Autonomous vehicles are expected to become a new means of transportation, potentially replacing existing public transit systems. These vehicles equip range sensors such as LiDAR to perceive their surroundings. However, several distance spoofing attacks targeting LiDAR have been identified. While detection methods for individual attacks have been proposed, evaluating the impact of combined attack—combinations of multiple attack types—is also essential for improving the safety of autonomous driving systems. This study organizes the key aspects necessary for designing combined attack scenarios and proposes an improved simulation environment capable of reproducing diverse combined attack. The feasibility of the proposed simulation environment is also demonstrated.

Traffic congestion in urban areas poses an issue in many countries. Various measures have been implemented to address these issues. For example, methods to restrict the number of vehicles that can enter, methods to provide priority to vehicles with multiple occupants, and methods to allow only public transportation to run on priority roads have been implemented. Furthermore, there are examples of methods that use Vehicle to Roadside (V2R) communication technologies to prevent public transportation such as buses from stopping at traffic lights. However, this is not effective on roads with many general vehicles. Additionally, efficient congestion reduction methods using Vehicle to Vehicle (V2V) communication technologies have been proposed, but many of them are unrealistic for general roads, such as being limited to highways where it is assumed that there are no people or bicycles. In this study, we propose a method to reduce congestion by realizing efficient right and left turns using V2R technology while considering realistic environments. Furthermore, we evaluate the method via simulations. The results show the effectiveness of the proposed method.

Since the COVID-19 pandemic, Japanese industry has been facing a labor shortage in various fields. In particular, the security sector is experiencing a serious labor shortage problem. University security operations are also being affected by the lack of security guards. On the other hand, robot technology is developing and it is becoming possible to perform not only simple tasks but also tasks that involve interaction with humans. In particular, robots are expected to be introduced in patrol security, which requires a large number of personnel, as much of the work involves confirming that there are no problems. A security guard should go there only if it cannot be confirmed that there is no problem. Therefore, the accuracy of confirmation and the cost of introducing and operation become issues. The technologies for recognizing human using cameras mounted or 3D-LiDAR (Light Detection And Raging) on robots has been established. However, how to use it in a dark place and recognition technology by the low-cost 2D-LiDAR have not been established. In this paper, we propose a human recognition method using 2D-LiDAR that extends the technologies of previous research, and present the results of our evaluation.

Recently, autonomous vehicle technologies have been widely developed. Moreover, these technologies are applied to autonomous carts for security guards, guidance robots, delivery carts, etc. A variety of human detection technologies are important for these robotic carts. There are many studies on avoiding collisions with humans and other obstacles. However, technologies to distinguish between humans and other obstacles are especially important for security guard robots. In this paper, we propose a standing human detection method using 2D-LiDARs to reduce costs. We show the details of our methods and results of evaluation.

In many countries (including Japan), birthrates are declining and populations are aging. The declining 18-year-old population is a problem for university education. High-quality university education is essential for the development of a country's economic and technological strengths. Many studies have been conducted on the learning of active students. However, the number of students with various problems is expected to increase in the future. Nevertheless, the numbers of faculty and staff might remain the same. Therefore, supporting programs will be required for certain individual students. As such, universities should analyze activity-tracking data to identify the students requiring additional support. In this study, we introduce agents for monitoring each student's activities such as attending classes, eating, going to the library, and visiting a club. The agents monitor the students’ smartphones connected to campus WiFi for accessing the internet or other contents for campus students without using background smartphone applications. Using experiments, we confirm that we can always locate the students.

In recent years, different robotic carts have been developed, and are suitable for use as transport, guide, and patrol robots on campuses or in buildings, or for event venues such as trade shows, with digital signage displays. These small robots move autonomously using a location information system, obstacle detection system, and maps. We focus on a patrol system for finding suspicious but harmless people such as aged wanderers. These suspicious people are found about once a year or a few times during events. Therefore, autonomous robots are useful for deterrence and to find the suspicious people. We assume that the patrol robots have global navigation satellite systems (GNSS) for their location information system, laser imaging detection and ranging (LiDAR) for obstacle detection, an infrared sensor for human detection, and a Bluetooth device to detect authorized humans. Moreover, we assume that event venues are outdoors and that there are many temporary booths. Therefore, simultaneous localization and mapping (SLAM) technologies are required. In this paper, we describe a localization method in an event space for SLAM. Moreover, we show the evaluation result of our proposed method.

