The system generates a graph Figure 8 shows the view from PE0. In this structure by dividing the space to be traversed into experiment, PE0 is assumed to be wearing a see- meshes. The size of the mesh was set to 3.5 m, a through HMD. System display PF, PD, and PS social distance defined by Edward et al[18]. as detected and shared by other edge devices. The hard to reach but easily conversable. In the system also displays a narrow-area safe route narrow-area safe path search, the Social Distance search result. The system superimposed bubbles according to the attributes of PF, PD, and PS was with sizes corresponding to the social distance for considered for the EDL evaluation. For PF with each attribute of PF, PD, and PS, and the safe fever, the Social Distance in the COVID-19 passing route (green). environment is defined as 6 feet by the Centers for Disease Control and Prevention and 1 m by the WHO [20][21]. We defined Social Distance as 5 m, five times the WHO standard. According to Matsunaga et al., when the distance to a person is less than 6 m, the likelihood of assault increases[22]. Therefore, we set the social distance to PD (person with dangerous facial expressions/attitudes) as 10 m. We also set the social distance as 20 m assuming that the PS (police officer or security guard) has an eye range of 20 m. Based on these social distances, the system calculates EDL for all edges in the same way as in a wide-area safe route search, updates the graph structure, and finds the shortest path using the Dijkstra method. In this way, we can Figure 7: Results of wide-area safe route search avoid dangerous people by computing travel paths that can maintain safe social distances. In addition, these social distances can be changed dynamically according to the user's attributes, season, time of use, and level of danger of the person to make it safer and more convenient. For example, the social distance can be adjusted to take more time when the user is a child or a woman, or when it is late at night. 2.3. Simulation Results Figure 8: Visualization detected and shared PF, Figure 7 shows the mapping results of the PD, and PS, and Narrow-area safety route search current positions of PF, PD, and PS detected and results shared by 100 edge devices and the wide-area safe route on a 2D map. We deployed PE0, a person 2.4. Smartphone prototype system tasked with moving from the start point S to the goal point G. PE0 is equipped with an edge device Simulator experiments have shown that the and moves along the route indicated by the edge system works effectively within the virtual device's wide-area safety route search. PE0 is Akihabara. Therefore, we developed a prototype equipped with an edge device and moves along system using a smartphone as shown in Figure 9. the route indicated by the edge device's wide-area The edge device on the simulator we implemented safe path search. Although the shortest route is to was an ideal device that could detect people and go straight from S to G, the blue color indicates their attributes within a radius of 30 meters that the route search is performed in consideration without error. However, since such an ideal of the level of danger. When PE0 needs to pass PF device does not exist in reality, our prototype system simply detects a person from a 2.5. Smartphone Prototype Result smartphone's RGBD camera image (without a level of danger assessment) and shares the As shown in Figure 11 we placed PF, a person person's location coordinates. We developed a assumed to have a fever, in the park. Next, smartphone application in Unity and installed it smartphones were placed at PE1, PE2, and PE3. on an iPhone 13 pro max (Figure 10). The app The location of PE1 is in the same park as PF and obtains the position coordinates (latitude, has no obstacles, so PF can be detected directly, longitude, and altitude) of the smartphone from but from the location of PE2 and PE3, PF cannot the Global Navigation Satellite System (GNSS), be detected directly due to the obstacles (height the azimuth and orientation of the device from the difference and trees). magnetic compass and gyro, and detects the person using the RGB camera and LiDAR, and also gets the distance to the person. The distance to the person is also obtained. The application then calculated the position coordinates of the PE1 detected person and displayed a translucent PF PE3 bubble (red sphere) with a diameter of 5 meters. The location information of the detected person is shared by Unity's photon plug-in and is superimposed on the same location from another smartphone. The location coordinates of the detected person are uploaded to the data server PE2 (Nifty Cloud mobile backend), and the Figure 11: PF and edge devices position information of the detected person can be viewed from a web browser (Google Map). Figure 12 shows the screen when looking at PF from PE1, where PF is detected and a red bubble Data Server Web Server with a diameter of 5 m is superimposed. Edge Device Edge Device Web Browser Figure 9: Smartphone prototype system position azimuth Person Detection coordinates orientation RGB-Cam/LiDAR Figure 12: Detected PF from PE1 Figure 13 and 14 show the view of PF from PE2 and PE3. From PE2 and PE3, the PF cannot be seen directly due to the height difference and the trees, respectively. However, a red bubble is displayed at the location of the PF based on the information from PE1, so that the user can iphone13 recognize the presence of the PF. pro max Figure 10: Prototype Application Figure 13: View of PF from PE2 Figure 15: Floated Bubble from PE2 Figure 14: View of PFs from PE3 Figure 16: Floated Bubble from PE3 A red bubble with a diameter of 5m Figure 17 shows the results of mapping the PFs representing the PF is shown in Figures 12, 13, detected by PE1 on Google Map (aerial photo) and 14, but these bubbles are not occluded by with images. This allows users in remote locations obstacles such as the ground or trees in real space, to determine the location of PFs. making it difficult to get a sense of distance. In the simulator, the 3D geometry of the entire townscape is known, so the bubbles can be occluded, but the prototype system cannot measure the 3D geometry of the real world, so occlusion is not possible. The LiDAR in the iPhone13 can measure real-world 3D geometry at close range (about 15m). The AR Foundation tool provides an Automatic Environment Occlusion function using LiDAR, but it does not work on objects at long distances as in this experiment. Therefore, We generally assume that there are fewer obstacles in the sky, we floated the bubble Figure 17: Detected persons on Google Map at 30 m above the detected PF and displayed a thin cylinder up to the bubble. The results are shown in Figures 15 and 16. 3. Discussion, and Future Works 3.2. Smart Phone and Future Works We have developed a prototype system that As mentioned in section 3.5, the bubble can detect and avoid criminal damage and representing the location of the detected PF is not unpleasant events in advance, using a simulator occluded by objects in the real world, making it and a smartphone. Through experiments, we difficult to get a sense of the distance. We floated confirmed that the system works and that the the bubble at 30 m above the PF, but the same information about the detected persons can be problem occurs when there is an object in the sky. shared. In the future, we would like to estimate the 3D geometry of the object in front of the PF from the 3.1. Simulator and future works video and perform bubble occlusion. Another issue is the accuracy of GNSS. We experimented in a park with no obstacles in the sky, but the Once the simulator was run and the locations GNSS accuracy was at best about 5 m horizontally of PFs, PDs, and PSs were identified on the 2D and 3 m vertically, with errors of 30 m or more in map, we felt that "we should avoid that dangerous some situations. Therefore, a method to estimate area where PFs and PDs are densely located," and self-position by measuring the 3D geometry of an did not feel that much need for wide-area safe object is possible. For example, position route search. It may be sufficient to have estimation can be performed by matching point information that "that area is dangerous". In cloud data obtained by LiDAR sensors installed in addition, when we had to pass by a PF or PD, the recent smartphones with real space, and AR display of social distance in the form of a something similar has already been implemented bubble made us feel secure, but when we had to in automatic driving. For this purpose, it is pass through their bubble, we felt great stress necessary to convert the entire real space into despite it being a simulation. On the other hand, point cloud data. However, point cloud data in when there was a PS that was supposed to be a cities and buildings are available through the police officer, etc., I was able to move with ease PLATEAU project [23] promoted by Japan's even if there was a PD. However, the bubble was Ministry of Land, Infrastructure, Transport, and often invisible behind a building, and when we Tourism, and we would like to consider turned a corner, we were sometimes surprised improving the accuracy of self-position when the bubble suddenly appeared, and we felt estimation using this method in the future. that the AR display needed to be improved in this Through these research activities, we will regard. The edge device on the simulator is an promote the social implementation of ideal device to detect PF, PD, and PS within a information-sharing and navigation systems that radius of 30m without error, but in reality, such an can detect and avoid criminal damage and ideal device is difficult to implement. Therefore, unpleasant events in advance. in the future, we would first like to narrow down the detection target to people with fever and conduct experiments in the real world to 4. 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