Traffic jams have recently become a significant problem in provincial cities in Japan, which tend to have poor railway services. Therefore, the main means of transportation are public buses, taxies, and private vehicles. Moreover, traffic accidents and road construction sites frequently block traffic. It is therefore difficult to estimate the travelling time from origin to destination in real-time. To estimate the travelling time, we must predict the behaviors of many vehicles that depend on an "origin to destination" (OD) traffic volume. In our previous study, we proposed an estimation method for OD traffic volume using two types of big-data, a road traffic census and mobile spatial statistics. In this study, we evaluated our proposed method on various situations through a traffic simulation.

Congestion reduction is one of the expected positive effects of the implementation of autonomous vehicles; however, in mixed environments containing autonomous and non-autonomous vehicles, the anticipated positive effects may not be fully attained. In this study, we simulate various mixed environments containing autonomous and non-autonomous vehicles and assess the traffic congestion issues encountered at a T-junction intersection. Then, a method to resolve these issues is proposed involving the adjustment of inter-vehicle distance. Finally, the proposed method is evaluated and shows positive results.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Recently, smartphones have become widely used as vehicle information devices. As a result, several advantages have appeared. First, a smartphone can log driving data such as location, vehicle status information, and so on. The logs do not depend on the car, but on the smartphone user. Second, a smartphone can gather driving-related information via the Internet. We believe that the advantages enable us to recognize several contexts. Therefore, we proposed a system that supports the user in changing provided services based on the context. We have discussed context recognition in order to create the system. One of the discussion outcomes is that the location context holds a very important role in guessing which function is required by a driver. However, it is difficult to recognize the location context using only latitude-longitude logs. In this paper, we propose a method for recognizing location context. The method is separated into three steps. First, values are recorded in a travel history such as a meshed map. The values represent driving experience points in a travelling area. Second, the method searches the travel history for a given driving area. Then, the value of the searched area is compared with a specified threshold. As a result, the location context is recognized. We implemented a prototype system and evaluated the proposed method. The results of the evaluation confirm the effectiveness of both the proposed method and expected future work.

Smartphones are replacing conventional on-vehicle information devices, and many driver-related smartphone applications are available in the market. On-vehicle information devices can recognize driver and environmental contexts in addition to the vehicle context. We discuss how to use these contexts to advance the operability of on-vehicle information devices and the user's experience. Then, we implement a prototype system and evaluate it. In this paper, we first classify contexts that occur while driving. Second, we propose a user-interface control system based on these contexts. Finally, we evaluate the operation time for the proposed method. The results of the evaluation confirm the effectiveness of the proposal.

It is predicted that many more consumers will use free telematics smartphone applications, given their increased availability. These telematics services require that servers and consumer devices, such as smartphones and car navigation terminals, transmit large amounts of small data through uplink. Developing methods to decrease the data size that is transmitted is one of the most significant issues in this field because of the cost of transmission for consumers and service providers. In this paper, we propose a new data compression method for application to telematics technology, which deletes redundant data and applies an appropriate compression method for a given situation. We evaluated the proposed method and report positive results.

The current technological trend in automotive navigation systems is toward “Display Audio” systems connected to a smartphone. However, there are some issues stemming from the differing life cycles of on-vehicle information devices and smartphones. Therefore, we expect that Display Audio will result in simple I/O devices connected via smartphones and a car-based network (e.g., Controller Area Network). However, there are numerous situations when it is necessary for a driver to easily and safely operate on-vehicle information devices. In this paper, we first discuss various types of on-vehicle information devices, and their intended use in driving situations. Second, we discuss the relation of those situations to various on-vehicle information applications. Third, we propose a simple user interface (UI) for connecting on-vehicle information devices. To conclude, we discuss the UI for on-vehicle information devices, based on context-aware technologies.

Recently, the size of the software on embedded devices, e.g., mobile phones, has been increasing rapidly. Complex processes in large scale software, such as event handlers, require bugs to be fixed after shipment. NAND flash memory devices are adopted in such devices in order to reduce the cost. The program code in a NAND flash memory is loaded to RAM using demand paging technologies. In many cases, compressed program code is stored in the NAND flash memory and loaded by extracting the program code because the loading time for the uncompressed code is larger than the total time for loading and extracting the compressed code with some compression algorithms. Byte-Pair-Encoding (BPE) is a suitable algorithm for this purpose. The compression rate for BPE is slightly less than GZIP and the extraction speed is very fast. However, the compression speed is very slow. Software updating functions require the compression of program code on a device and have to compress quickly. This paper discusses the software update functions employed for such embedded devices, and proposes a BPE compression speed acceleration technique for software updating. The results obtained from an evaluation show that the proposed method is effective in compressing program code.